WorldWideScience

Sample records for efficient subspace-based fundamental

  1. Scalable Density-Based Subspace Clustering

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

    2011-01-01

    For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering...... method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real...... and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well....

  2. Unsupervised spike sorting based on discriminative subspace learning.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2014-01-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

  3. A Comfort-Aware Energy Efficient HVAC System Based on the Subspace Identification Method

    Directory of Open Access Journals (Sweden)

    O. Tsakiridis

    2016-01-01

    Full Text Available A proactive heating method is presented aiming at reducing the energy consumption in a HVAC system while maintaining the thermal comfort of the occupants. The proposed technique fuses time predictions for the zones’ temperatures, based on a deterministic subspace identification method, and zones’ occupancy predictions, based on a mobility model, in a decision scheme that is capable of regulating the balance between the total energy consumed and the total discomfort cost. Simulation results for various occupation-mobility models demonstrate the efficiency of the proposed technique.

  4. Roller Bearing Monitoring by New Subspace-Based Damage Indicator

    Directory of Open Access Journals (Sweden)

    G. Gautier

    2015-01-01

    Full Text Available A frequency-band subspace-based damage identification method for fault diagnosis in roller bearings is presented. Subspace-based damage indicators are obtained by filtering the vibration data in the frequency range where damage is likely to occur, that is, around the bearing characteristic frequencies. The proposed method is validated by considering simulated data of a damaged bearing. Also, an experimental case is considered which focuses on collecting the vibration data issued from a run-to-failure test. It is shown that the proposed method can detect bearing defects and, as such, it appears to be an efficient tool for diagnosis purpose.

  5. Subspace Based Blind Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki

    2012-01-01

    The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...

  6. Robust subspace estimation using low-rank optimization theory and applications

    CERN Document Server

    Oreifej, Omar

    2014-01-01

    Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book,?the authors?discuss fundame

  7. Kernel based subspace projection of hyperspectral images

    DEFF Research Database (Denmark)

    Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten

    In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF...

  8. Subspace-Based Holistic Registration for Low-Resolution Facial Images

    Directory of Open Access Journals (Sweden)

    Boom BJ

    2010-01-01

    Full Text Available Subspace-based holistic registration is introduced as an alternative to landmark-based face registration, which has a poor performance on low-resolution images, as obtained in camera surveillance applications. The proposed registration method finds the alignment by maximizing the similarity score between a probe and a gallery image. We use a novel probabilistic framework for both user-independent as well as user-specific face registration. The similarity is calculated using the probability that the face image is correctly aligned in a face subspace, but additionally we take the probability into account that the face is misaligned based on the residual error in the dimensions perpendicular to the face subspace. We perform extensive experiments on the FRGCv2 database to evaluate the impact that the face registration methods have on face recognition. Subspace-based holistic registration on low-resolution images can improve face recognition in comparison with landmark-based registration on high-resolution images. The performance of the tested face recognition methods after subspace-based holistic registration on a low-resolution version of the FRGC database is similar to that after manual registration.

  9. Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment

    Energy Technology Data Exchange (ETDEWEB)

    Khuwaileh, B.A., E-mail: bakhuwai@ncsu.edu; Abdel-Khalik, H.S.

    2015-01-15

    Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.

  10. Seismic noise attenuation using an online subspace tracking algorithm

    NARCIS (Netherlands)

    Zhou, Yatong; Li, Shuhua; Zhang, D.; Chen, Yangkang

    2018-01-01

    We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient

  11. Extending the subspace hybrid method for eigenvalue problems in reactor physics calculation

    International Nuclear Information System (INIS)

    Zhang, Q.; Abdel-Khalik, H. S.

    2013-01-01

    This paper presents an innovative hybrid Monte-Carlo-Deterministic method denoted by the SUBSPACE method designed for improving the efficiency of hybrid methods for reactor analysis applications. The SUBSPACE method achieves its high computational efficiency by taking advantage of the existing correlations between desired responses. Recently, significant gains in computational efficiency have been demonstrated using this method for source driven problems. Within this work the mathematical theory behind the SUBSPACE method is introduced and extended to address core wide level k-eigenvalue problems. The method's efficiency is demonstrated based on a three-dimensional quarter-core problem, where responses are sought on the pin cell level. The SUBSPACE method is compared to the FW-CADIS method and is found to be more efficient for the utilized test problem because of the reason that the FW-CADIS method solves a forward eigenvalue problem and an adjoint fixed-source problem while the SUBSPACE method only solves an adjoint fixed-source problem. Based on the favorable results obtained here, we are confident that the applicability of Monte Carlo for large scale reactor analysis could be realized in the near future. (authors)

  12. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  13. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  14. Seismic noise attenuation using an online subspace tracking algorithm

    Science.gov (United States)

    Zhou, Yatong; Li, Shuhua; Zhang, Dong; Chen, Yangkang

    2018-02-01

    We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.

  15. An efficient preconditioning technique using Krylov subspace methods for 3D characteristics solvers

    International Nuclear Information System (INIS)

    Dahmani, M.; Le Tellier, R.; Roy, R.; Hebert, A.

    2005-01-01

    The Generalized Minimal RESidual (GMRES) method, using a Krylov subspace projection, is adapted and implemented to accelerate a 3D iterative transport solver based on the characteristics method. Another acceleration technique called the self-collision rebalancing technique (SCR) can also be used to accelerate the solution or as a left preconditioner for GMRES. The GMRES method is usually used to solve a linear algebraic system (Ax=b). It uses K(r (o) ,A) as projection subspace and AK(r (o) ,A) for the orthogonalization of the residual. This paper compares the performance of these two combined methods on various problems. To implement the GMRES iterative method, the characteristics equations are derived in linear algebra formalism by using the equivalence between the method of characteristics and the method of collision probability to end up with a linear algebraic system involving fluxes and currents. Numerical results show good performance of the GMRES technique especially for the cases presenting large material heterogeneity with a scattering ratio close to 1. Similarly, the SCR preconditioning slightly increases the GMRES efficiency

  16. Sinusoidal Order Estimation Using Angles between Subspaces

    Directory of Open Access Journals (Sweden)

    Søren Holdt Jensen

    2009-01-01

    Full Text Available We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods.

  17. The influence of different PAST-based subspace trackers on DaPT parameter estimation

    Science.gov (United States)

    Lechtenberg, M.; Götze, J.

    2012-09-01

    In the context of parameter estimation, subspace-based methods like ESPRIT have become common. They require a subspace separation e.g. based on eigenvalue/-vector decomposition. In time-varying environments, this can be done by subspace trackers. One class of these is based on the PAST algorithm. Our non-linear parameter estimation algorithm DaPT builds on-top of the ESPRIT algorithm. Evaluation of the different variants of the PAST algorithm shows which variant of the PAST algorithm is worthwhile in the context of frequency estimation.

  18. Time stepping free numerical solution of linear differential equations: Krylov subspace versus waveform relaxation

    NARCIS (Netherlands)

    Bochev, Mikhail A.; Oseledets, I.V.; Tyrtyshnikov, E.E.

    2013-01-01

    The aim of this paper is two-fold. First, we propose an efficient implementation of the continuous time waveform relaxation method based on block Krylov subspaces. Second, we compare this new implementation against Krylov subspace methods combined with the shift and invert technique.

  19. OpenSubspace

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

    2009-01-01

    Subspace clustering and projected clustering are recent research areas for clustering in high dimensional spaces. As the field is rather young, there is a lack of comparative studies on the advantages and disadvantages of the different algorithms. Part of the underlying problem is the lack...... of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this paper, we discuss the requirements for open source evaluation software. We propose OpenSubspace, an open source framework that meets...... these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in subspace and projected clustering....

  20. Invariant subspaces

    CERN Document Server

    Radjavi, Heydar

    2003-01-01

    This broad survey spans a wealth of studies on invariant subspaces, focusing on operators on separable Hilbert space. Largely self-contained, it requires only a working knowledge of measure theory, complex analysis, and elementary functional analysis. Subjects include normal operators, analytic functions of operators, shift operators, examples of invariant subspace lattices, compact operators, and the existence of invariant and hyperinvariant subspaces. Additional chapters cover certain results on von Neumann algebras, transitive operator algebras, algebras associated with invariant subspaces,

  1. Greedy subspace clustering.

    Science.gov (United States)

    2016-09-01

    We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets ...

  2. Relevant Subspace Clustering

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

    2009-01-01

    Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....

  3. Subspace-based interference removal methods for a multichannel biomagnetic sensor array

    Science.gov (United States)

    Sekihara, Kensuke; Nagarajan, Srikantan S.

    2017-10-01

    Objective. In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. Approach. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.

  4. The Detection of Subsynchronous Oscillation in HVDC Based on the Stochastic Subspace Identification Method

    Directory of Open Access Journals (Sweden)

    Chen Shi

    2014-01-01

    Full Text Available Subsynchronous oscillation (SSO usually caused by series compensation, power system stabilizer (PSS, high voltage direct current transmission (HVDC and other power electronic equipment, which will affect the safe operation of generator shafting even the system. It is very important to identify the modal parameters of SSO to take effective control strategies as well. Since the identification accuracy of traditional methods are not high enough, the stochastic subspace identification (SSI method is proposed to improve the identification accuracy of subsynchronous oscillation modal. The stochastic subspace identification method was compared with the other two methods on subsynchronous oscillation IEEE benchmark model and Xiang-Shang HVDC system model, the simulation results show that the stochastic subspace identification method has the advantages of high identification precision, high operation efficiency and strong ability of anti-noise.

  5. EVD Dualdating Based Online Subspace Learning

    Directory of Open Access Journals (Sweden)

    Bo Jin

    2014-01-01

    Full Text Available Conventional incremental PCA methods usually only discuss the situation of adding samples. In this paper, we consider two different cases: deleting samples and simultaneously adding and deleting samples. To avoid the NP-hard problem of downdating SVD without right singular vectors and specific position information, we choose to use EVD instead of SVD, which is used by most IPCA methods. First, we propose an EVD updating and downdating algorithm, called EVD dualdating, which permits simultaneous arbitrary adding and deleting operation, via transforming the EVD of the covariance matrix into a SVD updating problem plus an EVD of a small autocorrelation matrix. A comprehensive analysis is delivered to express the essence, expansibility, and computation complexity of EVD dualdating. A mathematical theorem proves that if the whole data matrix satisfies the low-rank-plus-shift structure, EVD dualdating is an optimal rank-k estimator under the sequential environment. A selection method based on eigenvalues is presented to determine the optimal rank k of the subspace. Then, we propose three incremental/decremental PCA methods: EVDD-IPCA, EVDD-DPCA, and EVDD-IDPCA, which are adaptive to the varying mean. Finally, plenty of comparative experiments demonstrate that EVDD-based methods outperform conventional incremental/decremental PCA methods in both efficiency and accuracy.

  6. Numerical solution of stiff burnup equation with short half lived nuclides by the Krylov subspace method

    International Nuclear Information System (INIS)

    Yamamoto, Akio; Tatsumi, Masahiro; Sugimura, Naoki

    2007-01-01

    The Krylov subspace method is applied to solve nuclide burnup equations used for lattice physics calculations. The Krylov method is an efficient approach for solving ordinary differential equations with stiff nature such as the nuclide burnup with short lived nuclides. Some mathematical fundamentals of the Krylov subspace method and its application to burnup equations are discussed. Verification calculations are carried out in a PWR pin-cell geometry with UO 2 fuel. A detailed burnup chain that includes 193 fission products and 28 heavy nuclides is used in the verification calculations. Shortest half life found in the present burnup chain is approximately 30 s ( 106 Rh). Therefore, conventional methods (e.g., the Taylor series expansion with scaling and squaring) tend to require longer computation time due to numerical stiffness. Comparison with other numerical methods (e.g., the 4-th order Runge-Kutta-Gill) reveals that the Krylov subspace method can provide accurate solution for a detailed burnup chain used in the present study with short computation time. (author)

  7. Geometric mean for subspace selection.

    Science.gov (United States)

    Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

    2009-02-01

    Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.

  8. Integrated Phoneme Subspace Method for Speech Feature Extraction

    Directory of Open Access Journals (Sweden)

    Park Hyunsin

    2009-01-01

    Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

  9. Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

    Science.gov (United States)

    Xu, Y; Li, N

    2014-09-01

    Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.

  10. Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems

    International Nuclear Information System (INIS)

    Xu, Y; Li, N

    2014-01-01

    Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator–prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework. (paper)

  11. Estimation of direction of arrival of a moving target using subspace based approaches

    Science.gov (United States)

    Ghosh, Ripul; Das, Utpal; Akula, Aparna; Kumar, Satish; Sardana, H. K.

    2016-05-01

    In this work, array processing techniques based on subspace decomposition of signal have been evaluated for estimation of direction of arrival of moving targets using acoustic signatures. Three subspace based approaches - Incoherent Wideband Multiple Signal Classification (IWM), Least Square-Estimation of Signal Parameters via Rotation Invariance Techniques (LS-ESPRIT) and Total Least Square- ESPIRIT (TLS-ESPRIT) are considered. Their performance is compared with conventional time delay estimation (TDE) approaches such as Generalized Cross Correlation (GCC) and Average Square Difference Function (ASDF). Performance evaluation has been conducted on experimentally generated data consisting of acoustic signatures of four different types of civilian vehicles moving in defined geometrical trajectories. Mean absolute error and standard deviation of the DOA estimates w.r.t. ground truth are used as performance evaluation metrics. Lower statistical values of mean error confirm the superiority of subspace based approaches over TDE based techniques. Amongst the compared methods, LS-ESPRIT indicated better performance.

  12. Consistency Analysis of Nearest Subspace Classifier

    OpenAIRE

    Wang, Yi

    2015-01-01

    The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also ...

  13. External Evaluation Measures for Subspace Clustering

    DEFF Research Database (Denmark)

    Günnemann, Stephan; Färber, Ines; Müller, Emmanuel

    2011-01-01

    research area of subspace clustering. We formalize general quality criteria for subspace clustering measures not yet addressed in the literature. We compare the existing external evaluation methods based on these criteria and pinpoint limitations. We propose a novel external evaluation measure which meets...

  14. Subspace learning from image gradient orientations

    NARCIS (Netherlands)

    Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

    2012-01-01

    We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the

  15. 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....

  16. Subspace System Identification of the Kalman Filter

    Directory of Open Access Journals (Sweden)

    David Di Ruscio

    2003-07-01

    Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.

  17. Lyapunov vectors and assimilation in the unstable subspace: theory and applications

    International Nuclear Information System (INIS)

    Palatella, Luigi; Carrassi, Alberto; Trevisan, Anna

    2013-01-01

    Based on a limited number of noisy observations, estimation algorithms provide a complete description of the state of a system at current time. Estimation algorithms that go under the name of assimilation in the unstable subspace (AUS) exploit the nonlinear stability properties of the forecasting model in their formulation. Errors that grow due to sensitivity to initial conditions are efficiently removed by confining the analysis solution in the unstable and neutral subspace of the system, the subspace spanned by Lyapunov vectors with positive and zero exponents, while the observational noise does not disturb the system along the stable directions. The formulation of the AUS approach in the context of four-dimensional variational assimilation (4DVar-AUS) and the extended Kalman filter (EKF-AUS) and its application to chaotic models is reviewed. In both instances, the AUS algorithms are at least as efficient but simpler to implement and computationally less demanding than their original counterparts. As predicted by the theory when error dynamics is linear, the optimal subspace dimension for 4DVar-AUS is given by the number of positive and null Lyapunov exponents, while the EKF-AUS algorithm, using the same unstable and neutral subspace, recovers the solution of the full EKF algorithm, but dealing with error covariance matrices of a much smaller dimension and significantly reducing the computational burden. Examples of the application to a simplified model of the atmospheric circulation and to the optimal velocity model for traffic dynamics are given. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (paper)

  18. Enhancing Low-Rank Subspace Clustering by Manifold Regularization.

    Science.gov (United States)

    Liu, Junmin; Chen, Yijun; Zhang, JiangShe; Xu, Zongben

    2014-07-25

    Recently, low-rank representation (LRR) method has achieved great success in subspace clustering (SC), which aims to cluster the data points that lie in a union of low-dimensional subspace. Given a set of data points, LRR seeks the lowest rank representation among the many possible linear combinations of the bases in a given dictionary or in terms of the data itself. However, LRR only considers the global Euclidean structure, while the local manifold structure, which is often important for many real applications, is ignored. In this paper, to exploit the local manifold structure of the data, a manifold regularization characterized by a Laplacian graph has been incorporated into LRR, leading to our proposed Laplacian regularized LRR (LapLRR). An efficient optimization procedure, which is based on alternating direction method of multipliers (ADMM), is developed for LapLRR. Experimental results on synthetic and real data sets are presented to demonstrate that the performance of LRR has been enhanced by using the manifold regularization.

  19. Fundamental Frequency Estimation using Polynomial Rooting of a Subspace-Based Method

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Jensen, Søren Holdt

    2010-01-01

    improvements compared to HMUSIC. First, by using the proposed method we can obtain an estimate of the fundamental frequency without doing a grid search like in HMUSIC. This is due to that the fundamental frequency is estimated as the argument of the root lying closest to the unit circle. Second, we obtain...... a higher spectral resolution compared to HMUSIC which is a property of polynomial rooting methods. Our simulation results show that the proposed method is applicable to real-life signals, and that we in most cases obtain a higher spectral resolution than HMUSIC....

  20. Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning

    DEFF Research Database (Denmark)

    Chakraborty, Rudrasis; Hauberg, Søren; Vemuri, Baba C.

    2017-01-01

    Principal Component Analysis (PCA) is a fundamental method for estimating a linear subspace approximation to high-dimensional data. Many algorithms exist in literature to achieve a statistically robust version of PCA called RPCA. In this paper, we present a geometric framework for computing the p...

  1. Krylov subspace methods for the solution of large systems of ODE's

    DEFF Research Database (Denmark)

    Thomsen, Per Grove; Bjurstrøm, Nils Henrik

    1998-01-01

    In Air Pollution Modelling large systems of ODE's arise. Solving such systems may be done efficientliy by Semi Implicit Runge-Kutta methods. The internal stages may be solved using Krylov subspace methods. The efficiency of this approach is investigated and verified.......In Air Pollution Modelling large systems of ODE's arise. Solving such systems may be done efficientliy by Semi Implicit Runge-Kutta methods. The internal stages may be solved using Krylov subspace methods. The efficiency of this approach is investigated and verified....

  2. Subspace based adaptive denoising of surface EMG from neurological injury patients

    Science.gov (United States)

    Liu, Jie; Ying, Dongwen; Zev Rymer, William; Zhou, Ping

    2014-10-01

    Objective: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. Approach: The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. Main results: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. Significance: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.

  3. Two-Level Chebyshev Filter Based Complementary Subspace Method: Pushing the Envelope of Large-Scale Electronic Structure Calculations.

    Science.gov (United States)

    Banerjee, Amartya S; Lin, Lin; Suryanarayana, Phanish; Yang, Chao; Pask, John E

    2018-06-12

    We describe a novel iterative strategy for Kohn-Sham density functional theory calculations aimed at large systems (>1,000 electrons), applicable to metals and insulators alike. In lieu of explicit diagonalization of the Kohn-Sham Hamiltonian on every self-consistent field (SCF) iteration, we employ a two-level Chebyshev polynomial filter based complementary subspace strategy to (1) compute a set of vectors that span the occupied subspace of the Hamiltonian; (2) reduce subspace diagonalization to just partially occupied states; and (3) obtain those states in an efficient, scalable manner via an inner Chebyshev filter iteration. By reducing the necessary computation to just partially occupied states and obtaining these through an inner Chebyshev iteration, our approach reduces the cost of large metallic calculations significantly, while eliminating subspace diagonalization for insulating systems altogether. We describe the implementation of the method within the framework of the discontinuous Galerkin (DG) electronic structure method and show that this results in a computational scheme that can effectively tackle bulk and nano systems containing tens of thousands of electrons, with chemical accuracy, within a few minutes or less of wall clock time per SCF iteration on large-scale computing platforms. We anticipate that our method will be instrumental in pushing the envelope of large-scale ab initio molecular dynamics. As a demonstration of this, we simulate a bulk silicon system containing 8,000 atoms at finite temperature, and obtain an average SCF step wall time of 51 s on 34,560 processors; thus allowing us to carry out 1.0 ps of ab initio molecular dynamics in approximately 28 h (of wall time).

  4. Two-qubit quantum computing in a projected subspace

    International Nuclear Information System (INIS)

    Bi Qiao; Ruda, H.E.; Zhan, M.S.

    2002-01-01

    A formulation for performing quantum computing in a projected subspace is presented, based on the subdynamical kinetic equation (SKE) for an open quantum system. The eigenvectors of the kinetic equation are shown to remain invariant before and after interaction with the environment. However, the eigenvalues in the projected subspace exhibit a type of phase shift to the evolutionary states. This phase shift does not destroy the decoherence-free (DF) property of the subspace because the associated fidelity is 1. This permits a universal formalism to be presented--the eigenprojectors of the free part of the Hamiltonian for the system and bath may be used to construct a DF projected subspace based on the SKE. To eliminate possible phase or unitary errors induced by the change in the eigenvalues, a cancellation technique is proposed, using the adjustment of the coupling time, and applied to a two-qubit computing system. A general criteria for constructing a DF-projected subspace from the SKE is discussed. Finally, a proposal for using triangulation to realize a decoherence-free subsystem based on SKE is presented. The concrete formulation for a two-qubit model is given exactly. Our approach is general and appears to be applicable to any type of decoherence

  5. Subspace K-means clustering.

    Science.gov (United States)

    Timmerman, Marieke E; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

    2013-12-01

    To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).

  6. Active Subspaces for Wind Plant Surrogate Modeling

    Energy Technology Data Exchange (ETDEWEB)

    King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Adcock, Christiane [Massachusetts Institute of Technology

    2018-01-12

    Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.

  7. On the dimension of subspaces with bounded Schmidt rank

    International Nuclear Information System (INIS)

    Cubitt, Toby; Montanaro, Ashley; Winter, Andreas

    2008-01-01

    We consider the question of how large a subspace of a given bipartite quantum system can be when the subspace contains only highly entangled states. This is motivated in part by results of Hayden et al. [e-print arXiv:quant-ph/0407049; Commun. Math. Phys., 265, 95 (2006)], which show that in large dxd-dimensional systems there exist random subspaces of dimension almost d 2 , all of whose states have entropy of entanglement at least log d-O(1). It is also a generalization of results on the dimension of completely entangled subspaces, which have connections with the construction of unextendible product bases. Here we take as entanglement measure the Schmidt rank, and determine, for every pair of local dimensions d A and d B , and every r, the largest dimension of a subspace consisting only of entangled states of Schmidt rank r or larger. This exact answer is a significant improvement on the best bounds that can be obtained using the random subspace techniques in Hayden et al. We also determine the converse: the largest dimension of a subspace with an upper bound on the Schmidt rank. Finally, we discuss the question of subspaces containing only states with Schmidt equal to r

  8. Closed and Open Loop Subspace System Identification of the Kalman Filter

    Directory of Open Access Journals (Sweden)

    David Di Ruscio

    2009-04-01

    Full Text Available Some methods for consistent closed loop subspace system identification presented in the literature are analyzed and compared to a recently published subspace algorithm for both open as well as for closed loop data, the DSR_e algorithm. Some new variants of this algorithm are presented and discussed. Simulation experiments are included in order to illustrate if the algorithms are variance efficient or not.

  9. N-screen aware multicriteria hybrid recommender system using weight based subspace clustering.

    Science.gov (United States)

    Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang

    2014-01-01

    This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements.

  10. Shape analysis with subspace symmetries

    KAUST Repository

    Berner, Alexander

    2011-04-01

    We address the problem of partial symmetry detection, i.e., the identification of building blocks a complex shape is composed of. Previous techniques identify parts that relate to each other by simple rigid mappings, similarity transforms, or, more recently, intrinsic isometries. Our approach generalizes the notion of partial symmetries to more general deformations. We introduce subspace symmetries whereby we characterize similarity by requiring the set of symmetric parts to form a low dimensional shape space. We present an algorithm to discover subspace symmetries based on detecting linearly correlated correspondences among graphs of invariant features. We evaluate our technique on various data sets. We show that for models with pronounced surface features, subspace symmetries can be found fully automatically. For complicated cases, a small amount of user input is used to resolve ambiguities. Our technique computes dense correspondences that can subsequently be used in various applications, such as model repair and denoising. © 2010 The Author(s).

  11. Index Formulae for Subspaces of Kreĭn Spaces

    NARCIS (Netherlands)

    Dijksma, Aad; Gheondea, Aurelian

    1996-01-01

    For a subspace S of a Kreĭn space K and an arbitrary fundamental decomposition K = K-[+]K+ of K, we prove the index formula κ-(S) + dim(S⊥ ∩ K+) = κ+(S⊥) + dim(S ∩ K-), where κ±(S) stands for the positive/negative signature of S. The difference dim(S ∩ K-) - dim(S⊥ ∩ K+), provided it is well

  12. Hankel Matrix Correlation Function-Based Subspace Identification Method for UAV Servo System

    Directory of Open Access Journals (Sweden)

    Minghong She

    2018-01-01

    Full Text Available For the identification problem of closed-loop subspace model, we propose a zero space projection method based on the estimation of correlation function to fill the block Hankel matrix of identification model by combining the linear algebra with geometry. By using the same projection of related data in time offset set and LQ decomposition, the multiplication operation of projection is achieved and dynamics estimation of the unknown equipment system model is obtained. Consequently, we have solved the problem of biased estimation caused when the open-loop subspace identification algorithm is applied to the closed-loop identification. A simulation example is given to show the effectiveness of the proposed approach. In final, the practicability of the identification algorithm is verified by hardware test of UAV servo system in real environment.

  13. Improved Stochastic Subspace System Identification for Structural Health Monitoring

    Science.gov (United States)

    Chang, Chia-Ming; Loh, Chin-Hsiung

    2015-07-01

    Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.

  14. A Rank-Constrained Matrix Representation for Hypergraph-Based Subspace Clustering

    Directory of Open Access Journals (Sweden)

    Yubao Sun

    2015-01-01

    Full Text Available This paper presents a novel, rank-constrained matrix representation combined with hypergraph spectral analysis to enable the recovery of the original subspace structures of corrupted data. Real-world data are frequently corrupted with both sparse error and noise. Our matrix decomposition model separates the low-rank, sparse error, and noise components from the data in order to enhance robustness to the corruption. In order to obtain the desired rank representation of the data within a dictionary, our model directly utilizes rank constraints by restricting the upper bound of the rank range. An alternative projection algorithm is proposed to estimate the low-rank representation and separate the sparse error from the data matrix. To further capture the complex relationship between data distributed in multiple subspaces, we use hypergraph to represent the data by encapsulating multiple related samples into one hyperedge. The final clustering result is obtained by spectral decomposition of the hypergraph Laplacian matrix. Validation experiments on the Extended Yale Face Database B, AR, and Hopkins 155 datasets show that the proposed method is a promising tool for subspace clustering.

  15. Structured Kernel Subspace Learning for Autonomous Robot Navigation.

    Science.gov (United States)

    Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

    2018-02-14

    This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

  16. Subspace-based optimization method for inverse scattering problems with an inhomogeneous background medium

    International Nuclear Information System (INIS)

    Chen, Xudong

    2010-01-01

    This paper proposes a version of the subspace-based optimization method to solve the inverse scattering problem with an inhomogeneous background medium where the known inhomogeneities are bounded in a finite domain. Although the background Green's function at each discrete point in the computational domain is not directly available in an inhomogeneous background scenario, the paper uses the finite element method to simultaneously obtain the Green's function at all discrete points. The essence of the subspace-based optimization method is that part of the contrast source is determined from the spectrum analysis without using any optimization, whereas the orthogonally complementary part is determined by solving a lower dimension optimization problem. This feature significantly speeds up the convergence of the algorithm and at the same time makes it robust against noise. Numerical simulations illustrate the efficacy of the proposed algorithm. The algorithm presented in this paper finds wide applications in nondestructive evaluation, such as through-wall imaging

  17. Subspace-based analysis of the ERT inverse problem

    Science.gov (United States)

    Ben Hadj Miled, Mohamed Khames; Miller, Eric L.

    2004-05-01

    In a previous work, we proposed a source-type formulation to the electrical resistance tomography (ERT) problem. Specifically, we showed that inhomogeneities in the medium can be viewed as secondary sources embedded in the homogeneous background medium and located at positions associated with variation in electrical conductivity. Assuming a piecewise constant conductivity distribution, the support of equivalent sources is equal to the boundary of the inhomogeneity. The estimation of the anomaly shape takes the form of an inverse source-type problem. In this paper, we explore the use of subspace methods to localize the secondary equivalent sources associated with discontinuities in the conductivity distribution. Our first alternative is the multiple signal classification (MUSIC) algorithm which is commonly used in the localization of multiple sources. The idea is to project a finite collection of plausible pole (or dipole) sources onto an estimated signal subspace and select those with largest correlations. In ERT, secondary sources are excited simultaneously but in different ways, i.e. with distinct amplitude patterns, depending on the locations and amplitudes of primary sources. If the number of receivers is "large enough", different source configurations can lead to a set of observation vectors that span the data subspace. However, since sources that are spatially close to each other have highly correlated signatures, seperation of such signals becomes very difficult in the presence of noise. To overcome this problem we consider iterative MUSIC algorithms like R-MUSIC and RAP-MUSIC. These recursive algorithms pose a computational burden as they require multiple large combinatorial searches. Results obtained with these algorithms using simulated data of different conductivity patterns are presented.

  18. An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning

    Directory of Open Access Journals (Sweden)

    Xiangchun Yu

    2018-01-01

    Full Text Available We investigate a novel way of robust face image feature extraction by adopting the methods based on Unsupervised Linear Subspace Learning to extract a small number of good features. Firstly, the face image is divided into blocks with the specified size, and then we propose and extract pooled Histogram of Oriented Gradient (pHOG over each block. Secondly, an improved Earth Mover’s Distance (EMD metric is adopted to measure the dissimilarity between blocks of one face image and the corresponding blocks from the rest of face images. Thirdly, considering the limitations of the original Locality Preserving Projections (LPP, we proposed the Block Structure LPP (BSLPP, which effectively preserves the structural information of face images. Finally, an adjacency graph is constructed and a small number of good features of a face image are obtained by methods based on Unsupervised Linear Subspace Learning. A series of experiments have been conducted on several well-known face databases to evaluate the effectiveness of the proposed algorithm. In addition, we construct the noise, geometric distortion, slight translation, slight rotation AR, and Extended Yale B face databases, and we verify the robustness of the proposed algorithm when faced with a certain degree of these disturbances.

  19. Adiabatic evolution of decoherence-free subspaces and its shortcuts

    Science.gov (United States)

    Wu, S. L.; Huang, X. L.; Li, H.; Yi, X. X.

    2017-10-01

    The adiabatic theorem and shortcuts to adiabaticity for time-dependent open quantum systems are explored in this paper. Starting from the definition of dynamical stable decoherence-free subspace, we show that, under a compact adiabatic condition, the quantum state remains in the time-dependent decoherence-free subspace with an extremely high purity, even though the dynamics of the open quantum system may not be adiabatic. The adiabatic condition mentioned here in the adiabatic theorem for open systems is very similar to that for closed quantum systems, except that the operators required to change slowly are the Lindblad operators. We also show that the adiabatic evolution of decoherence-free subspaces depends on the existence of instantaneous decoherence-free subspaces, which requires that the Hamiltonian of open quantum systems be engineered according to the incoherent control protocol. In addition, shortcuts to adiabaticity for adiabatic decoherence-free subspaces are also presented based on the transitionless quantum driving method. Finally, we provide an example that consists of a two-level system coupled to a broadband squeezed vacuum field to show our theory. Our approach employs Markovian master equations and the theory can apply to finite-dimensional quantum open systems.

  20. INDOOR SUBSPACING TO IMPLEMENT INDOORGML FOR INDOOR NAVIGATION

    Directory of Open Access Journals (Sweden)

    H. Jung

    2015-10-01

    Full Text Available According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.

  1. Indoor Subspacing to Implement Indoorgml for Indoor Navigation

    Science.gov (United States)

    Jung, H.; Lee, J.

    2015-10-01

    According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.

  2. Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Jensen, Søren Holdt

    2007-01-01

    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...... with working Matlab code and applications in speech processing....

  3. Controllable Subspaces of Open Quantum Dynamical Systems

    International Nuclear Information System (INIS)

    Zhang Ming; Gong Erling; Xie Hongwei; Hu Dewen; Dai Hongyi

    2008-01-01

    This paper discusses the concept of controllable subspace for open quantum dynamical systems. It is constructively demonstrated that combining structural features of decoherence-free subspaces with the ability to perform open-loop coherent control on open quantum systems will allow decoherence-free subspaces to be controllable. This is in contrast to the observation that open quantum dynamical systems are not open-loop controllable. To a certain extent, this paper gives an alternative control theoretical interpretation on why decoherence-free subspaces can be useful for quantum computation.

  4. Geometric subspace updates with applications to online adaptive nonlinear model reduction

    DEFF Research Database (Denmark)

    Zimmermann, Ralf; Peherstorfer, Benjamin; Willcox, Karen

    2018-01-01

    In many scientific applications, including model reduction and image processing, subspaces are used as ansatz spaces for the low-dimensional approximation and reconstruction of the state vectors of interest. We introduce a procedure for adapting an existing subspace based on information from...... Estimation (GROUSE). We establish for GROUSE a closed-form expression for the residual function along the geodesic descent direction. Specific applications of subspace adaptation are discussed in the context of image processing and model reduction of nonlinear partial differential equation systems....

  5. Diomres (k,m): An efficient method based on Krylov subspaces to solve big, dispersed, unsymmetrical linear systems

    Energy Technology Data Exchange (ETDEWEB)

    de la Torre Vega, E. [Instituto de Investigaciones Electricas, Cuernavaca (Mexico); Cesar Suarez Arriaga, M. [Universidad Michoacana SNH, Michoacan (Mexico)

    1995-03-01

    In geothermal simulation processes, MULKOM uses Integrated Finite Differences to solve the corresponding partial differential equations. This method requires to resolve efficiently big linear dispersed systems of non-symmetrical nature on each temporal iteration. The order of the system is usually greater than one thousand its solution could represent around 80% of CPU total calculation time. If the elapsed time solving this class of linear systems is reduced, the duration of numerical simulation decreases notably. When the matrix is big (N{ge}500) and with holes, it is inefficient to handle all the system`s elements, because it is perfectly figured out by its elements distinct of zero, quantity greatly minor than N{sup 2}. In this area, iteration methods introduce advantages with respect to gaussian elimination methods, because these last replenish matrices not having any special distribution of their non-zero elements and because they do not make use of the available solution estimations. The iterating methods of the Conjugated Gradient family, based on the subspaces of Krylov, possess the advantage of improving the convergence speed by means of preconditioning techniques. The creation of DIOMRES(k,m) method guarantees the continuous descent of the residual norm, without incurring in division by zero. This technique converges at most in N iterations if the system`s matrix is symmetrical, it does not employ too much memory to converge and updates immediately the approximation by using incomplete orthogonalization and adequate restarting. A preconditioned version of DIOMRES was applied to problems related to unsymmetrical systems with 1000 unknowns and less than five terms per equation. We found that this technique could reduce notably the time needful to find the solution without requiring memory increment. The coupling of this method to geothermal versions of MULKOM is in process.

  6. Experimental Comparison of Signal Subspace Based Noise Reduction Methods

    DEFF Research Database (Denmark)

    Hansen, Peter Søren Kirk; Hansen, Per Christian; Hansen, Steffen Duus

    1999-01-01

    The signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators...

  7. Subspace preservation, subspace locality, and gluing of completely positive maps

    International Nuclear Information System (INIS)

    Aaberg, Johan

    2004-01-01

    Three concepts concerning completely positive maps (CPMs) and trace preserving CPMs (channels) are introduced and investigated. These are named subspace preserving (SP) CPMs, subspace local (SL) channels, and gluing of CPMs. SP CPMs has, in the case of trace preserving CPMs, a simple interpretation as those which preserve probability weights on a given orthogonal sum decomposition of the Hilbert space of a quantum system. The proposed definition of subspace locality of quantum channels is an attempt to answer the question of what kind of restriction should be put on a channel, if it is to act 'locally' with respect to two 'locations', when these naturally correspond to a separation of the total Hilbert space in an orthogonal sum of subspaces, rather than a tensor product decomposition. As a description of the concept of gluings of quantum channels, consider a pair of 'evolution machines', each with the ability to evolve the internal state of a 'particle' inserted into its input. Each of these machines is characterized by a channel describing the operation the internal state has experienced when the particle is returned at the output. Suppose a particle is put in a superposition between the input of the first and the second machine. Here it is shown that the total evolution caused by a pair of such devices is not uniquely determined by the channels of the two machines. Such 'global' channels describing the machine pair are examples of gluings of the two single machine channels. Various expressions to generate the set of SP and SL channels, as well as expressions to generate the set of gluings of given channels, are deduced. We discuss conceptual aspects of the nature of these channels and the nature of the non-uniqueness of gluings

  8. DETECTION OF CHANGES OF THE SYSTEM TECHNICAL STATE USING STOCHASTIC SUBSPACE OBSERVATION METHOD

    Directory of Open Access Journals (Sweden)

    Andrzej Puchalski

    2014-03-01

    Full Text Available System diagnostics based on vibroacoustics signals, carried out by means of stochastic subspace methods was undertaken in the hereby paper. Subspace methods are the ones based on numerical linear algebra tools. The considered solutions belong to diagnostic methods according to data, leading to the generation of residuals allowing failure recognition of elements and assemblies in machines and devices. The algorithm of diagnostics according to the subspace observation method was applied – in the paper – for the estimation of the valve system of the spark ignition engine.

  9. Active Subspaces of Airfoil Shape Parameterizations

    Science.gov (United States)

    Grey, Zachary J.; Constantine, Paul G.

    2018-05-01

    Design and optimization benefit from understanding the dependence of a quantity of interest (e.g., a design objective or constraint function) on the design variables. A low-dimensional active subspace, when present, identifies important directions in the space of design variables; perturbing a design along the active subspace associated with a particular quantity of interest changes that quantity more, on average, than perturbing the design orthogonally to the active subspace. This low-dimensional structure provides insights that characterize the dependence of quantities of interest on design variables. Airfoil design in a transonic flow field with a parameterized geometry is a popular test problem for design methodologies. We examine two particular airfoil shape parameterizations, PARSEC and CST, and study the active subspaces present in two common design quantities of interest, transonic lift and drag coefficients, under each shape parameterization. We mathematically relate the two parameterizations with a common polynomial series. The active subspaces enable low-dimensional approximations of lift and drag that relate to physical airfoil properties. In particular, we obtain and interpret a two-dimensional approximation of both transonic lift and drag, and we show how these approximation inform a multi-objective design problem.

  10. Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing

    Directory of Open Access Journals (Sweden)

    Majid Shakhsi Dastgahian

    2016-11-01

    Full Text Available Millimeter-wave communication (mmWC is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS and mobile sets (MS. Unlike the conventional MIMO systems, Millimeter-wave (mmW systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.

  11. Subspace exclusion zones for damage localization

    DEFF Research Database (Denmark)

    Bernal, Dionisio; Ulriksen, Martin Dalgaard

    2018-01-01

    , this is exploited in the context of structural damage localization to cast the Subspace Exclusion Zone (SEZ) scheme, which locates damage by reconstructing the captured field quantity shifts from analytical subspaces indexed by postulated boundaries, the so-called exclusion zones (EZs), in a model of the structure...

  12. Subspace methods for pattern recognition in intelligent environment

    CERN Document Server

    Jain, Lakhmi

    2014-01-01

    This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

  13. On Covering Approximation Subspaces

    Directory of Open Access Journals (Sweden)

    Xun Ge

    2009-06-01

    Full Text Available Let (U';C' be a subspace of a covering approximation space (U;C and X⊂U'. In this paper, we show that and B'(X⊂B(X∩U'. Also, iff (U;C has Property Multiplication. Furthermore, some connections between outer (resp. inner definable subsets in (U;C and outer (resp. inner definable subsets in (U';C' are established. These results answer a question on covering approximation subspace posed by J. Li, and are helpful to obtain further applications of Pawlak rough set theory in pattern recognition and artificial intelligence.

  14. Subspace orthogonalization for substructuring preconditioners for nonsymmetric systems of linear equations

    Energy Technology Data Exchange (ETDEWEB)

    Starke, G. [Universitaet Karlsruhe (Germany)

    1994-12-31

    For nonselfadjoint elliptic boundary value problems which are preconditioned by a substructuring method, i.e., nonoverlapping domain decomposition, the author introduces and studies the concept of subspace orthogonalization. In subspace orthogonalization variants of Krylov methods the computation of inner products and vector updates, and the storage of basis elements is restricted to a (presumably small) subspace, in this case the edge and vertex unknowns with respect to the partitioning into subdomains. The author investigates subspace orthogonalization for two specific iterative algorithms, GMRES and the full orthogonalization method (FOM). This is intended to eliminate certain drawbacks of the Arnoldi-based Krylov subspace methods mentioned above. Above all, the length of the Arnoldi recurrences grows linearly with the iteration index which is therefore restricted to the number of basis elements that can be held in memory. Restarts become necessary and this often results in much slower convergence. The subspace orthogonalization methods, in contrast, require the storage of only the edge and vertex unknowns of each basis element which means that one can iterate much longer before restarts become necessary. Moreover, the computation of inner products is also restricted to the edge and vertex points which avoids the disturbance of the computational flow associated with the solution of subdomain problems. The author views subspace orthogonalization as an alternative to restarting or truncating Krylov subspace methods for nonsymmetric linear systems of equations. Instead of shortening the recurrences, one restricts them to a subset of the unknowns which has to be carefully chosen in order to be able to extend this partial solution to the entire space. The author discusses the convergence properties of these iteration schemes and its advantages compared to restarted or truncated versions of Krylov methods applied to the full preconditioned system.

  15. Conjunctive patches subspace learning with side information for collaborative image retrieval.

    Science.gov (United States)

    Zhang, Lining; Wang, Lipo; Lin, Weisi

    2012-08-01

    Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.

  16. On Optimal Short Recurrences for Generating Orthogonal Krylov Subspace Bases. Dedicated to Gene Golub

    Czech Academy of Sciences Publication Activity Database

    Liesen, J.; Strakoš, Zdeněk

    2008-01-01

    Roč. 50, č. 3 (2008), s. 485-503 ISSN 0036-1445 R&D Projects: GA AV ČR 1ET400300415; GA AV ČR IAA100300802 Institutional research plan: CEZ:AV0Z10300504 Keywords : Krylov subspace methods * orthogonal bases * short reccurences * conjugate gradient -like methods Subject RIV: IN - Informatics, Computer Science Impact factor: 2.739, year: 2008

  17. Linear Subspace Ranking Hashing for Cross-Modal Retrieval.

    Science.gov (United States)

    Li, Kai; Qi, Guo-Jun; Ye, Jun; Hua, Kien A

    2017-09-01

    Hashing has attracted a great deal of research in recent years due to its effectiveness for the retrieval and indexing of large-scale high-dimensional multimedia data. In this paper, we propose a novel ranking-based hashing framework that maps data from different modalities into a common Hamming space where the cross-modal similarity can be measured using Hamming distance. Unlike existing cross-modal hashing algorithms where the learned hash functions are binary space partitioning functions, such as the sign and threshold function, the proposed hashing scheme takes advantage of a new class of hash functions closely related to rank correlation measures which are known to be scale-invariant, numerically stable, and highly nonlinear. Specifically, we jointly learn two groups of linear subspaces, one for each modality, so that features' ranking orders in different linear subspaces maximally preserve the cross-modal similarities. We show that the ranking-based hash function has a natural probabilistic approximation which transforms the original highly discontinuous optimization problem into one that can be efficiently solved using simple gradient descent algorithms. The proposed hashing framework is also flexible in the sense that the optimization procedures are not tied up to any specific form of loss function, which is typical for existing cross-modal hashing methods, but rather we can flexibly accommodate different loss functions with minimal changes to the learning steps. We demonstrate through extensive experiments on four widely-used real-world multimodal datasets that the proposed cross-modal hashing method can achieve competitive performance against several state-of-the-arts with only moderate training and testing time.

  18. Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling.

    Science.gov (United States)

    Zhao, Bo; Setsompop, Kawin; Adalsteinsson, Elfar; Gagoski, Borjan; Ye, Huihui; Ma, Dan; Jiang, Yun; Ellen Grant, P; Griswold, Mark A; Wald, Lawrence L

    2018-02-01

    This article introduces a constrained imaging method based on low-rank and subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). A new model-based imaging method is developed for MRF to reconstruct high-quality time-series images and accurate tissue parameter maps (e.g., T 1 , T 2 , and spin density maps). Specifically, the proposed method exploits low-rank approximations of MRF time-series images, and further enforces temporal subspace constraints to capture magnetization dynamics. This allows the time-series image reconstruction problem to be formulated as a simple linear least-squares problem, which enables efficient computation. After image reconstruction, tissue parameter maps are estimated via dictionary-based pattern matching, as in the conventional approach. The effectiveness of the proposed method was evaluated with in vivo experiments. Compared with the conventional MRF reconstruction, the proposed method reconstructs time-series images with significantly reduced aliasing artifacts and noise contamination. Although the conventional approach exhibits some robustness to these corruptions, the improved time-series image reconstruction in turn provides more accurate tissue parameter maps. The improvement is pronounced especially when the acquisition time becomes short. The proposed method significantly improves the accuracy of MRF, and also reduces data acquisition time. Magn Reson Med 79:933-942, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  19. CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

    Science.gov (United States)

    Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

    2018-02-01

    In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

  20. A subspace preconditioning algorithm for eigenvector/eigenvalue computation

    Energy Technology Data Exchange (ETDEWEB)

    Bramble, J.H.; Knyazev, A.V.; Pasciak, J.E.

    1996-12-31

    We consider the problem of computing a modest number of the smallest eigenvalues along with orthogonal bases for the corresponding eigen-spaces of a symmetric positive definite matrix. In our applications, the dimension of a matrix is large and the cost of its inverting is prohibitive. In this paper, we shall develop an effective parallelizable technique for computing these eigenvalues and eigenvectors utilizing subspace iteration and preconditioning. Estimates will be provided which show that the preconditioned method converges linearly and uniformly in the matrix dimension when used with a uniform preconditioner under the assumption that the approximating subspace is close enough to the span of desired eigenvectors.

  1. Consistency analysis of subspace identification methods based on a linear regression approach

    DEFF Research Database (Denmark)

    Knudsen, Torben

    2001-01-01

    In the literature results can be found which claim consistency for the subspace method under certain quite weak assumptions. Unfortunately, a new result gives a counter example showing inconsistency under these assumptions and then gives new more strict sufficient assumptions which however does n...... not include important model structures as e.g. Box-Jenkins. Based on a simple least squares approach this paper shows the possible inconsistency under the weak assumptions and develops only slightly stricter assumptions sufficient for consistency and which includes any model structure...

  2. Discrete-State Stochastic Models of Calcium-Regulated Calcium Influx and Subspace Dynamics Are Not Well-Approximated by ODEs That Neglect Concentration Fluctuations

    Science.gov (United States)

    Weinberg, Seth H.; Smith, Gregory D.

    2012-01-01

    Cardiac myocyte calcium signaling is often modeled using deterministic ordinary differential equations (ODEs) and mass-action kinetics. However, spatially restricted “domains” associated with calcium influx are small enough (e.g., 10−17 liters) that local signaling may involve 1–100 calcium ions. Is it appropriate to model the dynamics of subspace calcium using deterministic ODEs or, alternatively, do we require stochastic descriptions that account for the fundamentally discrete nature of these local calcium signals? To address this question, we constructed a minimal Markov model of a calcium-regulated calcium channel and associated subspace. We compared the expected value of fluctuating subspace calcium concentration (a result that accounts for the small subspace volume) with the corresponding deterministic model (an approximation that assumes large system size). When subspace calcium did not regulate calcium influx, the deterministic and stochastic descriptions agreed. However, when calcium binding altered channel activity in the model, the continuous deterministic description often deviated significantly from the discrete stochastic model, unless the subspace volume is unrealistically large and/or the kinetics of the calcium binding are sufficiently fast. This principle was also demonstrated using a physiologically realistic model of calmodulin regulation of L-type calcium channels introduced by Yue and coworkers. PMID:23509597

  3. An alternative subspace approach to EEG dipole source localization

    Science.gov (United States)

    Xu, Xiao-Liang; Xu, Bobby; He, Bin

    2004-01-01

    In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.

  4. An alternative subspace approach to EEG dipole source localization

    International Nuclear Information System (INIS)

    Xu Xiaoliang; Xu, Bobby; He Bin

    2004-01-01

    In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist

  5. Using CUDA Technology for Defining the Stiffness Matrix in the Subspace of Eigenvectors

    Directory of Open Access Journals (Sweden)

    Yu. V. Berchun

    2015-01-01

    Full Text Available The aim is to improve the performance of solving a problem of deformable solid mechanics through the use of GPGPU. The paper describes technologies for computing systems using both a central and a graphics processor and provides motivation for using CUDA technology as the efficient one.The paper also analyses methods to solve the problem of defining natural frequencies and design waveforms, i.e. an iteration method in the subspace. The method includes several stages. The paper considers the most resource-hungry stage, which defines the stiffness matrix in the subspace of eigenforms and gives the mathematical interpretation of this stage.The GPU choice as a computing device is justified. The paper presents an algorithm for calculating the stiffness matrix in the subspace of eigenforms taking into consideration the features of input data. The global stiffness matrix is very sparse, and its size can reach tens of millions. Therefore, it is represented as a set of the stiffness matrices of the single elements of a model. The paper analyses methods of data representation in the software and selects the best practices for GPU computing.It describes the software implementation using CUDA technology to calculate the stiffness matrix in the subspace of eigenforms. Due to the input data nature, it is impossible to use the universal libraries of matrix computations (cuSPARSE and cuBLAS for loading the GPU. For efficient use of GPU resources in the software implementation, the stiffness matrices of elements are built in the block matrices of a special form. The advantages of using shared memory in GPU calculations are described.The transfer to the GPU computations allowed a twentyfold increase in performance (as compared to the multithreaded CPU-implementation on the model of middle dimensions (degrees of freedom about 2 million. Such an acceleration of one stage speeds up defining the natural frequencies and waveforms by the iteration method in a subspace

  6. Random matrix improved subspace clustering

    KAUST Repository

    Couillet, Romain

    2017-03-06

    This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based on vector channel observations in the context of massive MIMO wireless communication networks is provided.

  7. Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation

    OpenAIRE

    Bi, Qiao; Guo, Liu; Ruda, H. E.

    2010-01-01

    A formalism for quantum computing in decoherence-free subspaces is presented. The constructed subspaces are partial triangulated to an index related to environment. The quantum states in the subspaces are just projected states which are ruled by a subdynamic kinetic equation. These projected states can be used to perform ideal quantum logical operations without decoherence.

  8. Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

    KAUST Repository

    Siddiqui, Bilal A.; El-Ferik, Sami; Abdelkader, Mohamed

    2016-01-01

    In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

  9. Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

    KAUST Repository

    Siddiqui, Bilal A.

    2016-07-26

    In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

  10. Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization

    KAUST Repository

    Fornasier, Massimo

    2009-01-01

    This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a seminorm for a subspace. The optimization is realized by alternating minimizations of the functional on a sequence of orthogonal subspaces. On each subspace an iterative proximity-map algorithm is implemented via oblique thresholding, which is the main new tool introduced in this work. We provide convergence conditions for the algorithm in order to compute minimizers of the target energy. Analogous results are derived for a parallel variant of the algorithm. Applications are presented in domain decomposition methods for degenerate elliptic PDEs arising in total variation minimization and in accelerated sparse recovery algorithms based on 1-minimization. We include numerical examples which show e.cient solutions to classical problems in signal and image processing. © 2009 Society for Industrial and Applied Physics.

  11. Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation

    Directory of Open Access Journals (Sweden)

    Qiao Bi

    2010-01-01

    Full Text Available A formalism for quantum computing in decoherence-free subspaces is presented. The constructed subspaces are partial triangulated to an index related to environment. The quantum states in the subspaces are just projected states which are ruled by a subdynamic kinetic equation. These projected states can be used to perform ideal quantum logical operations without decoherence.

  12. Krylov subspace methods for solving large unsymmetric linear systems

    International Nuclear Information System (INIS)

    Saad, Y.

    1981-01-01

    Some algorithms based upon a projection process onto the Krylov subspace K/sub m/ = Span(r 0 , Ar 0 ,...,A/sup m/-1r 0 ) are developed, generalizing the method of conjugate gradients to unsymmetric systems. These methods are extensions of Arnoldi's algorithm for solving eigenvalue problems. The convergence is analyzed in terms of the distance of the solution to the subspace K/sub m/ and some error bounds are established showing, in particular, a similarity with the conjugate gradient method (for symmetric matrices) when the eigenvalues are real. Several numerical experiments are described and discussed

  13. LBAS: Lanczos Bidiagonalization with Subspace Augmentation for Discrete Inverse Problems

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Abe, Kyniyoshi

    The regularizing properties of Lanczos bidiagonalization are powerful when the underlying Krylov subspace captures the dominating components of the solution. In some applications the regularized solution can be further improved by augmenting the Krylov subspace with a low-dimensional subspace tha...

  14. Different structures on subspaces of OsckM

    Directory of Open Access Journals (Sweden)

    Čomić Irena

    2013-01-01

    Full Text Available The geometry of OsckM spaces was introduced by R. Miron and Gh. Atanasiu in [6] and [7]. The theory of these spaces was developed by R. Miron and his cooperators from Romania, Japan and other countries in several books and many papers. Only some of them are mentioned in references. Here we recall the construction of adapted bases in T(OsckM and T*(OsckM, which are comprehensive with the J structure. The theory of two complementary family of subspaces is presented as it was done in [2] and [4]. The operators J,J, θ,θ, p, p* are introduced in the ambient space and subspaces. Some new relations between them are established. The action of these operators on Liouville vector fields are examined.

  15. Hardware-efficient bosonic quantum error-correcting codes based on symmetry operators

    Science.gov (United States)

    Niu, Murphy Yuezhen; Chuang, Isaac L.; Shapiro, Jeffrey H.

    2018-03-01

    We establish a symmetry-operator framework for designing quantum error-correcting (QEC) codes based on fundamental properties of the underlying system dynamics. Based on this framework, we propose three hardware-efficient bosonic QEC codes that are suitable for χ(2 )-interaction based quantum computation in multimode Fock bases: the χ(2 ) parity-check code, the χ(2 ) embedded error-correcting code, and the χ(2 ) binomial code. All of these QEC codes detect photon-loss or photon-gain errors by means of photon-number parity measurements, and then correct them via χ(2 ) Hamiltonian evolutions and linear-optics transformations. Our symmetry-operator framework provides a systematic procedure for finding QEC codes that are not stabilizer codes, and it enables convenient extension of a given encoding to higher-dimensional qudit bases. The χ(2 ) binomial code is of special interest because, with m ≤N identified from channel monitoring, it can correct m -photon-loss errors, or m -photon-gain errors, or (m -1 )th -order dephasing errors using logical qudits that are encoded in O (N ) photons. In comparison, other bosonic QEC codes require O (N2) photons to correct the same degree of bosonic errors. Such improved photon efficiency underscores the additional error-correction power that can be provided by channel monitoring. We develop quantum Hamming bounds for photon-loss errors in the code subspaces associated with the χ(2 ) parity-check code and the χ(2 ) embedded error-correcting code, and we prove that these codes saturate their respective bounds. Our χ(2 ) QEC codes exhibit hardware efficiency in that they address the principal error mechanisms and exploit the available physical interactions of the underlying hardware, thus reducing the physical resources required for implementing their encoding, decoding, and error-correction operations, and their universal encoded-basis gate sets.

  16. Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction.

    Science.gov (United States)

    Chen, Peng; Yang, Yixin; Wang, Yong; Ma, Yuanliang

    2018-05-08

    When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers.

  17. A General Algorithm for Reusing Krylov Subspace Information. I. Unsteady Navier-Stokes

    Science.gov (United States)

    Carpenter, Mark H.; Vuik, C.; Lucas, Peter; vanGijzen, Martin; Bijl, Hester

    2010-01-01

    A general algorithm is developed that reuses available information to accelerate the iterative convergence of linear systems with multiple right-hand sides A x = b (sup i), which are commonly encountered in steady or unsteady simulations of nonlinear equations. The algorithm is based on the classical GMRES algorithm with eigenvector enrichment but also includes a Galerkin projection preprocessing step and several novel Krylov subspace reuse strategies. The new approach is applied to a set of test problems, including an unsteady turbulent airfoil, and is shown in some cases to provide significant improvement in computational efficiency relative to baseline approaches.

  18. Subspace confinement: how good is your qubit?

    International Nuclear Information System (INIS)

    Devitt, Simon J; Schirmer, Sonia G; Oi, Daniel K L; Cole, Jared H; Hollenberg, Lloyd C L

    2007-01-01

    The basic operating element of standard quantum computation is the qubit, an isolated two-level system that can be accurately controlled, initialized and measured. However, the majority of proposed physical architectures for quantum computation are built from systems that contain much more complicated Hilbert space structures. Hence, defining a qubit requires the identification of an appropriate controllable two-dimensional sub-system. This prompts the obvious question of how well a qubit, thus defined, is confined to this subspace, and whether we can experimentally quantify the potential leakage into states outside the qubit subspace. We demonstrate how subspace leakage can be characterized using minimal theoretical assumptions by examining the Fourier spectrum of the oscillation experiment

  19. Visual tracking based on the sparse representation of the PCA subspace

    Science.gov (United States)

    Chen, Dian-bing; Zhu, Ming; Wang, Hui-li

    2017-09-01

    We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis (PCA) subspace, and then we employ an L 1 regularization to restrict the sparsity of the residual term, an L 2 regularization term to restrict the sparsity of the representation coefficients, and an L 2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.

  20. Cumulant-Based Coherent Signal Subspace Method for Bearing and Range Estimation

    Directory of Open Access Journals (Sweden)

    Bourennane Salah

    2007-01-01

    Full Text Available A new method for simultaneous range and bearing estimation for buried objects in the presence of an unknown Gaussian noise is proposed. This method uses the MUSIC algorithm with noise subspace estimated by using the slice fourth-order cumulant matrix of the received data. The higher-order statistics aim at the removal of the additive unknown Gaussian noise. The bilinear focusing operator is used to decorrelate the received signals and to estimate the coherent signal subspace. A new source steering vector is proposed including the acoustic scattering model at each sensor. Range and bearing of the objects at each sensor are expressed as a function of those at the first sensor. This leads to the improvement of object localization anywhere, in the near-field or in the far-field zone of the sensor array. Finally, the performances of the proposed method are validated on data recorded during experiments in a water tank.

  1. Towards efficient solar-to-hydrogen conversion: Fundamentals and recent progress in copper-based chalcogenide photocathodes

    Directory of Open Access Journals (Sweden)

    Chen Yubin

    2016-09-01

    Full Text Available Photoelectrochemical (PEC water splitting for hydrogen generation has been considered as a promising route to convert and store solar energy into chemical fuels. In terms of its large-scale application, seeking semiconductor photoelectrodes with high efficiency and good stability should be essential. Although an enormous number of materials have been explored for solar water splitting in the last several decades, challenges still remain for the practical application. P-type copper-based chalcogenides, such as Cu(In, GaSe2 and Cu2ZnSnS4, have shown impressive performance in photovoltaics due to narrow bandgaps, high absorption coefficients, and good carrier transport properties. The obtained high efficiencies in photovoltaics have promoted the utilization of these materials into the field of PEC water splitting. A comprehensive review on copper-based chalcogenides for solar-to-hydrogen conversion would help advance the research in this expanding area. This review will cover the physicochemical properties of copper-based chalco-genides, developments of various photocathodes, strategies to enhance the PEC activity and stability, introductions of tandem PEC cells, and finally, prospects on their potential for the practical solar-to-hydrogen conversion. We believe this review article can provide some insights of fundamentals and applications of copper-based chalco-genide thin films for PEC water splitting.

  2. Reduced-Rank Adaptive Filtering Using Krylov Subspace

    Directory of Open Access Journals (Sweden)

    Sergueï Burykh

    2003-01-01

    Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.

  3. An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

    International Nuclear Information System (INIS)

    Zhang, Liangwei; Lin, Jing; Karim, Ramin

    2015-01-01

    The accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces. - Highlights: • An anomaly detection approach for high-dimensional reliability data is proposed. • The approach selects relevant subspaces by assessing vectorial angles. • The novel ABSAD approach displays superior accuracy over other alternatives. • Numerical illustration approves its efficacy in fault detection applications

  4. Reachable Distance Space: Efficient Sampling-Based Planning for Spatially Constrained Systems

    KAUST Repository

    Xinyu Tang,

    2010-01-25

    Motion planning for spatially constrained robots is difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end-effector placement for articulated linkages. It is usually computationally too expensive to apply sampling-based planners to these problems since it is difficult to generate valid configurations. We overcome this challenge by redefining the robot\\'s degrees of freedom and constraints into a new set of parameters, called reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the number of the robot\\'s degrees of freedom. In addition to supporting efficient sampling of configurations, we show that the RD-space formulation naturally supports planning and, in particular, we design a local planner suitable for use by sampling-based planners. We demonstrate the effectiveness and efficiency of our approach for several systems including closed chain planning with multiple loops, restricted end-effector sampling, and on-line planning for drawing/sculpting. We can sample single-loop closed chain systems with 1,000 links in time comparable to open chain sampling, and we can generate samples for 1,000-link multi-loop systems of varying topologies in less than a second. © 2010 The Author(s).

  5. Boundary regularity of Nevanlinna domains and univalent functions in model subspaces

    International Nuclear Information System (INIS)

    Baranov, Anton D; Fedorovskiy, Konstantin Yu

    2011-01-01

    In the paper we study boundary regularity of Nevanlinna domains, which have appeared in problems of uniform approximation by polyanalytic polynomials. A new method for constructing Nevanlinna domains with essentially irregular nonanalytic boundaries is suggested; this method is based on finding appropriate univalent functions in model subspaces, that is, in subspaces of the form K Θ =H 2 ominus ΘH 2 , where Θ is an inner function. To describe the irregularity of the boundaries of the domains obtained, recent results by Dolzhenko about boundary regularity of conformal mappings are used. Bibliography: 18 titles.

  6. Monomial codes seen as invariant subspaces

    Directory of Open Access Journals (Sweden)

    García-Planas María Isabel

    2017-08-01

    Full Text Available It is well known that cyclic codes are very useful because of their applications, since they are not computationally expensive and encoding can be easily implemented. The relationship between cyclic codes and invariant subspaces is also well known. In this paper a generalization of this relationship is presented between monomial codes over a finite field and hyperinvariant subspaces of n under an appropriate linear transformation. Using techniques of Linear Algebra it is possible to deduce certain properties for this particular type of codes, generalizing known results on cyclic codes.

  7. On the maximal dimension of a completely entangled subspace for ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    dim S = d1d2 ...dk − (d1 +···+ dk) + k − 1, where E is the collection of all completely entangled subspaces. When H1 = H2 and k = 2 an explicit orthonormal basis of a maximal completely entangled subspace of H1 ⊗ H2 is given. We also introduce a more delicate notion of a perfectly entangled subspace for a multipartite ...

  8. A New Inexact Inverse Subspace Iteration for Generalized Eigenvalue Problems

    Directory of Open Access Journals (Sweden)

    Fatemeh Mohammad

    2014-05-01

    Full Text Available In this paper‎, ‎we represent an inexact inverse‎ ‎subspace iteration method for computing a few eigenpairs of the‎ ‎generalized eigenvalue problem $Ax = \\lambda Bx$[Q.~Ye and P.~Zhang‎, ‎Inexact inverse subspace iteration for generalized eigenvalue‎ ‎problems‎, ‎Linear Algebra and its Application‎, ‎434 (2011 1697-1715‎‎]‎. ‎In particular‎, ‎the linear convergence property of the inverse‎ ‎subspace iteration is preserved‎.

  9. Curve Evolution in Subspaces and Exploring the Metameric Class of Histogram of Gradient Orientation based Features using Nonlinear Projection Methods

    DEFF Research Database (Denmark)

    Tatu, Aditya Jayant

    This thesis deals with two unrelated issues, restricting curve evolution to subspaces and computing image patches in the equivalence class of Histogram of Gradient orientation based features using nonlinear projection methods. Curve evolution is a well known method used in various applications like...... tracking interfaces, active contour based segmentation methods and others. It can also be used to study shape spaces, as deforming a shape can be thought of as evolving its boundary curve. During curve evolution a curve traces out a path in the infinite dimensional space of curves. Due to application...... specific requirements like shape priors or a given data model, and due to limitations of the computer, the computed curve evolution forms a path in some finite dimensional subspace of the space of curves. We give methods to restrict the curve evolution to a finite dimensional linear or implicitly defined...

  10. Kernel based subspace projection of near infrared hyperspectral images of maize kernels

    DEFF Research Database (Denmark)

    Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben

    2009-01-01

    In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...

  11. Semitransitive subspaces of operators

    Czech Academy of Sciences Publication Activity Database

    Bernik, J.; Drnovšek, R.; Hadwin, D.; Jafarian, A.; Bukovšek, D.K.; Košir, T.; Fijavž, M.K.; Laffey, T.; Livshits, L.; Mastnak, M.; Meshulam, R.; Müller, Vladimír; Nordgren, E.; Okniński, J.; Omladič, M.; Radjavi, H.; Sourour, A.; Timoney, R.

    2006-01-01

    Roč. 15, č. 1 (2006), s. 225-238 E-ISSN 1081-3810 Institutional research plan: CEZ:AV0Z10190503 Keywords : semitransitive subspaces Subject RIV: BA - General Mathematics Impact factor: 0.322, year: 2006 http://www.math.technion.ac.il/iic/ ela

  12. Random matrix improved subspace clustering

    KAUST Repository

    Couillet, Romain; Kammoun, Abla

    2017-01-01

    This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show

  13. Sparse subspace clustering for data with missing entries and high-rank matrix completion.

    Science.gov (United States)

    Fan, Jicong; Chow, Tommy W S

    2017-09-01

    Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Subspace K-means clustering

    NARCIS (Netherlands)

    Timmerman, Marieke E.; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

    2013-01-01

    To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the

  15. A subspace approach to high-resolution spectroscopic imaging.

    Science.gov (United States)

    Lam, Fan; Liang, Zhi-Pei

    2014-04-01

    To accelerate spectroscopic imaging using sparse sampling of (k,t)-space and subspace (or low-rank) modeling to enable high-resolution metabolic imaging with good signal-to-noise ratio. The proposed method, called SPectroscopic Imaging by exploiting spatiospectral CorrElation, exploits a unique property known as partial separability of spectroscopic signals. This property indicates that high-dimensional spectroscopic signals reside in a very low-dimensional subspace and enables special data acquisition and image reconstruction strategies to be used to obtain high-resolution spatiospectral distributions with good signal-to-noise ratio. More specifically, a hybrid chemical shift imaging/echo-planar spectroscopic imaging pulse sequence is proposed for sparse sampling of (k,t)-space, and a low-rank model-based algorithm is proposed for subspace estimation and image reconstruction from sparse data with the capability to incorporate prior information and field inhomogeneity correction. The performance of the proposed method has been evaluated using both computer simulations and phantom studies, which produced very encouraging results. For two-dimensional spectroscopic imaging experiments on a metabolite phantom, a factor of 10 acceleration was achieved with a minimal loss in signal-to-noise ratio compared to the long chemical shift imaging experiments and with a significant gain in signal-to-noise ratio compared to the accelerated echo-planar spectroscopic imaging experiments. The proposed method, SPectroscopic Imaging by exploiting spatiospectral CorrElation, is able to significantly accelerate spectroscopic imaging experiments, making high-resolution metabolic imaging possible. Copyright © 2014 Wiley Periodicals, Inc.

  16. Lie n-derivations on 7 -subspace lattice algebras

    Indian Academy of Sciences (India)

    all x ∈ K and all A ∈ Alg L. Based on this result, a complete characterization of linear n-Lie derivations on Alg L is obtained. Keywords. J -subspace lattice algebras; Lie derivations; Lie n-derivations; derivations. 2010 Mathematics Subject Classification. 47B47, 47L35. 1. Introduction. Let A be an algebra. Recall that a linear ...

  17. Gamow state vectors as functionals over subspaces of the nuclear space

    International Nuclear Information System (INIS)

    Bohm, A.

    1979-12-01

    Exponentially decaying Gamow state vectors are obtained from S-matrix poles in the lower half of the second sheet, and are defined as functionals over a subspace of the nuclear space, PHI. Exponentially growing Gamow state vectors are obtained from S-matrix poles in the upper half of the second sheet, and are defined as functionals over another subspace of PHI. On functionals over these two subspaces the dynamical group of time development splits into two semigroups

  18. Structural damage diagnosis based on on-line recursive stochastic subspace identification

    International Nuclear Information System (INIS)

    Loh, Chin-Hsiung; Weng, Jian-Huang; Liu, Yi-Cheng; Lin, Pei-Yang; Huang, Shieh-Kung

    2011-01-01

    This paper presents a recursive stochastic subspace identification (RSSI) technique for on-line and almost real-time structural damage diagnosis using output-only measurements. Through RSSI the time-varying natural frequencies of a system can be identified. To reduce the computation time in conducting LQ decomposition in RSSI, the Givens rotation as well as the matrix operation appending a new data set are derived. The relationship between the size of the Hankel matrix and the data length in each shifting moving window is examined so as to extract the time-varying features of the system without loss of generality and to establish on-line and almost real-time system identification. The result from the RSSI technique can also be applied to structural damage diagnosis. Off-line data-driven stochastic subspace identification was used first to establish the system matrix from the measurements of an undamaged (reference) case. Then the RSSI technique incorporating a Kalman estimator is used to extract the dynamic characteristics of the system through continuous monitoring data. The predicted residual error is defined as a damage feature and through the outlier statistics provides an indicator of damage. Verification of the proposed identification algorithm by using the bridge scouring test data and white noise response data of a reinforced concrete frame structure is conducted

  19. View subspaces for indexing and retrieval of 3D models

    Science.gov (United States)

    Dutagaci, Helin; Godil, Afzal; Sankur, Bülent; Yemez, Yücel

    2010-02-01

    View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms.

  20. Matrix Krylov subspace methods for image restoration

    Directory of Open Access Journals (Sweden)

    khalide jbilou

    2015-09-01

    Full Text Available In the present paper, we consider some matrix Krylov subspace methods for solving ill-posed linear matrix equations and in those problems coming from the restoration of blurred and noisy images. Applying the well known Tikhonov regularization procedure leads to a Sylvester matrix equation depending the Tikhonov regularized parameter. We apply the matrix versions of the well known Krylov subspace methods, namely the Least Squared (LSQR and the conjugate gradient (CG methods to get approximate solutions representing the restored images. Some numerical tests are presented to show the effectiveness of the proposed methods.

  1. On the numerical stability analysis of pipelined Krylov subspace methods

    Czech Academy of Sciences Publication Activity Database

    Carson, E.T.; Rozložník, Miroslav; Strakoš, Z.; Tichý, P.; Tůma, M.

    submitted 2017 (2018) R&D Projects: GA ČR GA13-06684S Grant - others:GA MŠk(CZ) LL1202 Institutional support: RVO:67985807 Keywords : Krylov subspace methods * the conjugate gradient method * numerical stability * inexact computations * delay of convergence * maximal attainable accuracy * pipelined Krylov subspace methods * exascale computations

  2. Microscopic theory of dynamical subspace for large amplitude collective motion

    International Nuclear Information System (INIS)

    Sakata, Fumihiko; Marumori, Toshio; Ogura, Masanori.

    1986-01-01

    A full quantum theory appropriate for describing large amplitude collective motion is proposed by exploiting the basic idea of the semi-classical theory so far developed within the time-depedent Hartree-Fock theory. A central problem of the quantum theory is how to determine an optimal representation called a dynamical representation specific for the collective subspace where the large amplitude collective motion is replicated as precisely as possible. As an extension of the semi-classical theory where the concept of an approximate integral surface played an important role, the collective subspace is properly characterized by introducing a concept of an approximate invariant subspace of the Hamiltonian. (author)

  3. ANNIT - An Efficient Inversion Algorithm based on Prediction Principles

    Science.gov (United States)

    Růžek, B.; Kolář, P.

    2009-04-01

    Solution of inverse problems represents meaningful job in geophysics. The amount of data is continuously increasing, methods of modeling are being improved and the computer facilities are also advancing great technical progress. Therefore the development of new and efficient algorithms and computer codes for both forward and inverse modeling is still up to date. ANNIT is contributing to this stream since it is a tool for efficient solution of a set of non-linear equations. Typical geophysical problems are based on parametric approach. The system is characterized by a vector of parameters p, the response of the system is characterized by a vector of data d. The forward problem is usually represented by unique mapping F(p)=d. The inverse problem is much more complex and the inverse mapping p=G(d) is available in an analytical or closed form only exceptionally and generally it may not exist at all. Technically, both forward and inverse mapping F and G are sets of non-linear equations. ANNIT solves such situation as follows: (i) joint subspaces {pD, pM} of original data and model spaces D, M, resp. are searched for, within which the forward mapping F is sufficiently smooth that the inverse mapping G does exist, (ii) numerical approximation of G in subspaces {pD, pM} is found, (iii) candidate solution is predicted by using this numerical approximation. ANNIT is working in an iterative way in cycles. The subspaces {pD, pM} are searched for by generating suitable populations of individuals (models) covering data and model spaces. The approximation of the inverse mapping is made by using three methods: (a) linear regression, (b) Radial Basis Function Network technique, (c) linear prediction (also known as "Kriging"). The ANNIT algorithm has built in also an archive of already evaluated models. Archive models are re-used in a suitable way and thus the number of forward evaluations is minimized. ANNIT is now implemented both in MATLAB and SCILAB. Numerical tests show good

  4. Krylov subspace method for evaluating the self-energy matrices in electron transport calculations

    DEFF Research Database (Denmark)

    Sørensen, Hans Henrik Brandenborg; Hansen, Per Christian; Petersen, D. E.

    2008-01-01

    We present a Krylov subspace method for evaluating the self-energy matrices used in the Green's function formulation of electron transport in nanoscale devices. A procedure based on the Arnoldi method is employed to obtain solutions of the quadratic eigenvalue problem associated with the infinite...... calculations. Numerical tests within a density functional theory framework are provided to validate the accuracy and robustness of the proposed method, which in most cases is an order of magnitude faster than conventional methods.......We present a Krylov subspace method for evaluating the self-energy matrices used in the Green's function formulation of electron transport in nanoscale devices. A procedure based on the Arnoldi method is employed to obtain solutions of the quadratic eigenvalue problem associated with the infinite...

  5. Development of Subspace-based Hybrid Monte Carlo-Deterministric Algorithms for Reactor Physics Calculations

    International Nuclear Information System (INIS)

    Abdel-Khalik, Hany S.; Zhang, Qiong

    2014-01-01

    The development of hybrid Monte-Carlo-Deterministic (MC-DT) approaches, taking place over the past few decades, have primarily focused on shielding and detection applications where the analysis requires a small number of responses, i.e. at the detector locations(s). This work further develops a recently introduced global variance reduction approach, denoted by the SUBSPACE approach is designed to allow the use of MC simulation, currently limited to benchmarking calculations, for routine engineering calculations. By way of demonstration, the SUBSPACE approach is applied to assembly level calculations used to generate the few-group homogenized cross-sections. These models are typically expensive and need to be executed in the order of 10 3 - 10 5 times to properly characterize the few-group cross-sections for downstream core-wide calculations. Applicability to k-eigenvalue core-wide models is also demonstrated in this work. Given the favorable results obtained in this work, we believe the applicability of the MC method for reactor analysis calculations could be realized in the near future.

  6. LogDet Rank Minimization with Application to Subspace Clustering

    Directory of Open Access Journals (Sweden)

    Zhao Kang

    2015-01-01

    Full Text Available Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.

  7. Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

    Full Text Available Small aperture microphone arrays provide many advantages for portable devices and hearing aid equipment. In this paper, a subspace based localization method is proposed for acoustic source using small aperture arrays. The effects of array aperture on localization are analyzed by using array response (array manifold. Besides array aperture, the frequency of acoustic source and the variance of signal power are simulated to demonstrate how to optimize localization performance, which is carried out by introducing frequency error with the proposed method. The proposed method for 5 mm array aperture is validated by simulations and experiments with MEMS microphone arrays. Different types of acoustic sources can be localized with the highest precision of 6 degrees even in the presence of wind noise and other noises. Furthermore, the proposed method reduces the computational complexity compared with other methods.

  8. Independence and totalness of subspaces in phase space methods

    Science.gov (United States)

    Vourdas, A.

    2018-04-01

    The concepts of independence and totalness of subspaces are introduced in the context of quasi-probability distributions in phase space, for quantum systems with finite-dimensional Hilbert space. It is shown that due to the non-distributivity of the lattice of subspaces, there are various levels of independence, from pairwise independence up to (full) independence. Pairwise totalness, totalness and other intermediate concepts are also introduced, which roughly express that the subspaces overlap strongly among themselves, and they cover the full Hilbert space. A duality between independence and totalness, that involves orthocomplementation (logical NOT operation), is discussed. Another approach to independence is also studied, using Rota's formalism on independent partitions of the Hilbert space. This is used to define informational independence, which is proved to be equivalent to independence. As an application, the pentagram (used in discussions on contextuality) is analysed using these concepts.

  9. On spectral subspaces and their applications to automorphism groups

    International Nuclear Information System (INIS)

    Olesen, Dorte

    1974-03-01

    An attempt is made to give a survey of the theory of spectra and spectral subspaces of group representations in an abstract Banach space setting. The theory is applied to the groups of automorphisms of operator algebras (mostly C*-algebras) and some important results of interest for mathematical physicists are proved (restrictions of the bitransposed action, spectral subspaces for the transposed action on a C*-algebra, and positive states and representations of Rsup(n)) [fr

  10. Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

    Directory of Open Access Journals (Sweden)

    Moumita Saha

    2016-01-01

    Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

  11. An efficient multiple particle filter based on the variational Bayesian approach

    KAUST Repository

    Ait-El-Fquih, Boujemaa

    2015-12-07

    This paper addresses the filtering problem in large-dimensional systems, in which conventional particle filters (PFs) remain computationally prohibitive owing to the large number of particles needed to obtain reasonable performances. To overcome this drawback, a class of multiple particle filters (MPFs) has been recently introduced in which the state-space is split into low-dimensional subspaces, and then a separate PF is applied to each subspace. In this paper, we adopt the variational Bayesian (VB) approach to propose a new MPF, the VBMPF. The proposed filter is computationally more efficient since the propagation of each particle requires generating one (new) particle only, while in the standard MPFs a set of (children) particles needs to be generated. In a numerical test, the proposed VBMPF behaves better than the PF and MPF.

  12. Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption

    Directory of Open Access Journals (Sweden)

    Zheping Yan

    2014-01-01

    Full Text Available A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.

  13. Investigation of the stochastic subspace identification method for on-line wind turbine tower monitoring

    Science.gov (United States)

    Dai, Kaoshan; Wang, Ying; Lu, Wensheng; Ren, Xiaosong; Huang, Zhenhua

    2017-04-01

    Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.

  14. Embeddings of model subspaces of the Hardy space: compactness and Schatten-von Neumann ideals

    International Nuclear Information System (INIS)

    Baranov, Anton D

    2009-01-01

    We study properties of the embedding operators of model subspaces K p Θ (defined by inner functions) in the Hardy space H p (coinvariant subspaces of the shift operator). We find a criterion for the embedding of K p Θ in L p (μ) to be compact similar to the Volberg-Treil theorem on bounded embeddings, and give a positive answer to a question of Cima and Matheson. The proof is based on Bernstein-type inequalities for functions in K p Θ . We investigate measures μ such that the embedding operator belongs to some Schatten-von Neumann ideal.

  15. Random subspaces for encryption based on a private shared Cartesian frame

    International Nuclear Information System (INIS)

    Bartlett, Stephen D.; Hayden, Patrick; Spekkens, Robert W.

    2005-01-01

    A private shared Cartesian frame is a novel form of private shared correlation that allows for both private classical and quantum communication. Cryptography using a private shared Cartesian frame has the remarkable property that asymptotically, if perfect privacy is demanded, the private classical capacity is three times the private quantum capacity. We demonstrate that if the requirement for perfect privacy is relaxed, then it is possible to use the properties of random subspaces to nearly triple the private quantum capacity, almost closing the gap between the private classical and quantum capacities

  16. High resolution through-the-wall radar image based on beamspace eigenstructure subspace methods

    Science.gov (United States)

    Yoon, Yeo-Sun; Amin, Moeness G.

    2008-04-01

    Through-the-wall imaging (TWI) is a challenging problem, even if the wall parameters and characteristics are known to the system operator. Proper target classification and correct imaging interpretation require the application of high resolution techniques using limited array size. In inverse synthetic aperture radar (ISAR), signal subspace methods such as Multiple Signal Classification (MUSIC) are used to obtain high resolution imaging. In this paper, we adopt signal subspace methods and apply them to the 2-D spectrum obtained from the delay-andsum beamforming image. This is in contrast to ISAR, where raw data, in frequency and angle, is directly used to form the estimate of the covariance matrix and array response vector. Using beams rather than raw data has two main advantages, namely, it improves the signal-to-noise ratio (SNR) and can correctly image typical indoor extended targets, such as tables and cabinets, as well as point targets. The paper presents both simulated and experimental results using synthesized and real data. It compares the performance of beam-space MUSIC and Capon beamformer. The experimental data is collected at the test facility in the Radar Imaging Laboratory, Villanova University.

  17. A generalized Schwinger boson mapping with a physical subspace

    International Nuclear Information System (INIS)

    Scholtz, F.G.; Geyer, H.B.

    1988-01-01

    We investigate the existence of a physical subspace for generalized Schwinger boson mappings of SO(2n+1) contains SO(2n) in view of previous observations by Marshalek and the recent construction of such a mapping and subspace for SO(8) by Kaup. It is shown that Kaup's construction can be attributed to the existence of a unique SO(8) automorphism. We proceed to construct a generalized Schwinger-type mapping for SO(2n+1) contains SO(2n) which, in contrast to a similar attempt by Yamamura and Nishiyama, indeed has a corresponding physical subspace. This new mapping includes in the special case of SO(8) the mapping by Kaup which is equivalent to the one given by Yamamura and Nishiyama for n=4. Nevertheless, we indicate the limitations of the generalized Schwinger mapping regarding its applicability to situations where one seeks to establish a direct link between phenomenological boson models and an underlying fermion microscopy. (orig.)

  18. Development of a Burnup Module DECBURN Based on the Krylov Subspace Method

    Energy Technology Data Exchange (ETDEWEB)

    Cho, J. Y.; Kim, K. S.; Shim, H. J.; Song, J. S

    2008-05-15

    This report is to develop a burnup module DECBURN that is essential for the reactor analysis and the assembly homogenization codes to trace the fuel composition change during the core burnup. The developed burnup module solves the burnup equation by the matrix exponential method based on the Krylov Subspace method. The final solution of the matrix exponential is obtained by the matrix scaling and squaring method. To develop DECBURN module, this report includes the followings as: (1) Krylov Subspace Method for Burnup Equation, (2) Manufacturing of the DECBURN module, (3) Library Structure Setup and Library Manufacturing, (4) Examination of the DECBURN module, (5) Implementation to the DeCART code and Verification. DECBURN library includes the decay constants, one-group cross section and the fission yields. Examination of the DECBURN module is performed by manufacturing a driver program, and the results of the DECBURN module is compared with those of the ORIGEN program. Also, the implemented DECBURN module to the DeCART code is applied to the LWR depletion benchmark and a OPR-1000 pin cell problem, and the solutions are compared with the HELIOS code to verify the computational soundness and accuracy. In this process, the criticality calculation method and the predictor-corrector scheme are introduced to the DeCART code for a function of the homogenization code. The examination by a driver program shows that the DECBURN module produces exactly the same solution with the ORIGEN program. DeCART code that equips the DECBURN module produces a compatible solution to the other codes for the LWR depletion benchmark. Also the multiplication factors of the DeCART code for the OPR-1000 pin cell problem agree to the HELIOS code within 100 pcm over the whole burnup steps. The multiplication factors with the criticality calculation are also compatible with the HELIOS code. These results mean that the developed DECBURN module works soundly and produces an accurate solution

  19. Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling.

    Science.gov (United States)

    Bo Zhao; Bilgic, Berkin; Adalsteinsson, Elfar; Griswold, Mark A; Wald, Lawrence L; Setsompop, Kawin

    2017-07-01

    Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T 1 , T 2 , and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.

  20. REVIEW APPROACHES ECONOMIC DEVELOPMENT OF THE TERRITORY OF THE ARCTIC ZONE OF THE RUSSIAN FEDERATION, PRESENTED IN THE FORM OF TARGET SUBSPACE

    Directory of Open Access Journals (Sweden)

    N. I. Didenko

    2015-01-01

    Full Text Available This paper presents a conceptual idea of the organization of management of development of the Arctic area of the Russian Federation in the form of a set of target subspace. Among the possible types of target subspace comprising the Arctic zone of the Russian Federation, allocated seven subspace: basic city mobile Camps, site production of mineral resources, recreational area, fishing area, the Northern Sea Route, infrastructure protection safe existence in the Arctic. The task of determining the most appropriate theoretical approach for the development of each target subspaces. To this end, the theoretical approaches of economic growth and development of the theory of "economic base» (Economic Base Theory; resource theory (Staple Theory; Theory sectors (Sector Theory; theory of growth poles (Growth Pole Theory; neoclassical theory (Neoclassical Growth Theory; theory of inter-regional trade (Interregional Trade Theory; theory of the commodity cycle; entrepreneurial theory (Entrepreneurship Theories.

  1. Uncertainty calculation for modal parameters used with stochastic subspace identification: an application to a bridge structure

    Science.gov (United States)

    Hsu, Wei-Ting; Loh, Chin-Hsiung; Chao, Shu-Hsien

    2015-03-01

    Stochastic subspace identification method (SSI) has been proven to be an efficient algorithm for the identification of liner-time-invariant system using multivariate measurements. Generally, the estimated modal parameters through SSI may be afflicted with statistical uncertainty, e.g. undefined measurement noises, non-stationary excitation, finite number of data samples etc. Therefore, the identified results are subjected to variance errors. Accordingly, the concept of the stabilization diagram can help users to identify the correct model, i.e. through removing the spurious modes. Modal parameters are estimated at successive model orders where the physical modes of the system are extracted and separated from the spurious modes. Besides, an uncertainty computation scheme was derived for the calculation of uncertainty bounds for modal parameters at some given model order. The uncertainty bounds of damping ratios are particularly interesting, as the estimation of damping ratios are difficult to obtain. In this paper, an automated stochastic subspace identification algorithm is addressed. First, the identification of modal parameters through covariance-driven stochastic subspace identification from the output-only measurements is used for discussion. A systematic way of investigation on the criteria for the stabilization diagram is presented. Secondly, an automated algorithm of post-processing on stabilization diagram is demonstrated. Finally, the computation of uncertainty bounds for each mode with all model order in the stabilization diagram is utilized to determine system natural frequencies and damping ratios. Demonstration of this study on the system identification of a three-span steel bridge under operation condition is presented. It is shown that the proposed new operation procedure for the automated covariance-driven stochastic subspace identification can enhance the robustness and reliability in structural health monitoring.

  2. Excluding Noise from Short Krylov Subspace Approximations to the Truncated Singular Value Decomposition (SVD)

    Science.gov (United States)

    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

  3. Performance Analysis of Blind Subspace-Based Signature Estimation Algorithms for DS-CDMA Systems with Unknown Correlated Noise

    Science.gov (United States)

    Zarifi, Keyvan; Gershman, Alex B.

    2006-12-01

    We analyze the performance of two popular blind subspace-based signature waveform estimation techniques proposed by Wang and Poor and Buzzi and Poor for direct-sequence code division multiple-access (DS-CDMA) systems with unknown correlated noise. Using the first-order perturbation theory, analytical expressions for the mean-square error (MSE) of these algorithms are derived. We also obtain simple high SNR approximations of the MSE expressions which explicitly clarify how the performance of these techniques depends on the environmental parameters and how it is related to that of the conventional techniques that are based on the standard white noise assumption. Numerical examples further verify the consistency of the obtained analytical results with simulation results.

  4. An adaptation of Krylov subspace methods to path following

    Energy Technology Data Exchange (ETDEWEB)

    Walker, H.F. [Utah State Univ., Logan, UT (United States)

    1996-12-31

    Krylov subspace methods at present constitute a very well known and highly developed class of iterative linear algebra methods. These have been effectively applied to nonlinear system solving through Newton-Krylov methods, in which Krylov subspace methods are used to solve the linear systems that characterize steps of Newton`s method (the Newton equations). Here, we will discuss the application of Krylov subspace methods to path following problems, in which the object is to track a solution curve as a parameter varies. Path following methods are typically of predictor-corrector form, in which a point near the solution curve is {open_quotes}predicted{close_quotes} by some easy but relatively inaccurate means, and then a series of Newton-like corrector iterations is used to return approximately to the curve. The analogue of the Newton equation is underdetermined, and an additional linear condition must be specified to determine corrector steps uniquely. This is typically done by requiring that the steps be orthogonal to an approximate tangent direction. Augmenting the under-determined system with this orthogonality condition in a straightforward way typically works well if direct linear algebra methods are used, but Krylov subspace methods are often ineffective with this approach. We will discuss recent work in which this orthogonality condition is imposed directly as a constraint on the corrector steps in a certain way. The means of doing this preserves problem conditioning, allows the use of preconditioners constructed for the fixed-parameter case, and has certain other advantages. Experiments on standard PDE continuation test problems indicate that this approach is effective.

  5. Subspace Arrangement Codes and Cryptosystems

    Science.gov (United States)

    2011-05-09

    Signature Date Acceptance for the Trident Scholar Committee Professor Carl E. Wick Associate Director of Midshipmen Research Signature Date SUBSPACE...Professor William Traves. I also thank Professor Carl Wick and the Trident Scholar Committee for providing me with the opportunity to conduct this... Sagan . Why the characteristic polynomial factors. Bulletin of the American Mathematical Society, 36(2):113–133, February 1999. [16] Karen E. Smith

  6. Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

    Directory of Open Access Journals (Sweden)

    Yu Ding

    2018-01-01

    Full Text Available Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.

  7. Towards automatic music transcription: note extraction based on independent subspace analysis

    Science.gov (United States)

    Wellhausen, Jens; Hoynck, Michael

    2005-01-01

    Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

  8. Krylov Subspace Methods for Complex Non-Hermitian Linear Systems. Thesis

    Science.gov (United States)

    Freund, Roland W.

    1991-01-01

    We consider Krylov subspace methods for the solution of large sparse linear systems Ax = b with complex non-Hermitian coefficient matrices. Such linear systems arise in important applications, such as inverse scattering, numerical solution of time-dependent Schrodinger equations, underwater acoustics, eddy current computations, numerical computations in quantum chromodynamics, and numerical conformal mapping. Typically, the resulting coefficient matrices A exhibit special structures, such as complex symmetry, or they are shifted Hermitian matrices. In this paper, we first describe a Krylov subspace approach with iterates defined by a quasi-minimal residual property, the QMR method, for solving general complex non-Hermitian linear systems. Then, we study special Krylov subspace methods designed for the two families of complex symmetric respectively shifted Hermitian linear systems. We also include some results concerning the obvious approach to general complex linear systems by solving equivalent real linear systems for the real and imaginary parts of x. Finally, numerical experiments for linear systems arising from the complex Helmholtz equation are reported.

  9. Krylov subspace method with communication avoiding technique for linear system obtained from electromagnetic analysis

    International Nuclear Information System (INIS)

    Ikuno, Soichiro; Chen, Gong; Yamamoto, Susumu; Itoh, Taku; Abe, Kuniyoshi; Nakamura, Hiroaki

    2016-01-01

    Krylov subspace method and the variable preconditioned Krylov subspace method with communication avoiding technique for a linear system obtained from electromagnetic analysis are numerically investigated. In the k−skip Krylov method, the inner product calculations are expanded by Krylov basis, and the inner product calculations are transformed to the scholar operations. k−skip CG method is applied for the inner-loop solver of Variable Preconditioned Krylov subspace methods, and the converged solution of electromagnetic problem is obtained using the method. (author)

  10. Subspace methods for identification of human ankle joint stiffness.

    Science.gov (United States)

    Zhao, Y; Westwick, D T; Kearney, R E

    2011-11-01

    Joint stiffness, the dynamic relationship between the angular position of a joint and the torque acting about it, describes the dynamic, mechanical behavior of a joint during posture and movement. Joint stiffness arises from both intrinsic and reflex mechanisms, but the torques due to these mechanisms cannot be measured separately experimentally, since they appear and change together. Therefore, the direct estimation of the intrinsic and reflex stiffnesses is difficult. In this paper, we present a new, two-step procedure to estimate the intrinsic and reflex components of ankle stiffness. In the first step, a discrete-time, subspace-based method is used to estimate a state-space model for overall stiffness from the measured overall torque and then predict the intrinsic and reflex torques. In the second step, continuous-time models for the intrinsic and reflex stiffnesses are estimated from the predicted intrinsic and reflex torques. Simulations and experimental results demonstrate that the algorithm estimates the intrinsic and reflex stiffnesses accurately. The new subspace-based algorithm has three advantages over previous algorithms: 1) It does not require iteration, and therefore, will always converge to an optimal solution; 2) it provides better estimates for data with high noise or short sample lengths; and 3) it provides much more accurate results for data acquired under the closed-loop conditions, that prevail when subjects interact with compliant loads.

  11. Detecting anomalies in crowded scenes via locality-constrained affine subspace coding

    Science.gov (United States)

    Fan, Yaxiang; Wen, Gongjian; Qiu, Shaohua; Li, Deren

    2017-07-01

    Video anomaly event detection is the process of finding an abnormal event deviation compared with the majority of normal or usual events. The main challenges are the high structure redundancy and the dynamic changes in the scenes that are in surveillance videos. To address these problems, we present a framework for anomaly detection and localization in videos that is based on locality-constrained affine subspace coding (LASC) and a model updating procedure. In our algorithm, LASC attempts to reconstruct the test sample by its top-k nearest subspaces, which are obtained by segmenting the normal samples space using a clustering method. A sample with a large reconstruction cost is detected as abnormal by setting a threshold. To adapt to the scene changes over time, a model updating strategy is proposed. We experiment on two public datasets: the UCSD dataset and the Avenue dataset. The results demonstrate that our method achieves competitive performance at a 700 fps on a single desktop PC.

  12. Counting Subspaces of a Finite Vector Space

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 15; Issue 11. Counting Subspaces of a Finite Vector Space – 1. Amritanshu Prasad. General Article Volume 15 Issue 11 November 2010 pp 977-987. Fulltext. Click here to view fulltext PDF. Permanent link:

  13. Visual characterization and diversity quantification of chemical libraries: 2. Analysis and selection of size-independent, subspace-specific diversity indices.

    Science.gov (United States)

    Colliandre, Lionel; Le Guilloux, Vincent; Bourg, Stephane; Morin-Allory, Luc

    2012-02-27

    High Throughput Screening (HTS) is a standard technique widely used to find hit compounds in drug discovery projects. The high costs associated with such experiments have highlighted the need to carefully design screening libraries in order to avoid wasting resources. Molecular diversity is an established concept that has been used to this end for many years. In this article, a new approach to quantify the molecular diversity of screening libraries is presented. The approach is based on the Delimited Reference Chemical Subspace (DRCS) methodology, a new method that can be used to delimit the densest subspace spanned by a reference library in a reduced 2D continuous space. A total of 22 diversity indices were implemented or adapted to this methodology, which is used here to remove outliers and obtain a relevant cell-based partition of the subspace. The behavior of these indices was assessed and compared in various extreme situations and with respect to a set of theoretical rules that a diversity function should satisfy when libraries of different sizes have to be compared. Some gold standard indices are found inappropriate in such a context, while none of the tested indices behave perfectly in all cases. Five DRCS-based indices accounting for different aspects of diversity were finally selected, and a simple framework is proposed to use them effectively. Various libraries have been profiled with respect to more specific subspaces, which further illustrate the interest of the method.

  14. Prewhitening for Rank-Deficient Noise in Subspace Methods for Noise Reduction

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Jensen, Søren Holdt

    2005-01-01

    A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, e.g., when the noise...... has narrow-band characteristics, or in the case of tonal noise. We extend the concept of prewhitening to include the case when the noise covariance matrix is rank deficient, using a weighted pseudoinverse and the quotient SVD, and we show how to formulate a general rank-reduction algorithm that works...... also for rank deficient noise. We also demonstrate how to formulate this algorithm by means of a quotient ULV decomposition, which allows for faster computation and updating. Finally we apply our algorithm to a problem involving a speech signal contaminated by narrow-band noise....

  15. An acceleration technique for 2D MOC based on Krylov subspace and domain decomposition methods

    International Nuclear Information System (INIS)

    Zhang Hongbo; Wu Hongchun; Cao Liangzhi

    2011-01-01

    Highlights: → We convert MOC into linear system solved by GMRES as an acceleration method. → We use domain decomposition method to overcome the inefficiency on large matrices. → Parallel technology is applied and a matched ray tracing system is developed. → Results show good efficiency even in large-scale and strong scattering problems. → The emphasis is that the technique is geometry-flexible. - Abstract: The method of characteristics (MOC) has great geometrical flexibility but poor computational efficiency in neutron transport calculations. The generalized minimal residual (GMRES) method, a type of Krylov subspace method, is utilized to accelerate a 2D generalized geometry characteristics solver AutoMOC. In this technique, a form of linear algebraic equation system for angular flux moments and boundary fluxes is derived to replace the conventional characteristics sweep (i.e. inner iteration) scheme, and then the GMRES method is implemented as an efficient linear system solver. This acceleration method is proved to be reliable in theory and simple for implementation. Furthermore, as introducing no restriction in geometry treatment, it is suitable for acceleration of an arbitrary geometry MOC solver. However, it is observed that the speedup decreases when the matrix becomes larger. The spatial domain decomposition method and multiprocessing parallel technology are then employed to overcome the problem. The calculation domain is partitioned into several sub-domains. For each of them, a smaller matrix is established and solved by GMRES; and the adjacent sub-domains are coupled by 'inner-edges', where the trajectory mismatches are considered adequately. Moreover, a matched ray tracing system is developed on the basis of AutoCAD, which allows a user to define the sub-domains on demand conveniently. Numerical results demonstrate that the acceleration techniques are efficient without loss of accuracy, even in the case of large-scale and strong scattering

  16. Application of Earthquake Subspace Detectors at Kilauea and Mauna Loa Volcanoes, Hawai`i

    Science.gov (United States)

    Okubo, P.; Benz, H.; Yeck, W.

    2016-12-01

    Recent studies have demonstrated the capabilities of earthquake subspace detectors for detailed cataloging and tracking of seismicity in a number of regions and settings. We are exploring the application of subspace detectors at the United States Geological Survey's Hawaiian Volcano Observatory (HVO) to analyze seismicity at Kilauea and Mauna Loa volcanoes. Elevated levels of microseismicity and occasional swarms of earthquakes associated with active volcanism here present cataloging challenges due the sheer numbers of earthquakes and an intrinsically low signal-to-noise environment featuring oceanic microseism and volcanic tremor in the ambient seismic background. With high-quality continuous recording of seismic data at HVO, we apply subspace detectors (Harris and Dodge, 2011, Bull. Seismol. Soc. Am., doi: 10.1785/0120100103) during intervals of noteworthy seismicity. Waveform templates are drawn from Magnitude 2 and larger earthquakes within clusters of earthquakes cataloged in the HVO seismic database. At Kilauea, we focus on seismic swarms in the summit caldera region where, despite continuing eruptions from vents in the summit region and in the east rift zone, geodetic measurements reflect a relatively inflated volcanic state. We also focus on seismicity beneath and adjacent to Mauna Loa's summit caldera that appears to be associated with geodetic expressions of gradual volcanic inflation, and where precursory seismicity clustered prior to both Mauna Loa's most recent eruptions in 1975 and 1984. We recover several times more earthquakes with the subspace detectors - down to roughly 2 magnitude units below the templates, based on relative amplitudes - compared to the numbers of cataloged earthquakes. The increased numbers of detected earthquakes in these clusters, and the ability to associate and locate them, allow us to infer details of the spatial and temporal distributions and possible variations in stresses within these key regions of the volcanoes.

  17. Supervised orthogonal discriminant subspace projects learning for face recognition.

    Science.gov (United States)

    Chen, Yu; Xu, Xiao-Hong

    2014-02-01

    In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Outlier Ranking via Subspace Analysis in Multiple Views of the Data

    DEFF Research Database (Denmark)

    Muller, Emmanuel; Assent, Ira; Iglesias, Patricia

    2012-01-01

    , a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its...... principled integration of multiple views into an outlierness measure uncovers outliers that are not detectable in the full attribute space. Our experimental evaluation demonstrates that Outrank successfully determines a high quality outlier ranking, and outperforms state-of-the-art outlierness measures....

  19. Quantum Zeno subspaces induced by temperature

    Energy Technology Data Exchange (ETDEWEB)

    Militello, B.; Scala, M.; Messina, A. [Dipartimento di Fisica dell' Universita di Palermo, Via Archirafi 36, I-90123 Palermo (Italy)

    2011-08-15

    We discuss the partitioning of the Hilbert space of a quantum system induced by the interaction with another system at thermal equilibrium, showing that the higher the temperature the more effective is the formation of Zeno subspaces. We show that our analysis keeps its validity even in the case of interaction with a bosonic reservoir, provided appropriate limitations of the relevant bandwidth.

  20. ASCS online fault detection and isolation based on an improved MPCA

    Science.gov (United States)

    Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

    2014-09-01

    Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

  1. Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods

    Science.gov (United States)

    Wang, C. L.; Funk, L. L.; Riedel, R. A.; Berry, K. D.

    2017-05-01

    3He gas based neutron Linear-Position-Sensitive Detectors (LPSDs) have been used for many neutron scattering instruments. Traditional Pulse-height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (NGD ratio) on the order of 105-106. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher Linear Discriminant Analysis (FLDA) and three Multivariate Analyses (MVAs) of the features were performed. The NGD ratios are improved by about 102-103 times compared with the traditional PHA method. Our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.

  2. Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

    Science.gov (United States)

    Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

    2014-05-01

    This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  3. An efficient method for sampling the essential subspace of proteins

    NARCIS (Netherlands)

    Amadei, A; Linssen, A.B M; de Groot, B.L.; van Aalten, D.M.F.; Berendsen, H.J.C.

    A method is presented for a more efficient sampling of the configurational space of proteins as compared to conventional sampling techniques such as molecular dynamics. The method is based on the large conformational changes in proteins revealed by the ''essential dynamics'' analysis. A form of

  4. Subspace in Linear Algebra: Investigating Students' Concept Images and Interactions with the Formal Definition

    Science.gov (United States)

    Wawro, Megan; Sweeney, George F.; Rabin, Jeffrey M.

    2011-01-01

    This paper reports on a study investigating students' ways of conceptualizing key ideas in linear algebra, with the particular results presented here focusing on student interactions with the notion of subspace. In interviews conducted with eight undergraduates, we found students' initial descriptions of subspace often varied substantially from…

  5. Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

    Directory of Open Access Journals (Sweden)

    Liying Yang

    2016-01-01

    Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

  6. Extended Krylov subspaces approximations of matrix functions. Application to computational electromagnetics

    Energy Technology Data Exchange (ETDEWEB)

    Druskin, V.; Lee, Ping [Schlumberger-Doll Research, Ridgefield, CT (United States); Knizhnerman, L. [Central Geophysical Expedition, Moscow (Russian Federation)

    1996-12-31

    There is now a growing interest in the area of using Krylov subspace approximations to compute the actions of matrix functions. The main application of this approach is the solution of ODE systems, obtained after discretization of partial differential equations by method of lines. In the event that the cost of computing the matrix inverse is relatively inexpensive, it is sometimes attractive to solve the ODE using the extended Krylov subspaces, originated by actions of both positive and negative matrix powers. Examples of such problems can be found frequently in computational electromagnetics.

  7. A block Krylov subspace time-exact solution method for linear ordinary differential equation systems

    NARCIS (Netherlands)

    Bochev, Mikhail A.

    2013-01-01

    We propose a time-exact Krylov-subspace-based method for solving linear ordinary differential equation systems of the form $y'=-Ay+g(t)$ and $y"=-Ay+g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial approximation of

  8. Subspace identification of distributed clusters of homogeneous systems

    NARCIS (Netherlands)

    Yu, C.; Verhaegen, M.H.G.

    2017-01-01

    This note studies the identification of a network comprised of interconnected clusters of LTI systems. Each cluster consists of homogeneous dynamical systems, and its interconnections with the rest of the network are unmeasurable. A subspace identification method is proposed for identifying a single

  9. Similarity measurement method of high-dimensional data based on normalized net lattice subspace

    Institute of Scientific and Technical Information of China (English)

    Li Wenfa; Wang Gongming; Li Ke; Huang Su

    2017-01-01

    The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity, leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals, and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this meth-od, three data types are used, and seven common similarity measurement methods are compared. The experimental result indicates that the relative difference of the method is increasing with the di-mensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition, the similarity range of this method in different dimensions is [0, 1], which is fit for similarity analysis after dimensionality reduction.

  10. A new efficient method for the calculation of interior eigenpairs and its application to vibrational structure problems

    Science.gov (United States)

    Petrenko, Taras; Rauhut, Guntram

    2017-03-01

    Vibrational configuration interaction theory is a common method for calculating vibrational levels and associated IR and Raman spectra of small and medium-sized molecules. When combined with appropriate configuration selection procedures, the method allows the treatment of configuration spaces with up to 1010 configurations. In general, this approach pursues the construction of the eigenstates with significant contributions of physically relevant configurations. The corresponding eigenfunctions are evaluated in the subspace of selected configurations. However, it can easily reach the dimension which is not tractable for conventional eigenvalue solvers. Although Davidson and Lanczos methods are the methods of choice for calculating exterior eigenvalues, they usually fall into stagnation when applied to interior states. The latter are commonly treated by the Jacobi-Davidson method. This approach in conjunction with matrix factorization for solving the correction equation (CE) is prohibitive for larger problems, and it has limited efficiency if the solution of the CE is based on Krylov's subspace algorithms. We propose an iterative subspace method that targets the eigenvectors with significant contributions to a given reference vector and is based on the optimality condition for the residual norm corresponding to the error in the solution vector. The subspace extraction and expansion are modified according to these principles which allow very efficient calculation of interior vibrational states with a strong multireference character in different vibrational structure problems. The convergence behavior of the method and its performance in comparison with the aforementioned algorithms are investigated in a set of benchmark calculations.

  11. Reachable Distance Space: Efficient Sampling-Based Planning for Spatially Constrained Systems

    KAUST Repository

    Xinyu Tang,; Thomas, S.; Coleman, P.; Amato, N. M.

    2010-01-01

    reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the number of the robot's degrees of freedom

  12. Single and multiple object tracking using log-euclidean Riemannian subspace and block-division appearance model.

    Science.gov (United States)

    Hu, Weiming; Li, Xi; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen; Zhang, Zhongfei

    2012-12-01

    Object appearance modeling is crucial for tracking objects, especially in videos captured by nonstationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-euclidean block-division appearance model which captures both the global and local spatial layout information about object appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-euclidean block-division appearance model captures changes in object appearance. For multi-object tracking, the appearance models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.

  13. Persymmetric Adaptive Detectors of Subspace Signals in Homogeneous and Partially Homogeneous Clutter

    Directory of Open Access Journals (Sweden)

    Ding Hao

    2015-08-01

    Full Text Available In the field of adaptive radar detection, an effective strategy to improve the detection performance is to exploit the structural information of the covariance matrix, especially in the case of insufficient reference cells. Thus, in this study, the problem of detecting multidimensional subspace signals is discussed by considering the persymmetric structure of the clutter covariance matrix, which implies that the covariance matrix is persymmetric about its cross diagonal. Persymmetric adaptive detectors are derived on the basis of the one-step principle as well as the two-step Generalized Likelihood Ratio Test (GLRT in homogeneous and partially homogeneous clutter. The proposed detectors consider the structural information of the covariance matrix at the design stage. Simulation results suggest performance improvement compared with existing detectors when reference cells are insufficient. Moreover, the detection performance is assessed with respect to the effects of the covariance matrix, signal subspace dimension, and mismatched performance of signal subspace as well as signal fluctuations.

  14. A Variational Approach to Video Registration with Subspace Constraints.

    Science.gov (United States)

    Garg, Ravi; Roussos, Anastasios; Agapito, Lourdes

    2013-01-01

    This paper addresses the problem of non-rigid video registration, or the computation of optical flow from a reference frame to each of the subsequent images in a sequence, when the camera views deformable objects. We exploit the high correlation between 2D trajectories of different points on the same non-rigid surface by assuming that the displacement of any point throughout the sequence can be expressed in a compact way as a linear combination of a low-rank motion basis. This subspace constraint effectively acts as a trajectory regularization term leading to temporally consistent optical flow. We formulate it as a robust soft constraint within a variational framework by penalizing flow fields that lie outside the low-rank manifold. The resulting energy functional can be decoupled into the optimization of the brightness constancy and spatial regularization terms, leading to an efficient optimization scheme. Additionally, we propose a novel optimization scheme for the case of vector valued images, based on the dualization of the data term. This allows us to extend our approach to deal with colour images which results in significant improvements on the registration results. Finally, we provide a new benchmark dataset, based on motion capture data of a flag waving in the wind, with dense ground truth optical flow for evaluation of multi-frame optical flow algorithms for non-rigid surfaces. Our experiments show that our proposed approach outperforms state of the art optical flow and dense non-rigid registration algorithms.

  15. Experimental fault-tolerant quantum cryptography in a decoherence-free subspace

    International Nuclear Information System (INIS)

    Zhang Qiang; Pan Jianwei; Yin Juan; Chen Tengyun; Lu Shan; Zhang Jun; Li Xiaoqiang; Yang Tao; Wang Xiangbin

    2006-01-01

    We experimentally implement a fault-tolerant quantum key distribution protocol with two photons in a decoherence-free subspace [Phys. Rev. A 72, 050304(R) (2005)]. It is demonstrated that our protocol can yield a good key rate even with a large bit-flip error rate caused by collective rotation, while the usual realization of the Bennett-Brassard 1984 protocol cannot produce any secure final key given the same channel. Since the experiment is performed in polarization space and does not need the calibration of a reference frame, important applications in free-space quantum communication are expected. Moreover, our method can also be used to robustly transmit an arbitrary two-level quantum state in a type of decoherence-free subspace

  16. MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING

    Data.gov (United States)

    National Aeronautics and Space Administration — MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract....

  17. Review on Microwave-Matter Interaction Fundamentals and Efficient Microwave-Associated Heating Strategies

    Science.gov (United States)

    Sun, Jing; Wang, Wenlong; Yue, Qinyan

    2016-01-01

    Microwave heating is rapidly emerging as an effective and efficient tool in various technological and scientific fields. A comprehensive understanding of the fundamentals of microwave–matter interactions is the precondition for better utilization of microwave technology. However, microwave heating is usually only known as dielectric heating, and the contribution of the magnetic field component of microwaves is often ignored, which, in fact, contributes greatly to microwave heating of some aqueous electrolyte solutions, magnetic dielectric materials and certain conductive powder materials, etc. This paper focuses on this point and presents a careful review of microwave heating mechanisms in a comprehensive manner. Moreover, in addition to the acknowledged conventional microwave heating mechanisms, the special interaction mechanisms between microwave and metal-based materials are attracting increasing interest for a variety of metallurgical, plasma and discharge applications, and therefore are reviewed particularly regarding the aspects of the reflection, heating and discharge effects. Finally, several distinct strategies to improve microwave energy utilization efficiencies are proposed and discussed with the aim of tackling the energy-efficiency-related issues arising from the application of microwave heating. This work can present a strategic guideline for the developed understanding and utilization of the microwave heating technology. PMID:28773355

  18. Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.

    Science.gov (United States)

    Brunton, Steven L; Brunton, Bingni W; Proctor, Joshua L; Kutz, J Nathan

    2016-01-01

    In this wIn this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control.ork, we explore finite

  19. Evaluating Clustering in Subspace Projections of High Dimensional Data

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Günnemann, Stephan; Assent, Ira

    2009-01-01

    Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation...... and comparison between these paradigms on a common basis. Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects...... of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention. In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering...

  20. Heterogeneous photocatalysis for air and water treatment: Fundamental needs for quantum efficiency enhancement

    Energy Technology Data Exchange (ETDEWEB)

    Ollis, D.F. [North Carolina State Univ., Raleigh, NC (United States)

    1996-09-01

    In the remediation industries, a useful treatment technology must be {open_quotes}general, robust, and cheap{close_quotes}. Among oxidation processes, heterogeneous photocatalysis is now broadly demonstrated to destroy common water and air contaminants. The potential process uses of highly stable titania, long lived lamps (one year), and room temperature operation, indicating a simple and robust process. We are left to address the third criterion: Can photocatalysis be {open_quotes}cheap{close_quotes}? In both liquid phase and gas phase treatment and purification by photocatalysis, it is established that the primary barrier to commercialization is often cost. Cost in return is dominated by the efficiency with which solar or lamp photons are harvested for productive light, and subsequent dark, reactions. This paper therefore defines fundamental needs in photocatalysis for pollution control in terms of activities which could lead to quantum efficiency enhancement. We first recall three related definitions. The quantum yield (QY) is the ratio of molecules of reactant converted per photon absorbed, a fundamental quantity. A less fundamental, but more easily measured variable is the quantum efficiency (QE), the ratio of molecules converted per photon entering the reactor. A third variable is the energy required per order of magnitude pollutant reduction, or EEO, a definition which provides for easy energy cost comparisons among different technologies. Each measure cited here reflects the photon, and thus the electrical, cost of this photochemistry.

  1. Fundamental understanding and development of low-cost, high-efficiency silicon solar cells

    Energy Technology Data Exchange (ETDEWEB)

    ROHATGI,A.; NARASIMHA,S.; MOSCHER,J.; EBONG,A.; KAMRA,S.; KRYGOWSKI,T.; DOSHI,P.; RISTOW,A.; YELUNDUR,V.; RUBY,DOUGLAS S.

    2000-05-01

    The overall objectives of this program are (1) to develop rapid and low-cost processes for manufacturing that can improve yield, throughput, and performance of silicon photovoltaic devices, (2) to design and fabricate high-efficiency solar cells on promising low-cost materials, and (3) to improve the fundamental understanding of advanced photovoltaic devices. Several rapid and potentially low-cost technologies are described in this report that were developed and applied toward the fabrication of high-efficiency silicon solar cells.

  2. Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality

    Directory of Open Access Journals (Sweden)

    Carlos A. L. Pires

    2017-01-01

    Full Text Available We propose an expansion of multivariate time-series data into maximally independent source subspaces. The search is made among rotations of prewhitened data which maximize non-Gaussianity of candidate sources. We use a tensorial invariant approximation of the multivariate negentropy in terms of a linear combination of squared coskewness and cokurtosis. By solving a high-order singular value decomposition problem, we extract the axes associated with most non-Gaussianity. Moreover, an estimate of the Gaussian subspace is provided by the trailing singular vectors. The independent subspaces are obtained through the search of “quasi-independent” components within the estimated non-Gaussian subspace, followed by the identification of groups with significant joint negentropies. Sources result essentially from the coherency of extremes of the data components. The method is then applied to the global sea surface temperature anomalies, equatorward of 65°, after being tested with non-Gaussian surrogates consistent with the data anomalies. The main emerging independent components and subspaces, supposedly generated by independent forcing, include different variability modes, namely, The East-Pacific, the Central Pacific, and the Atlantic Niños, the Atlantic Multidecadal Oscillation, along with the subtropical dipoles in the Indian, South Pacific, and South-Atlantic oceans. Benefits and usefulness of independent subspaces are then discussed.

  3. Fast and Statistically Efficient Fundamental Frequency Estimation

    DEFF Research Database (Denmark)

    Nielsen, Jesper Kjær; Jensen, Tobias Lindstrøm; Jensen, Jesper Rindom

    2016-01-01

    Fundamental frequency estimation is a very important task in many applications involving periodic signals. For computational reasons, fast autocorrelation-based estimation methods are often used despite parametric estimation methods having superior estimation accuracy. However, these parametric...... a recursive solver. Via benchmarks, we demonstrate that the computation time is reduced by approximately two orders of magnitude. The proposed fast algorithm is available for download online....

  4. Central subspace dimensionality reduction using covariance operators.

    Science.gov (United States)

    Kim, Minyoung; Pavlovic, Vladimir

    2011-04-01

    We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets. A class of DRR methods exploits the notion of inverse regression (IR) to discover central subspaces. Whereas most existing IR techniques rely on explicit output space slicing, we propose a novel method called the Covariance Operator Inverse Regression (COIR) that generalizes IR to nonlinear input/output spaces without explicit target slicing. COIR's unique properties make DRR applicable to problem domains with high-dimensional output data corrupted by potentially significant amounts of noise. Unlike recent kernel dimensionality reduction methods that employ iterative nonconvex optimization, COIR yields a closed-form solution. We also establish the link between COIR, other DRR techniques, and popular supervised dimensionality reduction methods, including canonical correlation analysis and linear discriminant analysis. We then extend COIR to semi-supervised settings where many of the input points lack their labels. We demonstrate the benefits of COIR on several important regression problems in both fully supervised and semi-supervised settings.

  5. Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control

    Science.gov (United States)

    Brunton, Steven L.; Brunton, Bingni W.; Proctor, Joshua L.; Kutz, J. Nathan

    2016-01-01

    In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control. PMID:26919740

  6. Active Subspace Methods for Data-Intensive Inverse Problems

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Qiqi [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

    2017-04-27

    The project has developed theory and computational tools to exploit active subspaces to reduce the dimension in statistical calibration problems. This dimension reduction enables MCMC methods to calibrate otherwise intractable models. The same theoretical and computational tools can also reduce the measurement dimension for calibration problems that use large stores of data.

  7. Comparison Study of Subspace Identification Methods Applied to Flexible Structures

    Science.gov (United States)

    Abdelghani, M.; Verhaegen, M.; Van Overschee, P.; De Moor, B.

    1998-09-01

    In the past few years, various time domain methods for identifying dynamic models of mechanical structures from modal experimental data have appeared. Much attention has been given recently to so-called subspace methods for identifying state space models. This paper presents a detailed comparison study of these subspace identification methods: the eigensystem realisation algorithm with observer/Kalman filter Markov parameters computed from input/output data (ERA/OM), the robust version of the numerical algorithm for subspace system identification (N4SID), and a refined version of the past outputs scheme of the multiple-output error state space (MOESP) family of algorithms. The comparison is performed by simulating experimental data using the five mode reduced model of the NASA Mini-Mast structure. The general conclusion is that for the case of white noise excitations as well as coloured noise excitations, the N4SID/MOESP algorithms perform equally well but give better results (improved transfer function estimates, improved estimates of the output) compared to the ERA/OM algorithm. The key computational step in the three algorithms is the approximation of the extended observability matrix of the system to be identified, for N4SID/MOESP, or of the observer for the system to be identified, for the ERA/OM. Furthermore, the three algorithms only require the specification of one dimensioning parameter.

  8. Time-domain simulations for metallic nano-structures - a Krylov-subspace approach beyond the limitations of FDTD

    Energy Technology Data Exchange (ETDEWEB)

    Koenig, Michael [Institut fuer Theoretische Festkoerperphysik, Universitaet Karlsruhe (Germany); Karlsruhe School of Optics and Photonics (KSOP), Universitaet Karlsruhe (Germany); Niegemann, Jens; Tkeshelashvili, Lasha; Busch, Kurt [Institut fuer Theoretische Festkoerperphysik, Universitaet Karlsruhe (Germany); DFG Forschungszentrum Center for Functional Nanostructures (CFN), Universitaet Karlsruhe (Germany); Karlsruhe School of Optics and Photonics (KSOP), Universitaet Karlsruhe (Germany)

    2008-07-01

    Numerical simulations of metallic nano-structures are crucial for the efficient design of plasmonic devices. Conventional time-domain solvers such as FDTD introduce large numerical errors especially at metallic surfaces. Our approach combines a discontinuous Galerkin method on an adaptive mesh for the spatial discretisation with a Krylov-subspace technique for the time-stepping procedure. Thus, the higher-order accuracy in both time and space is supported by unconditional stability. As illustrative examples, we compare numerical results obtained with our method against analytical reference solutions and results from FDTD calculations.

  9. Speech Denoising in White Noise Based on Signal Subspace Low-rank Plus Sparse Decomposition

    Directory of Open Access Journals (Sweden)

    yuan Shuai

    2017-01-01

    Full Text Available In this paper, a new subspace speech enhancement method using low-rank and sparse decomposition is presented. In the proposed method, we firstly structure the corrupted data as a Toeplitz matrix and estimate its effective rank for the underlying human speech signal. Then the low-rank and sparse decomposition is performed with the guidance of speech rank value to remove the noise. Extensive experiments have been carried out in white Gaussian noise condition, and experimental results show the proposed method performs better than conventional speech enhancement methods, in terms of yielding less residual noise and lower speech distortion.

  10. Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

    Science.gov (United States)

    Ren, W. X.; Lin, Y. Q.; Fang, S. E.

    2011-11-01

    One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

  11. Quantum theory of dynamical collective subspace for large-amplitude collective motion

    International Nuclear Information System (INIS)

    Sakata, Fumihiko; Marumori, Toshio; Ogura, Masanori.

    1986-03-01

    By placing emphasis on conceptual correspondence to the ''classical'' theory which has been developed within the framework of the time-dependent Hartree-Fock theory, a full quantum theory appropriate for describing large-amplitude collective motion is proposed. A central problem of the quantum theory is how to determine an optimal representation called a dynamical representation; the representation is specific for the collective subspace where the large-amplitude collective motion is replicated as satisfactorily as possible. As an extension of the classical theory where the concept of an approximate integral surface plays an important role, the dynamical representation is properly characterized by introducing a concept of an approximate invariant subspace of the Hamiltonian. (author)

  12. Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization

    KAUST Repository

    Fornasier, Massimo; Schö nlieb, Carola-Bibiane

    2009-01-01

    This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a seminorm for a subspace. The optimization is realized by alternating minimizations of the functional on a

  13. Breaking of separability condition for dynamical collective subspace; Onset of quantum chaos in large-amplitude collective motion

    Energy Technology Data Exchange (ETDEWEB)

    Sakata, Fumihiko [Tokyo Univ., Tanashi (Japan). Inst. for Nuclear Study; Yamamoto, Yoshifumi; Marumori, Toshio; Iida, Shinji; Tsukuma, Hidehiko

    1989-11-01

    It is the purpose of the present paper to study 'global structure' of the state space of an N-body interacting fermion system, which exhibits regular, transient and stochastic phases depending on strength of the interaction. An optimum representation called a dynamical representation plays an essential role in this investigation. The concept of the dynamical representation has been introduced in the quantum theory of dynamical subspace in our previous paper, in order to determine self-consistently an optimum collective subspace as well as an optimum collective Hamiltonian. In the theory, furthermore, dynamical conditions called separability and stability conditions have been provided in order to identify the optimum collective subspace as an approximate invariant subspace of the Hamiltonian. Physical meaning of these conditions are clarified from a viewpoint to relate breaking of them with bifurcation of the collectivity and an onset of quantum chaos from the regular collective motion, by illustrating the general idea with numerical results obtained for a simple soluble model. It turns out that the onset of the stochastic phase is associated with dissolution of the quantum numbers to specify the collective subspace and this dissolution is induced by the breaking of the separability condition in the dynamical representation. (author).

  14. Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints.

    Science.gov (United States)

    Chiew, Mark; Graedel, Nadine N; Miller, Karla L

    2018-07-01

    Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis. We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R = 16 in a radial-Cartesian acquisition. We show that this approach works with approximate, or not perfectly informative constraints, where the derived benefit is commensurate with the information content contained in the constraints. The proposed method extends low-rank approximation methods for under-sampled fMRI data acquisition by leveraging knowledge of expected task-based variance in the data, enabling improvements in the speed and efficiency of fMRI data acquisition without the loss of subtle features. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  15. A Comparative Study for Orthogonal Subspace Projection and Constrained Energy Minimization

    National Research Council Canada - National Science Library

    Du, Qian; Ren, Hsuan; Chang, Chein-I

    2003-01-01

    ...: orthogonal subspace projection (OSP) and constrained energy minimization (CEM). It is shown that they are closely related and essentially equivalent provided that the noise is white with large SNR...

  16. Fast regularizing sequential subspace optimization in Banach spaces

    International Nuclear Information System (INIS)

    Schöpfer, F; Schuster, T

    2009-01-01

    We are concerned with fast computations of regularized solutions of linear operator equations in Banach spaces in case only noisy data are available. To this end we modify recently developed sequential subspace optimization methods in such a way that the therein employed Bregman projections onto hyperplanes are replaced by Bregman projections onto stripes whose width is in the order of the noise level

  17. Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

    Directory of Open Access Journals (Sweden)

    Alim Samat

    2016-03-01

    Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.

  18. A Kalman-based Fundamental Frequency Estimation Algorithm

    DEFF Research Database (Denmark)

    Shi, Liming; Nielsen, Jesper Kjær; Jensen, Jesper Rindom

    2017-01-01

    Fundamental frequency estimation is an important task in speech and audio analysis. Harmonic model-based methods typically have superior estimation accuracy. However, such methods usually as- sume that the fundamental frequency and amplitudes are station- ary over a short time frame. In this pape...

  19. Recursive subspace identification for in flight modal analysis of airplanes

    OpenAIRE

    De Cock , Katrien; Mercère , Guillaume; De Moor , Bart

    2006-01-01

    International audience; In this paper recursive subspace identification algorithms are applied to track the modal parameters of airplanes on-line during test flights. The ability to track changes in the damping ratios and the influence of the forgetting factor are studied through simulations.

  20. Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval.

    Science.gov (United States)

    Li, Ke; Pang, Kaiyue; Song, Yi-Zhe; Hospedales, Timothy M; Xiang, Tao; Zhang, Honggang

    2017-08-25

    We study the problem of fine-grained sketch-based image retrieval. By performing instance-level (rather than category-level) retrieval, it embodies a timely and practical application, particularly with the ubiquitous availability of touchscreens. Three factors contribute to the challenging nature of the problem: (i) free-hand sketches are inherently abstract and iconic, making visual comparisons with photos difficult, (ii) sketches and photos are in two different visual domains, i.e. black and white lines vs. color pixels, and (iii) fine-grained distinctions are especially challenging when executed across domain and abstraction-level. To address these challenges, we propose to bridge the image-sketch gap both at the high-level via parts and attributes, as well as at the low-level, via introducing a new domain alignment method. More specifically, (i) we contribute a dataset with 304 photos and 912 sketches, where each sketch and image is annotated with its semantic parts and associated part-level attributes. With the help of this dataset, we investigate (ii) how strongly-supervised deformable part-based models can be learned that subsequently enable automatic detection of part-level attributes, and provide pose-aligned sketch-image comparisons. To reduce the sketch-image gap when comparing low-level features, we also (iii) propose a novel method for instance-level domain-alignment, that exploits both subspace and instance-level cues to better align the domains. Finally (iv) these are combined in a matching framework integrating aligned low-level features, mid-level geometric structure and high-level semantic attributes. Extensive experiments conducted on our new dataset demonstrate effectiveness of the proposed method.

  1. Concept of a collective subspace associated with the invariance principle of the Schroedinger equation

    International Nuclear Information System (INIS)

    Marumori, Toshio; Hayashi, Akihisa; Tomoda, Toshiaki; Kuriyama, Atsushi; Maskawa, Toshihide

    1980-01-01

    The aim of this series of papers is to propose a microscopic theory to go beyond the situations where collective motions are described by the random phase approximation, i.e., by small amplitude harmonic oscillations about equilibrium. The theory is thus appropriate for the microscopic description of the large amplitude collective motion of soft nuclei. The essential idea is to develop a method to determine the collective subspace (or submanifold) in the many-particle Hilbert space in an optimal way, on the basis of a fundamental principle called the invariance principle of the Schroedinger equation. By using the principle within the framework of the Hartree-Fock theory, it is shown that the theory can clarify the structure of the so-called ''phonon-bands'' by self-consistently deriving the collective Hamiltonian where the number of the ''physical phonon'' is conserved. The purpose of this paper is not to go into detailed quantitative discussion, but rather to develop the basic idea. (author)

  2. Parallel algorithms for unconstrained optimization by multisplitting with inexact subspace search - the abstract

    Energy Technology Data Exchange (ETDEWEB)

    Renaut, R.; He, Q. [Arizona State Univ., Tempe, AZ (United States)

    1994-12-31

    In a new parallel iterative algorithm for unconstrained optimization by multisplitting is proposed. In this algorithm the original problem is split into a set of small optimization subproblems which are solved using well known sequential algorithms. These algorithms are iterative in nature, e.g. DFP variable metric method. Here the authors use sequential algorithms based on an inexact subspace search, which is an extension to the usual idea of an inexact fine search. Essentially the idea of the inexact line search for nonlinear minimization is that at each iteration the authors only find an approximate minimum in the line search direction. Hence by inexact subspace search, they mean that, instead of finding the minimum of the subproblem at each interation, they do an incomplete down hill search to give an approximate minimum. Some convergence and numerical results for this algorithm will be presented. Further, the original theory will be generalized to the situation with a singular Hessian. Applications for nonlinear least squares problems will be presented. Experimental results will be presented for implementations on an Intel iPSC/860 Hypercube with 64 nodes as well as on the Intel Paragon.

  3. Quantum cloning of mixed states in symmetric subspaces

    International Nuclear Information System (INIS)

    Fan Heng

    2003-01-01

    Quantum-cloning machine for arbitrary mixed states in symmetric subspaces is proposed. This quantum-cloning machine can be used to copy part of the output state of another quantum-cloning machine and is useful in quantum computation and quantum information. The shrinking factor of this quantum cloning achieves the well-known upper bound. When the input is identical pure states, two different fidelities of this cloning machine are optimal

  4. A unified classifier for robust face recognition based on combining multiple subspace algorithms

    Science.gov (United States)

    Ijaz Bajwa, Usama; Ahmad Taj, Imtiaz; Waqas Anwar, Muhammad

    2012-10-01

    Face recognition being the fastest growing biometric technology has expanded manifold in the last few years. Various new algorithms and commercial systems have been proposed and developed. However, none of the proposed or developed algorithm is a complete solution because it may work very well on one set of images with say illumination changes but may not work properly on another set of image variations like expression variations. This study is motivated by the fact that any single classifier cannot claim to show generally better performance against all facial image variations. To overcome this shortcoming and achieve generality, combining several classifiers using various strategies has been studied extensively also incorporating the question of suitability of any classifier for this task. The study is based on the outcome of a comprehensive comparative analysis conducted on a combination of six subspace extraction algorithms and four distance metrics on three facial databases. The analysis leads to the selection of the most suitable classifiers which performs better on one task or the other. These classifiers are then combined together onto an ensemble classifier by two different strategies of weighted sum and re-ranking. The results of the ensemble classifier show that these strategies can be effectively used to construct a single classifier that can successfully handle varying facial image conditions of illumination, aging and facial expressions.

  5. Von Neumann algebras as complemented subspaces of B(H)

    DEFF Research Database (Denmark)

    Christensen, Erik; Wang, Liguang

    2014-01-01

    Let M be a von Neumann algebra of type II1 which is also a complemented subspace of B( H). We establish an algebraic criterion, which ensures that M is an injective von Neumann algebra. As a corollary we show that if M is a complemented factor of type II1 on a Hilbert space H, then M is injective...

  6. Fundamental Aeronautics Program: Overview of Project Work in Supersonic Cruise Efficiency

    Science.gov (United States)

    Castner, Raymond

    2011-01-01

    The Supersonics Project, part of NASA?s Fundamental Aeronautics Program, contains a number of technical challenge areas which include sonic boom community response, airport noise, high altitude emissions, cruise efficiency, light weight durable engines/airframes, and integrated multi-discipline system design. This presentation provides an overview of the current (2011) activities in the supersonic cruise efficiency technical challenge, and is focused specifically on propulsion technologies. The intent is to develop and validate high-performance supersonic inlet and nozzle technologies. Additional work is planned for design and analysis tools for highly-integrated low-noise, low-boom applications. If successful, the payoffs include improved technologies and tools for optimized propulsion systems, propulsion technologies for a minimized sonic boom signature, and a balanced approach to meeting efficiency and community noise goals. In this propulsion area, the work is divided into advanced supersonic inlet concepts, advanced supersonic nozzle concepts, low fidelity computational tool development, high fidelity computational tools, and improved sensors and measurement capability. The current work in each area is summarized.

  7. On the Kalman Filter error covariance collapse into the unstable subspace

    Directory of Open Access Journals (Sweden)

    A. Trevisan

    2011-03-01

    Full Text Available When the Extended Kalman Filter is applied to a chaotic system, the rank of the error covariance matrices, after a sufficiently large number of iterations, reduces to N+ + N0 where N+ and N0 are the number of positive and null Lyapunov exponents. This is due to the collapse into the unstable and neutral tangent subspace of the solution of the full Extended Kalman Filter. Therefore the solution is the same as the solution obtained by confining the assimilation to the space spanned by the Lyapunov vectors with non-negative Lyapunov exponents. Theoretical arguments and numerical verification are provided to show that the asymptotic state and covariance estimates of the full EKF and of its reduced form, with assimilation in the unstable and neutral subspace (EKF-AUS are the same. The consequences of these findings on applications of Kalman type Filters to chaotic models are discussed.

  8. Highly efficient 400  W near-fundamental-mode green thin-disk laser.

    Science.gov (United States)

    Piehler, Stefan; Dietrich, Tom; Rumpel, Martin; Graf, Thomas; Ahmed, Marwan Abdou

    2016-01-01

    We report on the efficient generation of continuous-wave, high-brightness green laser radiation. Green lasers are particularly interesting for reliable and reproducible deep-penetration welding of copper or for pumping Ti:Sa oscillators. By intracavity second-harmonic generation in a thin-disk laser resonator designed for fundamental-mode operation, an output power of up to 403 W is demonstrated at a wavelength of 515 nm with almost diffraction-limited beam quality. The unprecedented optical efficiency of 40.7% of green output power with respect to the pump power of the thin-disk laser is enabled by the intracavity use of a highly efficient grating waveguide mirror, which combines the functions of wavelength stabilization and spectral narrowing, as well as polarization selection in a single element.

  9. Margin-Wide Earthquake Subspace Scanning Along the Cascadia Subduction Zone Using the Cascadia Initiative Amphibious Dataset

    Science.gov (United States)

    Morton, E.; Bilek, S. L.; Rowe, C. A.

    2017-12-01

    Understanding the spatial extent and behavior of the interplate contact in the Cascadia Subduction Zone (CSZ) may prove pivotal to preparation for future great earthquakes, such as the M9 event of 1700. Current and historic seismic catalogs are limited in their integrity by their short duration, given the recurrence rate of great earthquakes, and by their rather high magnitude of completeness for the interplate seismic zone, due to its offshore distance from these land-based networks. This issue is addressed via the 2011-2015 Cascadia Initiative (CI) amphibious seismic array deployment, which combined coastal land seismometers with more than 60 ocean-bottom seismometers (OBS) situated directly above the presumed plate interface. We search the CI dataset for small, previously undetected interplate earthquakes to identify seismic patches on the megathrust. Using the automated subspace detection method, we search for previously undetected events. Our subspace comprises eigenvectors derived from CI OBS and on-land waveforms extracted for existing catalog events that appear to have occurred on the plate interface. Previous work focused on analysis of two repeating event clusters off the coast of Oregon spanning all 4 years of deployment. Here we expand earlier results to include detection and location analysis to the entire CSZ margin during the first year of CI deployment, with more than 200 new events detected for the central portion of the margin. Template events used for subspace scanning primarily occurred beneath the land surface along the coast, at the downdip edge of modeled high slip patches for the 1700 event, with most concentrated at the northwestern edge of the Olympic Peninsula.

  10. Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery

    Science.gov (United States)

    2016-09-27

    Bian, Student Member, IEEE, and Hamid Krim, Fellow, IEEE Abstract The success of sparse models in computer vision and machine learning is due to the...16. SECURITY CLASSIFICATION OF: The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional data...vision and machine learning is due to the fact that, high dimensional data is distributed in a union of low dimensional subspaces in many real-world

  11. Reverse time migration by Krylov subspace reduced order modeling

    Science.gov (United States)

    Basir, Hadi Mahdavi; Javaherian, Abdolrahim; Shomali, Zaher Hossein; Firouz-Abadi, Roohollah Dehghani; Gholamy, Shaban Ali

    2018-04-01

    Imaging is a key step in seismic data processing. To date, a myriad of advanced pre-stack depth migration approaches have been developed; however, reverse time migration (RTM) is still considered as the high-end imaging algorithm. The main limitations associated with the performance cost of reverse time migration are the intensive computation of the forward and backward simulations, time consumption, and memory allocation related to imaging condition. Based on the reduced order modeling, we proposed an algorithm, which can be adapted to all the aforementioned factors. Our proposed method benefit from Krylov subspaces method to compute certain mode shapes of the velocity model computed by as an orthogonal base of reduced order modeling. Reverse time migration by reduced order modeling is helpful concerning the highly parallel computation and strongly reduces the memory requirement of reverse time migration. The synthetic model results showed that suggested method can decrease the computational costs of reverse time migration by several orders of magnitudes, compared with reverse time migration by finite element method.

  12. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling

    Energy Technology Data Exchange (ETDEWEB)

    Vrugt, Jasper A [Los Alamos National Laboratory; Hyman, James M [Los Alamos National Laboratory; Robinson, Bruce A [Los Alamos National Laboratory; Higdon, Dave [Los Alamos National Laboratory; Ter Braak, Cajo J F [NETHERLANDS; Diks, Cees G H [UNIV OF AMSTERDAM

    2008-01-01

    Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.

  13. Empirical Examination of Fundamental Indexation in the German Market

    Science.gov (United States)

    Mihm, Max; Locarek-Junge, Hermann

    Index Funds, Exchange Traded Funds and Derivatives give investors easy access to well diversified index portfolios. These index-based investment products exhibit low fees, which make them an attractive alternative to actively managed funds. Against this background, a new class of stock indices has been established based on the concept of “Fundamental Indexation”. The selection and weighting of index constituents is conducted by means of fundamental criteria like total assets, book value or number of employees. This paper examines the performance of fundamental indices in the German equity market. For this purpose, a backtest of five fundamental indices is conducted over the last 20 years. Furthermore the index returns are analysed under the assumption of an efficient as well as an inefficient market. Index returns in efficient markets are explained by applying the three factor model for stock returns of Fama and French (J Financ Econ 33(1):3-56, 1993). The results show that the outperformance of fundamental indices is partly due to a higher risk exposure, particularly to companies with a low price to book ratio. By relaxing the assumption of market efficiency, a return drag of capitalisation weighted indices can be deduced. Given a mean-reverting movement of prices, a direct connection between market capitalisation and index weighting leads to inferior returns.

  14. Beamforming using subspace estimation from a diagonally averaged sample covariance.

    Science.gov (United States)

    Quijano, Jorge E; Zurk, Lisa M

    2017-08-01

    The potential benefit of a large-aperture sonar array for high resolution target localization is often challenged by the lack of sufficient data required for adaptive beamforming. This paper introduces a Toeplitz-constrained estimator of the clairvoyant signal covariance matrix corresponding to multiple far-field targets embedded in background isotropic noise. The estimator is obtained by averaging along subdiagonals of the sample covariance matrix, followed by covariance extrapolation using the method of maximum entropy. The sample covariance is computed from limited data snapshots, a situation commonly encountered with large-aperture arrays in environments characterized by short periods of local stationarity. Eigenvectors computed from the Toeplitz-constrained covariance are used to construct signal-subspace projector matrices, which are shown to reduce background noise and improve detection of closely spaced targets when applied to subspace beamforming. Monte Carlo simulations corresponding to increasing array aperture suggest convergence of the proposed projector to the clairvoyant signal projector, thereby outperforming the classic projector obtained from the sample eigenvectors. Beamforming performance of the proposed method is analyzed using simulated data, as well as experimental data from the Shallow Water Array Performance experiment.

  15. A frequency domain subspace algorithm for mixed causal, anti-causal LTI systems

    NARCIS (Netherlands)

    Fraanje, Rufus; Verhaegen, Michel; Verdult, Vincent; Pintelon, Rik

    2003-01-01

    The paper extends the subspacc identification method to estimate state-space models from frequency response function (FRF) samples, proposed by McKelvey et al. (1996) for mixed causal/anti-causal systems, and shows that other frequency domain subspace algorithms can be extended similarly. The method

  16. Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

    Science.gov (United States)

    Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

    2018-02-01

    A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

  17. Quantum probabilities as Dempster-Shafer probabilities in the lattice of subspaces

    International Nuclear Information System (INIS)

    Vourdas, A.

    2014-01-01

    The orthocomplemented modular lattice of subspaces L[H(d)], of a quantum system with d-dimensional Hilbert space H(d), is considered. A generalized additivity relation which holds for Kolmogorov probabilities is violated by quantum probabilities in the full lattice L[H(d)] (it is only valid within the Boolean subalgebras of L[H(d)]). This suggests the use of more general (than Kolmogorov) probability theories, and here the Dempster-Shafer probability theory is adopted. An operator D(H 1 ,H 2 ), which quantifies deviations from Kolmogorov probability theory is introduced, and it is shown to be intimately related to the commutator of the projectors P(H 1 ),P(H 2 ), to the subspaces H 1 , H 2 . As an application, it is shown that the proof of the inequalities of Clauser, Horne, Shimony, and Holt for a system of two spin 1/2 particles is valid for Kolmogorov probabilities, but it is not valid for Dempster-Shafer probabilities. The violation of these inequalities in experiments supports the interpretation of quantum probabilities as Dempster-Shafer probabilities

  18. Expedited Holonomic Quantum Computation via Net Zero-Energy-Cost Control in Decoherence-Free Subspace.

    Science.gov (United States)

    Pyshkin, P V; Luo, Da-Wei; Jing, Jun; You, J Q; Wu, Lian-Ao

    2016-11-25

    Holonomic quantum computation (HQC) may not show its full potential in quantum speedup due to the prerequisite of a long coherent runtime imposed by the adiabatic condition. Here we show that the conventional HQC can be dramatically accelerated by using external control fields, of which the effectiveness is exclusively determined by the integral of the control fields in the time domain. This control scheme can be realized with net zero energy cost and it is fault-tolerant against fluctuation and noise, significantly relaxing the experimental constraints. We demonstrate how to realize the scheme via decoherence-free subspaces. In this way we unify quantum robustness merits of this fault-tolerant control scheme, the conventional HQC and decoherence-free subspace, and propose an expedited holonomic quantum computation protocol.

  19. Third-order nonlinear differential operators preserving invariant subspaces of maximal dimension

    International Nuclear Information System (INIS)

    Qu Gai-Zhu; Zhang Shun-Li; Li Yao-Long

    2014-01-01

    In this paper, third-order nonlinear differential operators are studied. It is shown that they are quadratic forms when they preserve invariant subspaces of maximal dimension. A complete description of third-order quadratic operators with constant coefficients is obtained. One example is given to derive special solutions for evolution equations with third-order quadratic operators. (general)

  20. Memory Efficient PCA Methods for Large Group ICA.

    Science.gov (United States)

    Rachakonda, Srinivas; Silva, Rogers F; Liu, Jingyu; Calhoun, Vince D

    2016-01-01

    Principal component analysis (PCA) is widely used for data reduction in group independent component analysis (ICA) of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT). The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads), accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4 GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations ideal for big

  1. Memory efficient PCA methods for large group ICA

    Directory of Open Access Journals (Sweden)

    Srinivas eRachakonda

    2016-02-01

    Full Text Available Principal component analysis (PCA is widely used for data reduction in group independent component analysis (ICA of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT. The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads, accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations

  2. Code subspaces for LLM geometries

    Science.gov (United States)

    Berenstein, David; Miller, Alexandra

    2018-03-01

    We consider effective field theory around classical background geometries with a gauge theory dual, specifically those in the class of LLM geometries. These are dual to half-BPS states of N= 4 SYM. We find that the language of code subspaces is natural for discussing the set of nearby states, which are built by acting with effective fields on these backgrounds. This work extends our previous work by going beyond the strict infinite N limit. We further discuss how one can extract the topology of the state beyond N→∞ and find that, as before, uncertainty and entanglement entropy calculations provide a useful tool to do so. Finally, we discuss obstructions to writing down a globally defined metric operator. We find that the answer depends on the choice of reference state that one starts with. Therefore, within this setup, there is ambiguity in trying to write an operator that describes the metric globally.

  3. Modal–Physical Hybrid System Identification of High-rise Building via Subspace and Inverse-Mode Methods

    Directory of Open Access Journals (Sweden)

    Kohei Fujita

    2017-08-01

    Full Text Available A system identification (SI problem of high-rise buildings is investigated under restricted data environments. The shear and bending stiffnesses of a shear-bending model (SB model representing the high-rise buildings are identified via the smart combination of the subspace and inverse-mode methods. Since the shear and bending stiffnesses of the SB model can be identified in the inverse-mode method by using the lowest mode of horizontal displacements and floor rotation angles, the lowest mode of the objective building is identified first by using the subspace method. Identification of the lowest mode is performed by using the amplitude of transfer functions derived in the subspace method. Considering the resolution in measuring the floor rotation angles in lower stories, floor rotation angles in most stories are predicted from the floor rotation angle at the top floor. An empirical equation of floor rotation angles is proposed by investigating those for various building models. From the viewpoint of application of the present SI method to practical situations, a non-simultaneous measurement system is also proposed. In order to investigate the reliability and accuracy of the proposed SI method, a 10-story building frame subjected to micro-tremor is examined.

  4. On the selection of user-defined parameters in data-driven stochastic subspace identification

    Science.gov (United States)

    Priori, C.; De Angelis, M.; Betti, R.

    2018-02-01

    The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI. Then, the case of non-stationary seismic excitations with a reduced number of samples is considered. In this paper, partitions of the data matrix different from the one proposed in the SSI literature are investigated, together with the influence of different choices of the weighting matrices. The study is carried out considering two different applications: (1) data obtained from vibration tests on a scaled structure and (2) in-situ tests on a reinforced concrete building. Referring to the former, the identification of a steel frame structure tested on a shaking table is performed using its responses in terms of absolute accelerations to a stationary (white noise) base excitation and to non-stationary seismic excitations of low intensity. Black-box and modal models are identified in both cases and the results are compared with those from an input-output subspace technique. With regards to the latter, the identification of a complex hospital building is conducted using data obtained from ambient vibration tests.

  5. Invariant subspaces in some function spaces on symmetric spaces. II

    International Nuclear Information System (INIS)

    Platonov, S S

    1998-01-01

    Let G be a semisimple connected Lie group with finite centre, K a maximal compact subgroup of G, and M=G/K a Riemannian symmetric space of non-compact type. We study the problem of describing the structure of closed linear subspaces in various function spaces on M that are invariant under the quasiregular representation of the group G. We consider the case when M is a symplectic symmetric space of rank 1

  6. Estimating the number of components and detecting outliers using Angle Distribution of Loading Subspaces (ADLS) in PCA analysis.

    Science.gov (United States)

    Liu, Y J; Tran, T; Postma, G; Buydens, L M C; Jansen, J

    2018-08-22

    Principal Component Analysis (PCA) is widely used in analytical chemistry, to reduce the dimensionality of a multivariate data set in a few Principal Components (PCs) that summarize the predominant patterns in the data. An accurate estimate of the number of PCs is indispensable to provide meaningful interpretations and extract useful information. We show how existing estimates for the number of PCs may fall short for datasets with considerable coherence, noise or outlier presence. We present here how Angle Distribution of the Loading Subspaces (ADLS) can be used to estimate the number of PCs based on the variability of loading subspace across bootstrap resamples. Based on comprehensive comparisons with other well-known methods applied on simulated dataset, we show that ADLS (1) may quantify the stability of a PCA model with several numbers of PCs simultaneously; (2) better estimate the appropriate number of PCs when compared with the cross-validation and scree plot methods, specifically for coherent data, and (3) facilitate integrated outlier detection, which we introduce in this manuscript. We, in addition, demonstrate how the analysis of different types of real-life spectroscopic datasets may benefit from these advantages of ADLS. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  7. Fundamental and future prospects of printed ambipolar fluorene-type polymer light-emitting transistors for improved external quantum efficiency, mobility, and emission pattern

    Science.gov (United States)

    Kajii, Hirotake

    2018-05-01

    In this review, we focus on the improved external quantum efficiency, field-effect mobility, and emission pattern of top-gate-type polymer light-emitting transistors (PLETs) based on ambipolar fluorene-type polymers. A low-temperature, high-efficiency, printable red phosphorescent PLET based on poly(alkylfluorene) with modified alkyl side chains fabricated by a film transfer process is demonstrated. Device fabrication based on oriented films leads to an improved EL intensity owing to the increase in field-effect mobility. There are three factors that affect the transport of carriers, i.e., the energy level, threshold voltage, and mobility of each layer for heterostructure PLETs, which result in various emission patterns such as the line-shaped, multicolor and in-plane emission pattern in the full-channel area between source and drain electrodes. Fundamentals and future prospects in heterostructure devices are discussed and reviewed.

  8. Metastable decoherence-free subspaces and electromagnetically induced transparency in interacting many-body systems

    DEFF Research Database (Denmark)

    Macieszczak, Katarzyna; Zhou, Yanli; Hofferberth, Sebastian

    2017-01-01

    to stationarity this leads to a slow dynamics, which renders the typical assumption of fast relaxation invalid. We derive analytically the effective nonequilibrium dynamics in the decoherence-free subspace, which features coherent and dissipative two-body interactions. We discuss the use of this scenario...

  9. PRINCIPLES, BASES, AND LAWS OF FUNDAMENTAL INFORMATICS

    Directory of Open Access Journals (Sweden)

    Gennady N. Zverev

    2013-01-01

    Full Text Available This paper defines the goals and problems of fundamental informatics, formulates principal laws of information universe and constructive bases of information objects and processes. The classification of semantics types of knowledge and skills is presented. 

  10. High-efficiency power transfer for silicon-based photonic devices

    Science.gov (United States)

    Son, Gyeongho; Yu, Kyoungsik

    2018-02-01

    We demonstrate an efficient coupling of guided light of 1550 nm from a standard single-mode optical fiber to a silicon waveguide using the finite-difference time-domain method and propose a fabrication method of tapered optical fibers for efficient power transfer to silicon-based photonic integrated circuits. Adiabatically-varying fiber core diameters with a small tapering angle can be obtained using the tube etching method with hydrofluoric acid and standard single-mode fibers covered by plastic jackets. The optical power transmission of the fundamental HE11 and TE-like modes between the fiber tapers and the inversely-tapered silicon waveguides was calculated with the finite-difference time-domain method to be more than 99% at a wavelength of 1550 nm. The proposed method for adiabatic fiber tapering can be applied in quantum optics, silicon-based photonic integrated circuits, and nanophotonics. Furthermore, efficient coupling within the telecommunication C-band is a promising approach for quantum networks in the future.

  11. Residual and Backward Error Bounds in Minimum Residual Krylov Subspace Methods

    Czech Academy of Sciences Publication Activity Database

    Paige, C. C.; Strakoš, Zdeněk

    2002-01-01

    Roč. 23, č. 6 (2002), s. 1899-1924 ISSN 1064-8275 R&D Projects: GA AV ČR IAA1030103 Institutional research plan: AV0Z1030915 Keywords : linear equations * eigenproblem * large sparse matrices * iterative solutions * Krylov subspace methods * Arnoldi method * GMRES * modified Gram-Schmidt * least squares * total least squares * singular values Subject RIV: BA - General Mathematics Impact factor: 1.291, year: 2002

  12. A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG-Based Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Xin Chai

    2017-05-01

    Full Text Available Electroencephalography (EEG-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects. Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE, which

  13. Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Debesh Jha

    2017-01-01

    Full Text Available Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT for feature extraction, probabilistic principal component analysis (PPCA for dimensionality reduction, and a random subspace ensemble (RSE classifier along with the K-nearest neighbors (KNN algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV, the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study.

  14. Projected Gauss-Seidel subspace minimization method for interactive rigid body dynamics

    DEFF Research Database (Denmark)

    Silcowitz-Hansen, Morten; Abel, Sarah Maria Niebe; Erleben, Kenny

    2010-01-01

    artifacts such as viscous or damped contact response. In this paper, we present a new approach to contact force determination. We formulate the contact force problem as a nonlinear complementarity problem, and discretize the problem to derive the Projected Gauss–Seidel method. We combine the Projected Gauss......–Seidel method with a subspace minimization method. Our new method shows improved qualities and superior convergence properties for specific configurations....

  15. A Krylov Subspace Method for Unstructured Mesh SN Transport Computation

    International Nuclear Information System (INIS)

    Yoo, Han Jong; Cho, Nam Zin; Kim, Jong Woon; Hong, Ser Gi; Lee, Young Ouk

    2010-01-01

    Hong, et al., have developed a computer code MUST (Multi-group Unstructured geometry S N Transport) for the neutral particle transport calculations in three-dimensional unstructured geometry. In this code, the discrete ordinates transport equation is solved by using the discontinuous finite element method (DFEM) or the subcell balance methods with linear discontinuous expansion. In this paper, the conventional source iteration in the MUST code is replaced by the Krylov subspace method to reduce computing time and the numerical test results are given

  16. Fundamental Aeronautics Program: Overview of Propulsion Work in the Supersonic Cruise Efficiency Technical Challenge

    Science.gov (United States)

    Castner, Ray

    2012-01-01

    The Supersonics Project, part of NASA's Fundamental Aeronautics Program, contains a number of technical challenge areas which include sonic boom community response, airport noise, high altitude emissions, cruise efficiency, light weight durable engines/airframes, and integrated multi-discipline system design. This presentation provides an overview of the current (2012) activities in the supersonic cruise efficiency technical challenge, and is focused specifically on propulsion technologies. The intent is to develop and validate high-performance supersonic inlet and nozzle technologies. Additional work is planned for design and analysis tools for highly-integrated low-noise, low-boom applications. If successful, the payoffs include improved technologies and tools for optimized propulsion systems, propulsion technologies for a minimized sonic boom signature, and a balanced approach to meeting efficiency and community noise goals. In this propulsion area, the work is divided into advanced supersonic inlet concepts, advanced supersonic nozzle concepts, low fidelity computational tool development, high fidelity computational tools, and improved sensors and measurement capability. The current work in each area is summarized.

  17. Efficient multipartite entanglement purification with the entanglement link from a subspace

    Energy Technology Data Exchange (ETDEWEB)

    Deng Fuguo [Department of Physics, Applied Optics Beijing Area Major Laboratory, Beijing Conventional University, Beijing 100875 (China)

    2011-11-15

    We present an efficient multipartite entanglement purification protocol (MEPP) for N-photon systems in a Greenberger-Horne-Zeilinger state with parity-check detectors. It contains two parts. One is the conventional MEPP with which the parties can obtain a high-fidelity N-photon ensemble directly, similar to the MEPP with controlled-not gates. The other is our recycling MEPP in which the entanglement link is used to produce some N-photon entangled systems from entangled N{sup '}-photon subsystems (2{<=}N{sup '}efficiently by measuring the photons with potential bit-flip errors. With these two parts, the present MEPP has a higher efficiency than all other conventional MEPPs.

  18. Robust Switching Control and Subspace Identification for Flutter of Flexible Wing

    Directory of Open Access Journals (Sweden)

    Yizhe Wang

    2018-01-01

    Full Text Available Active flutter suppression and subspace identification for a flexible wing model using micro fiber composite actuator were experimentally studied in a low speed wind tunnel. NACA0006 thin airfoil model was used for the experimental object to verify the performance of identification algorithm and designed controller. The equation of the fluid, vibration, and piezoelectric coupled motion was theoretically analyzed and experimentally identified under the open-loop and closed-loop condition by subspace method for controller design. A robust pole placement algorithm in terms of linear matrix inequality that accommodates the model uncertainty caused by identification deviation and flow speed variation was utilized to stabilize the divergent aeroelastic system. For further enlarging the flutter envelope, additional controllers were designed subject to the models beyond the flutter speed. Wind speed was measured online as the decision parameter of switching between the controllers. To ensure the stability of arbitrary switching, Common Lyapunov function method was applied to design the robust pole placement controllers for different models to ensure that the closed-loop system shared a common Lyapunov function. Wind tunnel result showed that the designed controllers could stabilize the time varying aeroelastic system over a wide range under arbitrary switching.

  19. Fundamental studies of graphene/graphite and graphene-based Schottky photovoltaic devices

    Science.gov (United States)

    Miao, Xiaochang

    In the carbon allotropes family, graphene is one of the most versatile members and has been extensively studied since 2004. The goal of this dissertation is not only to investigate the novel fundamental science of graphene and its three-dimensional sibling, graphite, but also to explore graphene's promising potential in modern electronic and optoelectronic devices. The first two chapters provide a concise introduction to the fundamental solid state physics of graphene (as well as graphite) and the physics at the metal/semiconductor interfaces. In the third chapter, we demonstrate the formation of Schottky junctions at the interfaces of graphene (semimetal) and various inorganic semiconductors that play dominating roles in today's semiconductor technology, such as Si, SiC, GaAs and GaN. As shown from their current-voltage (I -V) and capacitance-voltage (C-V) characteristics, the interface physics can be well described within the framework of the Schottky-Mott model. The results are also well consist with that from our previous studies on graphite based Schottky diodes. In the fourth chapter, as an extension of graphene based Schottky work, we investigate the photovoltaic (PV) effect of graphene/Si junctions after chemically doped with an organic polymer (TFSA). The power conversion efficiency of the solar cell improves from 1.9% to 8.6% after TFSA doping, which is the record in all graphene based PVs. The I -V, C-V and external quantum efficiency measurements suggest 12 that such a significant enhancement in the device performance can be attributed to a doping-induced decrease in the series resistance and a simultaneous increase in the built-in potential. In the fifth chapter, we investigate for the first time the effect of uniaxial strains on magneto-transport properties of graphene. We find that low-temperature weak localization effect in monolayer graphene is gradually suppressed under increasing strains, which is due to a strain-induced decreased intervalley

  20. Banach C*-algebras not containing a subspace isomorphic to C0

    International Nuclear Information System (INIS)

    Basit, B.

    1989-09-01

    If X is a locally Hausdorff space and C 0 (X) the Banach algebra of continuous functions defined on X vanishing at infinity, we showed that a subalgebra A of C 0 (X) is finite dimensional if it does not contain a subspace isomorphic to the Banach space C 0 of convergent to zero complex sequences. In this paper we extend this result to noncommutative Banach C*-algebras and Banach* algebras. 10 refs

  1. Perturbation for Frames for a Subspace of a Hilbert Space

    DEFF Research Database (Denmark)

    Christensen, Ole; deFlicht, C.; Lennard, C.

    1997-01-01

    We extend a classical result stating that a sufficiently small perturbation$\\{ g_i \\}$ of a Riesz sequence $\\{ f_i \\}$ in a Hilbert space $H$ is again a Riesz sequence. It turns out that the analog result for a frame does not holdunless the frame is complete. However, we are able to prove a very...... similarresult for frames in the case where the gap between the subspaces$\\overline{span} \\{f_i \\}$ and $\\overline{span} \\{ g_i \\}$ is small enough. We give a geometric interpretation of the result....

  2. Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring

    Directory of Open Access Journals (Sweden)

    Alexander Caicedo

    2016-11-01

    Full Text Available Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP, assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + _. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first three days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen

  3. Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring.

    Science.gov (United States)

    Caicedo, Alexander; Varon, Carolina; Hunyadi, Borbala; Papademetriou, Maria; Tachtsidis, Ilias; Van Huffel, Sabine

    2016-01-01

    Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ . SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the

  4. Subspace Dimensionality: A Tool for Automated QC in Seismic Array Processing

    Science.gov (United States)

    Rowe, C. A.; Stead, R. J.; Begnaud, M. L.

    2013-12-01

    Because of the great resolving power of seismic arrays, the application of automated processing to array data is critically important in treaty verification work. A significant problem in array analysis is the inclusion of bad sensor channels in the beamforming process. We are testing an approach to automated, on-the-fly quality control (QC) to aid in the identification of poorly performing sensor channels prior to beam-forming in routine event detection or location processing. The idea stems from methods used for large computer servers, when monitoring traffic at enormous numbers of nodes is impractical on a node-by node basis, so the dimensionality of the node traffic is instead monitoried for anomalies that could represent malware, cyber-attacks or other problems. The technique relies upon the use of subspace dimensionality or principal components of the overall system traffic. The subspace technique is not new to seismology, but its most common application has been limited to comparing waveforms to an a priori collection of templates for detecting highly similar events in a swarm or seismic cluster. In the established template application, a detector functions in a manner analogous to waveform cross-correlation, applying a statistical test to assess the similarity of the incoming data stream to known templates for events of interest. In our approach, we seek not to detect matching signals, but instead, we examine the signal subspace dimensionality in much the same way that the method addresses node traffic anomalies in large computer systems. Signal anomalies recorded on seismic arrays affect the dimensional structure of the array-wide time-series. We have shown previously that this observation is useful in identifying real seismic events, either by looking at the raw signal or derivatives thereof (entropy, kurtosis), but here we explore the effects of malfunctioning channels on the dimension of the data and its derivatives, and how to leverage this effect for

  5. A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

    2011-01-01

    comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we...

  6. Gene selection for microarray data classification via subspace learning and manifold regularization.

    Science.gov (United States)

    Tang, Chang; Cao, Lijuan; Zheng, Xiao; Wang, Minhui

    2017-12-19

    With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

  7. Imaging of heart acoustic based on the sub-space methods using a microphone array.

    Science.gov (United States)

    Moghaddasi, Hanie; Almasganj, Farshad; Zoroufian, Arezoo

    2017-07-01

    Heart disease is one of the leading causes of death around the world. Phonocardiogram (PCG) is an important bio-signal which represents the acoustic activity of heart, typically without any spatiotemporal information of the involved acoustic sources. The aim of this study is to analyze the PCG by employing a microphone array by which the heart internal sound sources could be localized, too. In this paper, it is intended to propose a modality by which the locations of the active sources in the heart could also be investigated, during a cardiac cycle. In this way, a microphone array with six microphones is employed as the recording set up to be put on the human chest. In the following, the Group Delay MUSIC algorithm which is a sub-space based localization method is used to estimate the location of the heart sources in different phases of the PCG. We achieved to 0.14cm mean error for the sources of first heart sound (S 1 ) simulator and 0.21cm mean error for the sources of second heart sound (S 2 ) simulator with Group Delay MUSIC algorithm. The acoustical diagrams created for human subjects show distinct patterns in various phases of the cardiac cycles such as the first and second heart sounds. Moreover, the evaluated source locations for the heart valves are matched with the ones that are obtained via the 4-dimensional (4D) echocardiography applied, to a real human case. Imaging of heart acoustic map presents a new outlook to indicate the acoustic properties of cardiovascular system and disorders of valves and thereby, in the future, could be used as a new diagnostic tool. Copyright © 2017. Published by Elsevier B.V.

  8. A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery

    Science.gov (United States)

    Sun, Weiwei; Ma, Jun; Yang, Gang; Du, Bo; Zhang, Liangpei

    2017-06-01

    A new Bayesian method named Poisson Nonnegative Matrix Factorization with Parameter Subspace Clustering Constraint (PNMF-PSCC) has been presented to extract endmembers from Hyperspectral Imagery (HSI). First, the method integrates the liner spectral mixture model with the Bayesian framework and it formulates endmember extraction into a Bayesian inference problem. Second, the Parameter Subspace Clustering Constraint (PSCC) is incorporated into the statistical program to consider the clustering of all pixels in the parameter subspace. The PSCC could enlarge differences among ground objects and helps finding endmembers with smaller spectrum divergences. Meanwhile, the PNMF-PSCC method utilizes the Poisson distribution as the prior knowledge of spectral signals to better explain the quantum nature of light in imaging spectrometer. Third, the optimization problem of PNMF-PSCC is formulated into maximizing the joint density via the Maximum A Posterior (MAP) estimator. The program is finally solved by iteratively optimizing two sub-problems via the Alternating Direction Method of Multipliers (ADMM) framework and the FURTHESTSUM initialization scheme. Five state-of-the art methods are implemented to make comparisons with the performance of PNMF-PSCC on both the synthetic and real HSI datasets. Experimental results show that the PNMF-PSCC outperforms all the five methods in Spectral Angle Distance (SAD) and Root-Mean-Square-Error (RMSE), and especially it could identify good endmembers for ground objects with smaller spectrum divergences.

  9. Subspace identification of Hammer stein models using support vector machines

    International Nuclear Information System (INIS)

    Al-Dhaifallah, Mujahed

    2011-01-01

    System identification is the art of finding mathematical tools and algorithms that build an appropriate mathematical model of a system from measured input and output data. Hammerstein model, consisting of a memoryless nonlinearity followed by a dynamic linear element, is often a good trade-off as it can represent some dynamic nonlinear systems very accurately, but is nonetheless quite simple. Moreover, the extensive knowledge about LTI system representations can be applied to the dynamic linear block. On the other hand, finding an effective representation for the nonlinearity is an active area of research. Recently, support vector machines (SVMs) and least squares support vector machines (LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In contrast with other approximation methods, SVMs do not require a-priori structural information. Furthermore, there are well established methods with guaranteed convergence (ordinary least squares, quadratic programming) for fitting LS-SVMs and SVMs. The general objective of this research is to develop new subspace algorithms for Hammerstein systems based on SVM regression.

  10. Robust adaptive subspace detection in impulsive noise

    KAUST Repository

    Ben Atitallah, Ismail

    2016-09-13

    This paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.

  11. Robust adaptive subspace detection in impulsive noise

    KAUST Repository

    Ben Atitallah, Ismail; Kammoun, Abla; Alouini, Mohamed-Slim; Al-Naffouri, Tareq Y.

    2016-01-01

    This paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.

  12. Challenge Based Innovation: Translating Fundamental Research into Societal Applications

    Science.gov (United States)

    Kurikka, Joona; Utriainen, Tuuli; Repokari, Lauri

    2016-01-01

    This paper is based on work done at IdeaSquare, a new innovation experiment at CERN, the European Organization for Nuclear Research. The paper explores the translation of fundamental research into societal applications with the help of multidisciplinary student teams, project- and problem-based learning and design thinking methods. The theme is…

  13. Harmonic/inter-harmonic detection based on DNS-MUSIC function%基于噪声子空间分解MUSIC函数的谐波/间谐波检测算法

    Institute of Scientific and Technical Information of China (English)

    孟玲玲; 孙常栋; 王晓东

    2012-01-01

    A detection method based on DNS-MUSIC(Decomposition of Noise Subspace in MUSIC) is presented for the harmonic/inter-harmonic of power system. According to the eigenvalue decomposition theory,the signal self-correlation matrix is decomposed into the signal subspace and the noise subspace. The noise subspace is further decomposed based on their orthogonality,and then transformed to construct its DNS-MUSIC function. The estimated frequencies of fundamental and harmonics are obtained by solving the polynomial of DNS-MUSIC. Combined with the estimated frequency components of power system signal,the extended Prony algorithm is then applied to detect its amplitude and phase of power system signal. Simulation proves the feasibility .efficiency and stability of the proposed algorithm in comparison with other classical algorithms.%针对电力系统中存在的谐波和间谐波问题,提出了基于噪声子空间分解MUSIC (DNS-MUSIC)函数的谐波/间谐波检测方法.利用信号自相关矩阵的特征值分解理论,将信号的自相关矩阵分解为信号子空间和噪声子空间,利用2个子空间的正交性进一步分解噪声子空间,对其进行变换,构造出基于噪声子空间分解的特征多项式(DNS-MUSIC函数),求解该多项式得到信号基波和谐波频率预估计,结合消噪思想检测电力系统信号频率成分,然后利用扩展Prony法检测信号的幅值和相位.通过仿真实验与其他经典算法比较,结果证明了所提算法的可行性、高效性和稳定性.

  14. Computerized data base of the fundamental constants of nature

    International Nuclear Information System (INIS)

    Henry, E.A.; Hampel, V.E.

    1975-01-01

    Fifty-seven fundamental constants of nature were computerized from the up-to-date evaluations of E. R. Cohen and B. N. Taylor. The constants are annotated with regard to symbol, value, uncertainty, and scaling factor. This computerization is part of the scientific data base project of the Information Research Group at Lawrence Livermore Laboratory. The MASTER CONTROL data base management system is used. The computerized fundamental constants can be requested from the ERDA Computer Program Exchange and Information Center of the Argonne National Laboratory or from the National Technical Information Service of the U. S. Department of Commerce. This is the first of a series of releases on preparation of computerized scientific and technological data banks. The next release is a data bank of conversion factors for different units of measurements. 3 figures

  15. Subspace Barzilai-Borwein Gradient Method for Large-Scale Bound Constrained Optimization

    International Nuclear Information System (INIS)

    Xiao Yunhai; Hu Qingjie

    2008-01-01

    An active set subspace Barzilai-Borwein gradient algorithm for large-scale bound constrained optimization is proposed. The active sets are estimated by an identification technique. The search direction consists of two parts: some of the components are simply defined; the other components are determined by the Barzilai-Borwein gradient method. In this work, a nonmonotone line search strategy that guarantees global convergence is used. Preliminary numerical results show that the proposed method is promising, and competitive with the well-known method SPG on a subset of bound constrained problems from CUTEr collection

  16. Experimental Study of Generalized Subspace Filters for the Cocktail Party Situation

    DEFF Research Database (Denmark)

    Christensen, Knud Bank; Christensen, Mads Græsbøll; Boldt, Jesper B.

    2016-01-01

    This paper investigates the potential performance of generalized subspace filters for speech enhancement in cocktail party situations with very poor signal/noise ratio, e.g. down to -15 dB. Performance metrics output signal/noise ratio, signal/ distortion ratio, speech quality rating and speech...... intelligibility rating are mapped as functions of two algorithm parameters, revealing clear trade-off options between noise, distortion and subjective performances and a recommended choice of trade-off. Given sufficiently good noise statistics, SNR improvements around 20 dB as well as PESQ quality and STOI...

  17. Practical Low Data-Complexity Subspace-Trail Cryptanalysis of Round-Reduced PRINCE

    DEFF Research Database (Denmark)

    Grassi, Lorenzo; Rechberger, Christian

    2016-01-01

    Subspace trail cryptanalysis is a very recent new cryptanalysis technique, and includes differential, truncated differential, impossible differential, and integral attacks as special cases. In this paper, we consider PRINCE, a widely analyzed block cipher proposed in 2012. After the identification......-plaintext category. The attacks have been verified using a C implementation. Of independent interest, we consider a variant of PRINCE in which ShiftRows and MixLayer operations are exchanged in position. In particular, our result shows that the position of ShiftRows and MixLayer operations influences the security...

  18. A Mixed Integer Efficient Global Optimization Framework: Applied to the Simultaneous Aircraft Design, Airline Allocation and Revenue Management Problem

    Science.gov (United States)

    Roy, Satadru

    Traditional approaches to design and optimize a new system, often, use a system-centric objective and do not take into consideration how the operator will use this new system alongside of other existing systems. This "hand-off" between the design of the new system and how the new system operates alongside other systems might lead to a sub-optimal performance with respect to the operator-level objective. In other words, the system that is optimal for its system-level objective might not be best for the system-of-systems level objective of the operator. Among the few available references that describe attempts to address this hand-off, most follow an MDO-motivated subspace decomposition approach of first designing a very good system and then provide this system to the operator who decides the best way to use this new system along with the existing systems. The motivating example in this dissertation presents one such similar problem that includes aircraft design, airline operations and revenue management "subspaces". The research here develops an approach that could simultaneously solve these subspaces posed as a monolithic optimization problem. The monolithic approach makes the problem a Mixed Integer/Discrete Non-Linear Programming (MINLP/MDNLP) problem, which are extremely difficult to solve. The presence of expensive, sophisticated engineering analyses further aggravate the problem. To tackle this challenge problem, the work here presents a new optimization framework that simultaneously solves the subspaces to capture the "synergism" in the problem that the previous decomposition approaches may not have exploited, addresses mixed-integer/discrete type design variables in an efficient manner, and accounts for computationally expensive analysis tools. The framework combines concepts from efficient global optimization, Kriging partial least squares, and gradient-based optimization. This approach then demonstrates its ability to solve an 11 route airline network

  19. Efficient Vector-Based Forwarding for Underwater Sensor Networks

    Directory of Open Access Journals (Sweden)

    Peng Xie

    2010-01-01

    Full Text Available Underwater Sensor Networks (UWSNs are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, node mobility, high error probability, and 3-dimensional space. These new features bring many challenges to the network protocol design of UWSNs. In this paper, we tackle one fundamental problem in UWSNs: robust, scalable, and energy efficient routing. We propose vector-based forwarding (VBF, a geographic routing protocol. In VBF, the forwarding path is guided by a vector from the source to the target, no state information is required on the sensor nodes, and only a small fraction of the nodes is involved in routing. To improve the robustness, packets are forwarded in redundant and interleaved paths. Further, a localized and distributed self-adaptation algorithm allows the nodes to reduce energy consumption by discarding redundant packets. VBF performs well in dense networks. For sparse networks, we propose a hop-by-hop vector-based forwarding (HH-VBF protocol, which adapts the vector-based approach at every hop. We evaluate the performance of VBF and HH-VBF through extensive simulations. The simulation results show that VBF achieves high packet delivery ratio and energy efficiency in dense networks and HH-VBF has high packet delivery ratio even in sparse networks.

  20. Efficient statistically accurate algorithms for the Fokker-Planck equation in large dimensions

    Science.gov (United States)

    Chen, Nan; Majda, Andrew J.

    2018-02-01

    Solving the Fokker-Planck equation for high-dimensional complex turbulent dynamical systems is an important and practical issue. However, most traditional methods suffer from the curse of dimensionality and have difficulties in capturing the fat tailed highly intermittent probability density functions (PDFs) of complex systems in turbulence, neuroscience and excitable media. In this article, efficient statistically accurate algorithms are developed for solving both the transient and the equilibrium solutions of Fokker-Planck equations associated with high-dimensional nonlinear turbulent dynamical systems with conditional Gaussian structures. The algorithms involve a hybrid strategy that requires only a small number of ensembles. Here, a conditional Gaussian mixture in a high-dimensional subspace via an extremely efficient parametric method is combined with a judicious non-parametric Gaussian kernel density estimation in the remaining low-dimensional subspace. Particularly, the parametric method provides closed analytical formulae for determining the conditional Gaussian distributions in the high-dimensional subspace and is therefore computationally efficient and accurate. The full non-Gaussian PDF of the system is then given by a Gaussian mixture. Different from traditional particle methods, each conditional Gaussian distribution here covers a significant portion of the high-dimensional PDF. Therefore a small number of ensembles is sufficient to recover the full PDF, which overcomes the curse of dimensionality. Notably, the mixture distribution has significant skill in capturing the transient behavior with fat tails of the high-dimensional non-Gaussian PDFs, and this facilitates the algorithms in accurately describing the intermittency and extreme events in complex turbulent systems. It is shown in a stringent set of test problems that the method only requires an order of O (100) ensembles to successfully recover the highly non-Gaussian transient PDFs in up to 6

  1. Dissatisfaction of Compact Picard Condition (CPC) with GRACE satellite data and its treatment by Generalized Tikhonov in Sobolev subspace

    Science.gov (United States)

    AllahTavakoli, Y.; Bagheri, H.; Safari, A.; Sharifi, M.

    2012-04-01

    This paper is mainly aiming to prove that the stripy noises in the map of earth's surface mass-density changes derived from GRACE Satellites gravimetry, is due to a dissatisfaction of Compact Picard Condition (CPC) with the GRACE data in the inversion of the Newton Integral Equation over the thin layer of earth; and hence the paper proposes the regularization strategies as efficient tools to treat the Ill-posedness and consequently to de-strip the data. First of all, we preferred to slightly modify the mathematical model of earth's surface mass-density changes developed creatively first by J. Wahr and et.al (1998), according to the all their previous assumptions plus taking into consideration the effect of the earth topography. By the modification we expect that some uncertainties in the prior model have been reduced to some extent. Then we analyzed the CPC on the model and we demonstrated how to perform Generalized Tikhonov regularization in Sobolev subspace for overcoming the instability of the problem. Then we applied the strategy in some simulations and case studies to validate our ideas. The simulations confirm that the stripy noises in the GRACE-derived map of the mass-density changes are due to the CPC dissatisfaction and furthermore the case studies show that Generalized Tikhonov regularization in Sobolev subspace is an influential filtering tool to de-strip the noisy data. Also, the case studies interestingly show that the effect of the topography is comparable to the effect of the load Love numbers on the Wahr's model; hence it may be taken into consideration when the load Love numbers have been taken into account.

  2. MODAL TRACKING of A Structural Device: A Subspace Identification Approach

    Energy Technology Data Exchange (ETDEWEB)

    Candy, J. V. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Franco, S. N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Ruggiero, E. L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Emmons, M. C. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Lopez, I. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Stoops, L. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-03-20

    Mechanical devices operating in an environment contaminated by noise, uncertainties, and extraneous disturbances lead to low signal-to-noise-ratios creating an extremely challenging processing problem. To detect/classify a device subsystem from noisy data, it is necessary to identify unique signatures or particular features. An obvious feature would be resonant (modal) frequencies emitted during its normal operation. In this report, we discuss a model-based approach to incorporate these physical features into a dynamic structure that can be used for such an identification. The approach we take after pre-processing the raw vibration data and removing any extraneous disturbances is to obtain a representation of the structurally unknown device along with its subsystems that capture these salient features. One approach is to recognize that unique modal frequencies (sinusoidal lines) appear in the estimated power spectrum that are solely characteristic of the device under investigation. Therefore, the objective of this effort is based on constructing a black box model of the device that captures these physical features that can be exploited to “diagnose” whether or not the particular device subsystem (track/detect/classify) is operating normally from noisy vibrational data. Here we discuss the application of a modern system identification approach based on stochastic subspace realization techniques capable of both (1) identifying the underlying black-box structure thereby enabling the extraction of structural modes that can be used for analysis and modal tracking as well as (2) indicators of condition and possible changes from normal operation.

  3. Comparative analysis of different weight matrices in subspace system identification for structural health monitoring

    Science.gov (United States)

    Shokravi, H.; Bakhary, NH

    2017-11-01

    Subspace System Identification (SSI) is considered as one of the most reliable tools for identification of system parameters. Performance of a SSI scheme is considerably affected by the structure of the associated identification algorithm. Weight matrix is a variable in SSI that is used to reduce the dimensionality of the state-space equation. Generally one of the weight matrices of Principle Component (PC), Unweighted Principle Component (UPC) and Canonical Variate Analysis (CVA) are used in the structure of a SSI algorithm. An increasing number of studies in the field of structural health monitoring are using SSI for damage identification. However, studies that evaluate the performance of the weight matrices particularly in association with accuracy, noise resistance, and time complexity properties are very limited. In this study, the accuracy, noise-robustness, and time-efficiency of the weight matrices are compared using different qualitative and quantitative metrics. Three evaluation metrics of pole analysis, fit values and elapsed time are used in the assessment process. A numerical model of a mass-spring-dashpot and operational data is used in this research paper. It is observed that the principal components obtained using PC algorithms are more robust against noise uncertainty and give more stable results for the pole distribution. Furthermore, higher estimation accuracy is achieved using UPC algorithm. CVA had the worst performance for pole analysis and time efficiency analysis. The superior performance of the UPC algorithm in the elapsed time is attributed to using unit weight matrices. The obtained results demonstrated that the process of reducing dimensionality in CVA and PC has not enhanced the time efficiency but yield an improved modal identification in PC.

  4. Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

    Directory of Open Access Journals (Sweden)

    Omar Eldwaik

    2018-01-01

    Full Text Available Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.

  5. s-Step Krylov Subspace Methods as Bottom Solvers for Geometric Multigrid

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Samuel [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Lijewski, Mike [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Almgren, Ann [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Straalen, Brian Van [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Carson, Erin [Univ. of California, Berkeley, CA (United States); Knight, Nicholas [Univ. of California, Berkeley, CA (United States); Demmel, James [Univ. of California, Berkeley, CA (United States)

    2014-08-14

    Geometric multigrid solvers within adaptive mesh refinement (AMR) applications often reach a point where further coarsening of the grid becomes impractical as individual sub domain sizes approach unity. At this point the most common solution is to use a bottom solver, such as BiCGStab, to reduce the residual by a fixed factor at the coarsest level. Each iteration of BiCGStab requires multiple global reductions (MPI collectives). As the number of BiCGStab iterations required for convergence grows with problem size, and the time for each collective operation increases with machine scale, bottom solves in large-scale applications can constitute a significant fraction of the overall multigrid solve time. In this paper, we implement, evaluate, and optimize a communication-avoiding s-step formulation of BiCGStab (CABiCGStab for short) as a high-performance, distributed-memory bottom solver for geometric multigrid solvers. This is the first time s-step Krylov subspace methods have been leveraged to improve multigrid bottom solver performance. We use a synthetic benchmark for detailed analysis and integrate the best implementation into BoxLib in order to evaluate the benefit of a s-step Krylov subspace method on the multigrid solves found in the applications LMC and Nyx on up to 32,768 cores on the Cray XE6 at NERSC. Overall, we see bottom solver improvements of up to 4.2x on synthetic problems and up to 2.7x in real applications. This results in as much as a 1.5x improvement in solver performance in real applications.

  6. On an efficient modification of singular value decomposition using independent component analysis for improved MRS denoising and quantification

    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

  7. INSCY

    DEFF Research Database (Denmark)

    Assent, Ira; Krieger, Ralph; Müller, Emmanuel

    2008-01-01

    Clustering is an established data mining technique for grouping objects based on mutual similarity. In high-dimensional spaces, clusters are typically hidden in the scatter of irrelevant attributes. To detect these hidden clusters, subspace clustering focuses on relevant attribute projections...... for each individual cluster. As the number of possible projections is exponential in the number of dimensions, efficiency is crucial for these high-dimensional settings. Moreover, the resulting subspace clusters are often highly redundant, i.e. many clusters are detected multiply in several projections....... Containing essentially the same information, redundant subspace clusters have to be removed to allow users to review the entire output. In addition, removal of low-dimensional redundancy actually improves quality. In this work we propose a novel index structure for efficient subspace clustering with in...

  8. Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms.

    Science.gov (United States)

    Harikumar, G; Bresler, Y

    1999-01-01

    We address the problem of restoring an image from its noisy convolutions with two or more unknown finite impulse response (FIR) filters. We develop theoretical results about the existence and uniqueness of solutions, and show that under some generically true assumptions, both the filters and the image can be determined exactly in the absence of noise, and stably estimated in its presence. We present efficient algorithms to estimate the blur functions and their sizes. These algorithms are of two types, subspace-based and likelihood-based, and are extensions of techniques proposed for the solution of the multichannel blind deconvolution problem in one dimension. We present memory and computation-efficient techniques to handle the very large matrices arising in the two-dimensional (2-D) case. Once the blur functions are determined, they are used in a multichannel deconvolution step to reconstruct the unknown image. The theoretical and practical implications of edge effects, and "weakly exciting" images are examined. Finally, the algorithms are demonstrated on synthetic and real data.

  9. Evidence-based dentistry: fundamentals for the dentist.

    Science.gov (United States)

    Bauer, Janet; Chiappelli, Francesco; Spackman, Sue; Prolo, Paolo; Stevenson, Richard

    2006-06-01

    This article explains the fundamentals of evidence-based dentistry for the dentist. Evidence-based dentistry is a discipline whose primary participant is the translational researcher. Recent developments have emphasized the importance of this discipline (clinical and translational research) for improving health care. The process of evidence-based dentistry is the reciprocation of new and existing evidence between dentists and quantitative and qualitative researchers, facilitated by the translational researcher. The product of this reciprocation is the clinical practice guideline, or best evidence, that provides the patient options in choosing treatments or services. These options are quantified and qualified by decision, utility, and cost data. Using shared decision-making, the dentist and patient arrive at a mutual understanding of which option best meets an acceptable and preferred treatment course that is cost effective. This option becomes the clinical decision.

  10. Magnetoelectric polymer-based composites fundamentals and applications

    CERN Document Server

    Martins, Pedro

    2017-01-01

    The first book on this topic provides a comprehensive and well-structured overview of the fundamentals, synthesis and emerging applications of magnetoelectric polymer materials. Following an introduction to the basic aspects of polymer based magnetoelectric materials and recent developments, subsequent chapters discuss the various types as well as their synthesis and characterization. There then follows a review of the latest applications, such as memories, sensors and actuators. The book concludes with a look at future technological advances. An essential reference for entrants to the field as well as for experienced researchers.

  11. A Fundamental Approach to Developing Aluminium based Bulk Amorphous Alloys based on Stable Liquid Metal Structures and Electronic Equilibrium - 154041

    Science.gov (United States)

    2017-03-28

    AFRL-AFOSR-JP-TR-2017-0027 A Fundamental Approach to Developing Aluminium -based Bulk Amorphous Alloys based on Stable Liquid-Metal Structures and...to 16 Dec 2016 4.  TITLE AND SUBTITLE A Fundamental Approach to Developing Aluminium -based Bulk Amorphous Alloys based on Stable Liquid-Metal...Air Force Research Laboratory for accurately predicting compositions of new amorphous alloys specifically based on aluminium with properties superior

  12. Energy Management Strategies based on efficiency map for Fuel Cell Hybrid Vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Feroldi, Diego; Serra, Maria; Riera, Jordi [Institut de Robotica i Informatica Industrial (CSIC-UPC), C. Llorens i Artigas 4, 08028 Barcelona (Spain)

    2009-05-15

    The addition of a fast auxiliary power source like a supercapacitor bank in fuel cell-based vehicles has a great potential because permits a significant reduction of the hydrogen consumption and an improvement of the vehicle efficiency. The Energy Management Strategies, commanding the power split between the power sources in the hybrid arrangement to fulfil the power requirement, perform a fundamental role to achieve this objective. In this work, three strategies based on the knowledge of the fuel cell efficiency map are proposed. These strategies are attractive due to the relative simplicity of the real time implementation and the good performance. The strategies are tested both in a simulation environment and in an experimental setup using a 1.2-kW PEM fuel cell. The results, in terms of hydrogen consumption, are compared with an optimal case, which is assessed trough an advantageous technique also introduced in this work and with a pure fuel cell vehicle as well. This comparative reveals high efficiency and good performance, allowing to save up to 26% of hydrogen in urban scenarios. (author)

  13. Ordering sparse matrices for cache-based systems

    International Nuclear Information System (INIS)

    Biswas, Rupak; Oliker, Leonid

    2001-01-01

    The Conjugate Gradient (CG) algorithm is the oldest and best-known Krylov subspace method used to solve sparse linear systems. Most of the coating-point operations within each CG iteration is spent performing sparse matrix-vector multiplication (SPMV). We examine how various ordering and partitioning strategies affect the performance of CG and SPMV when different programming paradigms are used on current commercial cache-based computers. However, a multithreaded implementation on the cacheless Cray MTA demonstrates high efficiency and scalability without any special ordering or partitioning

  14. Data-Driven Nonlinear Subspace Modeling for Prediction and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Ping; Song, Heda; Wang, Hong; Chai, Tianyou

    2017-09-01

    Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multi-phase and multi-field coupling and large time delay occur during its operation. In BF operation, the molten iron temperature (MIT) as well as Si, P and S contents of molten iron are the most essential molten iron quality (MIQ) indices, whose measurement, modeling and control have always been important issues in metallurgic engineering and automation field. This paper develops a novel data-driven nonlinear state space modeling for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques. First, to improve modeling efficiency, a data-driven hybrid method combining canonical correlation analysis and correlation analysis is proposed to identify the most influential controllable variables as the modeling inputs from multitudinous factors would affect the MIQ indices. Then, a Hammerstein model for the prediction of MIQ indices is established using the LS-SVM based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method to fit the complex nonlinear kernel function. Compared to the original Hammerstein model, this simplified model can not only significantly reduce the computational complexity, but also has almost the same reliability and accuracy for a stable prediction of MIQ indices. Last, in order to verify the practicability of the developed model, it is applied in designing a genetic algorithm based nonlinear predictive controller for multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approach.

  15. Quasistatic Seismic Damage Indicators for RC Structures from Dissipating Energies in Tangential Subspaces

    Directory of Open Access Journals (Sweden)

    Wilfried B. Krätzig

    2014-01-01

    Full Text Available This paper applies recent research on structural damage description to earthquake-resistant design concepts. Based on the primary design aim of life safety, this work adopts the necessity of additional protection aims for property, installation, and equipment. This requires the definition of damage indicators, which are able to quantify the arising structural damage. As in present design, it applies nonlinear quasistatic (pushover concepts due to code provisions as simplified dynamic design tools. Substituting so nonlinear time-history analyses, seismic low-cycle fatigue of RC structures is approximated in similar manner. The treatment will be embedded into a finite element environment, and the tangential stiffness matrix KT in tangential subspaces then is identified as the most general entry for structural damage information. Its spectra of eigenvalues λi or natural frequencies ωi of the structure serve to derive damage indicators Di, applicable to quasistatic evaluation of seismic damage. Because det KT=0 denotes structural failure, such damage indicators range from virgin situation Di=0 to failure Di=1 and thus correspond with Fema proposals on performance-based seismic design. Finally, the developed concept is checked by reanalyses of two experimentally investigated RC frames.

  16. Efficient solar hydrogen production by photocatalytic water splitting: From fundamental study to pilot demonstration

    Energy Technology Data Exchange (ETDEWEB)

    Jing, Dengwei; Guo, Liejin; Zhao, Liang; Zhang, Ximin; Liu, Huan; Li, Mingtao; Shen, Shaohua; Liu, Guanjie; Hu, Xiaowei; Zhang, Xianghui; Zhang, Kai; Ma, Lijin; Guo, Penghui [State Key Lab of Multiphase Flow in Power Engineering, Xi' an Jiaotong University, 28 Xianning West Road, Xi' an 710049 (China)

    2010-07-15

    Photocatalytic water splitting with solar light is one of the most promising technologies for solar hydrogen production. From a systematic point of view, whether it is photocatalyst and reaction system development or the reactor-related design, the essentials could be summarized as: photon transfer limitations and mass transfer limitations (in the case of liquid phase reactions). Optimization of these two issues are therefore given special attention throughout our study. In this review, the state of the art for the research of photocatalytic hydrogen production, both outcomes and challenges in this field, were briefly reviewed. Research progress of our lab, from fundamental study of photocatalyst preparation to reactor configuration and pilot level demonstration, were introduced, showing the complete process of our effort for this technology to be economic viable in the near future. Our systematic and continuous study in this field lead to the development of a Compound Parabolic Concentrator (CPC) based photocatalytic hydrogen production solar rector for the first time. We have demonstrated the feasibility for efficient photocatalytic hydrogen production under direct solar light. The exiting challenges and difficulties for this technology to proceed from successful laboratory photocatalysis set-up up to an industrially relevant scale are also proposed. These issues have been the object of our research and would also be the direction of our study in future. (author)

  17. Efficient algorithms for factorization and join of blades

    NARCIS (Netherlands)

    Fontijne, D.; Dorst, L.; Bayro-Corrochano, E.; Scheuermann, G.

    2010-01-01

    Subspaces are powerful tools for modeling geometry. In geometric algebra, they are represented using blades and constructed using the outer product. Producing the actual geometrical intersection (meet) and union (join) of subspaces, rather than the simplified linearizations often used in

  18. Efficient algorithms for factorization and join of blades

    NARCIS (Netherlands)

    Fontijne, D.

    2008-01-01

    Subspaces are powerful tools for modeling geometry. In geometric algebra, they are represented using blades and constructed using the outer product. To produce the actual geometrical intersection (Meet) and union (Join) of subspaces, rather than the simplified linearizations often used in

  19. Using Video-Based Modeling to Promote Acquisition of Fundamental Motor Skills

    Science.gov (United States)

    Obrusnikova, Iva; Rattigan, Peter J.

    2016-01-01

    Video-based modeling is becoming increasingly popular for teaching fundamental motor skills to children in physical education. Two frequently used video-based instructional strategies that incorporate modeling are video prompting (VP) and video modeling (VM). Both strategies have been used across multiple disciplines and populations to teach a…

  20. The fundamental theorem of linearised Regge calculus

    International Nuclear Information System (INIS)

    Barrett, J.W.

    1987-01-01

    In linearised Regge calculus in a topologically trivial region, the space of linearised deviations of the edge lengths from a flat configuration, divided by the subspace of deformations due to translations of the vertices, is equivalent to the space of the linearised curvatures which satisfy the Bianchi identities. (orig.)

  1. Estimating security betas using prior information based on firm fundamentals

    NARCIS (Netherlands)

    Cosemans, M.; Frehen, R.; Schotman, P.C.; Bauer, R.

    2010-01-01

    This paper proposes a novel approach for estimating time-varying betas of individual stocks that incorporates prior information based on fundamentals. We shrink the rolling window estimate of beta towards a firm-specific prior that is motivated by asset pricing theory. The prior captures structural

  2. Estimating absolute configurational entropies of macromolecules: the minimally coupled subspace approach.

    Directory of Open Access Journals (Sweden)

    Ulf Hensen

    Full Text Available We develop a general minimally coupled subspace approach (MCSA to compute absolute entropies of macromolecules, such as proteins, from computer generated canonical ensembles. Our approach overcomes limitations of current estimates such as the quasi-harmonic approximation which neglects non-linear and higher-order correlations as well as multi-minima characteristics of protein energy landscapes. Here, Full Correlation Analysis, adaptive kernel density estimation, and mutual information expansions are combined and high accuracy is demonstrated for a number of test systems ranging from alkanes to a 14 residue peptide. We further computed the configurational entropy for the full 67-residue cofactor of the TATA box binding protein illustrating that MCSA yields improved results also for large macromolecular systems.

  3. A highly efficient approach to protein interactome mapping based on collaborative filtering framework.

    Science.gov (United States)

    Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

    2015-01-09

    The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

  4. Scalable and efficient separation of hydrogen isotopes using graphene-based electrochemical pumping

    Science.gov (United States)

    Lozada-Hidalgo, M.; Zhang, S.; Hu, S.; Esfandiar, A.; Grigorieva, I. V.; Geim, A. K.

    2017-05-01

    Thousands of tons of isotopic mixtures are processed annually for heavy-water production and tritium decontamination. The existing technologies remain extremely energy intensive and require large capital investments. New approaches are needed to reduce the industry's footprint. Recently, micrometre-size crystals of graphene are shown to act as efficient sieves for hydrogen isotopes pumped through graphene electrochemically. Here we report a fully-scalable approach, using graphene obtained by chemical vapour deposition, which allows a proton-deuteron separation factor of around 8, despite cracks and imperfections. The energy consumption is projected to be orders of magnitude smaller with respect to existing technologies. A membrane based on 30 m2 of graphene, a readily accessible amount, could provide a heavy-water output comparable to that of modern plants. Even higher efficiency is expected for tritium separation. With no fundamental obstacles for scaling up, the technology's simplicity, efficiency and green credentials call for consideration by the nuclear and related industries.

  5. Fundamental limits of repeaterless quantum communications

    Science.gov (United States)

    Pirandola, Stefano; Laurenza, Riccardo; Ottaviani, Carlo; Banchi, Leonardo

    2017-01-01

    Quantum communications promises reliable transmission of quantum information, efficient distribution of entanglement and generation of completely secure keys. For all these tasks, we need to determine the optimal point-to-point rates that are achievable by two remote parties at the ends of a quantum channel, without restrictions on their local operations and classical communication, which can be unlimited and two-way. These two-way assisted capacities represent the ultimate rates that are reachable without quantum repeaters. Here, by constructing an upper bound based on the relative entropy of entanglement and devising a dimension-independent technique dubbed ‘teleportation stretching', we establish these capacities for many fundamental channels, namely bosonic lossy channels, quantum-limited amplifiers, dephasing and erasure channels in arbitrary dimension. In particular, we exactly determine the fundamental rate-loss tradeoff affecting any protocol of quantum key distribution. Our findings set the limits of point-to-point quantum communications and provide precise and general benchmarks for quantum repeaters. PMID:28443624

  6. Predicting fundamental and realized distributions based on thermal niche: A case study of a freshwater turtle

    Science.gov (United States)

    Rodrigues, João Fabrício Mota; Coelho, Marco Túlio Pacheco; Ribeiro, Bruno R.

    2018-04-01

    Species distribution models (SDM) have been broadly used in ecology to address theoretical and practical problems. Currently, there are two main approaches to generate SDMs: (i) correlative, which is based on species occurrences and environmental predictor layers and (ii) process-based models, which are constructed based on species' functional traits and physiological tolerances. The distributions estimated by each approach are based on different components of species niche. Predictions of correlative models approach species realized niches, while predictions of process-based are more akin to species fundamental niche. Here, we integrated the predictions of fundamental and realized distributions of the freshwater turtle Trachemys dorbigni. Fundamental distribution was estimated using data of T. dorbigni's egg incubation temperature, and realized distribution was estimated using species occurrence records. Both types of distributions were estimated using the same regression approaches (logistic regression and support vector machines), both considering macroclimatic and microclimatic temperatures. The realized distribution of T. dorbigni was generally nested in its fundamental distribution reinforcing theoretical assumptions that the species' realized niche is a subset of its fundamental niche. Both modelling algorithms produced similar results but microtemperature generated better results than macrotemperature for the incubation model. Finally, our results reinforce the conclusion that species realized distributions are constrained by other factors other than just thermal tolerances.

  7. An additive subspace preconditioning method for the iterative solution of some problems with extreme contrasts in coefficients

    Czech Academy of Sciences Publication Activity Database

    Axelsson, Owe

    2014-01-01

    Roč. 22, č. 4 (2014), s. 289-310 ISSN 1570-2820 R&D Projects: GA MŠk ED1.1.00/02.0070 Institutional support: RVO:68145535 Keywords : preconditioning * additive subspace * small eigenvalues Subject RIV: BA - General Mathematics Impact factor: 2.310, year: 2014 http://www.degruyter.com/view/j/jnma.2014.22.issue-4/jnma-2014-0013/jnma-2014-0013. xml

  8. Model, analysis, and evaluation of the effects of analog VLSI arithmetic on linear subspace-based image recognition.

    Science.gov (United States)

    Carvajal, Gonzalo; Figueroa, Miguel

    2014-07-01

    Typical image recognition systems operate in two stages: feature extraction to reduce the dimensionality of the input space, and classification based on the extracted features. Analog Very Large Scale Integration (VLSI) is an attractive technology to achieve compact and low-power implementations of these computationally intensive tasks for portable embedded devices. However, device mismatch limits the resolution of the circuits fabricated with this technology. Traditional layout techniques to reduce the mismatch aim to increase the resolution at the transistor level, without considering the intended application. Relating mismatch parameters to specific effects in the application level would allow designers to apply focalized mismatch compensation techniques according to predefined performance/cost tradeoffs. This paper models, analyzes, and evaluates the effects of mismatched analog arithmetic in both feature extraction and classification circuits. For the feature extraction, we propose analog adaptive linear combiners with on-chip learning for both Least Mean Square (LMS) and Generalized Hebbian Algorithm (GHA). Using mathematical abstractions of analog circuits, we identify mismatch parameters that are naturally compensated during the learning process, and propose cost-effective guidelines to reduce the effect of the rest. For the classification, we derive analog models for the circuits necessary to implement Nearest Neighbor (NN) approach and Radial Basis Function (RBF) networks, and use them to emulate analog classifiers with standard databases of face and hand-writing digits. Formal analysis and experiments show how we can exploit adaptive structures and properties of the input space to compensate the effects of device mismatch at the application level, thus reducing the design overhead of traditional layout techniques. Results are also directly extensible to multiple application domains using linear subspace methods. Copyright © 2014 Elsevier Ltd. All rights

  9. Domain decomposed preconditioners with Krylov subspace methods as subdomain solvers

    Energy Technology Data Exchange (ETDEWEB)

    Pernice, M. [Univ. of Utah, Salt Lake City, UT (United States)

    1994-12-31

    Domain decomposed preconditioners for nonsymmetric partial differential equations typically require the solution of problems on the subdomains. Most implementations employ exact solvers to obtain these solutions. Consequently work and storage requirements for the subdomain problems grow rapidly with the size of the subdomain problems. Subdomain solves constitute the single largest computational cost of a domain decomposed preconditioner, and improving the efficiency of this phase of the computation will have a significant impact on the performance of the overall method. The small local memory available on the nodes of most message-passing multicomputers motivates consideration of the use of an iterative method for solving subdomain problems. For large-scale systems of equations that are derived from three-dimensional problems, memory considerations alone may dictate the need for using iterative methods for the subdomain problems. In addition to reduced storage requirements, use of an iterative solver on the subdomains allows flexibility in specifying the accuracy of the subdomain solutions. Substantial savings in solution time is possible if the quality of the domain decomposed preconditioner is not degraded too much by relaxing the accuracy of the subdomain solutions. While some work in this direction has been conducted for symmetric problems, similar studies for nonsymmetric problems appear not to have been pursued. This work represents a first step in this direction, and explores the effectiveness of performing subdomain solves using several transpose-free Krylov subspace methods, GMRES, transpose-free QMR, CGS, and a smoothed version of CGS. Depending on the difficulty of the subdomain problem and the convergence tolerance used, a reduction in solution time is possible in addition to the reduced memory requirements. The domain decomposed preconditioner is a Schur complement method in which the interface operators are approximated using interface probing.

  10. A Fundamental Parameter-Based Calibration Model for an Intrinsic Germanium X-Ray Fluorescence Spectrometer

    DEFF Research Database (Denmark)

    Christensen, Leif Højslet; Pind, Niels

    1982-01-01

    A matrix-independent fundamental parameter-based calibration model for an energy-dispersive X-ray fluorescence spectrometer has been developed. This model, which is part of a fundamental parameter approach quantification method, accounts for both the excitation and detection probability. For each...... secondary target a number of relative calibration constants are calculated on the basis of knowledge of the irradiation geometry, the detector specifications, and tabulated fundamental physical parameters. The absolute calibration of the spectrometer is performed by measuring one pure element standard per...

  11. A Channelization-Based DOA Estimation Method for Wideband Signals

    Directory of Open Access Journals (Sweden)

    Rui Guo

    2016-07-01

    Full Text Available In this paper, we propose a novel direction of arrival (DOA estimation method for wideband signals with sensor arrays. The proposed method splits the wideband array output into multiple frequency sub-channels and estimates the signal parameters using a digital channelization receiver. Based on the output sub-channels, a channelization-based incoherent signal subspace method (Channelization-ISM and a channelization-based test of orthogonality of projected subspaces method (Channelization-TOPS are proposed. Channelization-ISM applies narrowband signal subspace methods on each sub-channel independently. Then the arithmetic mean or geometric mean of the estimated DOAs from each sub-channel gives the final result. Channelization-TOPS measures the orthogonality between the signal and the noise subspaces of the output sub-channels to estimate DOAs. The proposed channelization-based method isolates signals in different bandwidths reasonably and improves the output SNR. It outperforms the conventional ISM and TOPS methods on estimation accuracy and dynamic range, especially in real environments. Besides, the parallel processing architecture makes it easy to implement on hardware. A wideband digital array radar (DAR using direct wideband radio frequency (RF digitization is presented. Experiments carried out in a microwave anechoic chamber with the wideband DAR are presented to demonstrate the performance. The results verify the effectiveness of the proposed method.

  12. A Subspace Approach to the Structural Decomposition and Identification of Ankle Joint Dynamic Stiffness.

    Science.gov (United States)

    Jalaleddini, Kian; Tehrani, Ehsan Sobhani; Kearney, Robert E

    2017-06-01

    The purpose of this paper is to present a structural decomposition subspace (SDSS) method for decomposition of the joint torque to intrinsic, reflexive, and voluntary torques and identification of joint dynamic stiffness. First, it formulates a novel state-space representation for the joint dynamic stiffness modeled by a parallel-cascade structure with a concise parameter set that provides a direct link between the state-space representation matrices and the parallel-cascade parameters. Second, it presents a subspace method for the identification of the new state-space model that involves two steps: 1) the decomposition of the intrinsic and reflex pathways and 2) the identification of an impulse response model of the intrinsic pathway and a Hammerstein model of the reflex pathway. Extensive simulation studies demonstrate that SDSS has significant performance advantages over some other methods. Thus, SDSS was more robust under high noise conditions, converging where others failed; it was more accurate, giving estimates with lower bias and random errors. The method also worked well in practice and yielded high-quality estimates of intrinsic and reflex stiffnesses when applied to experimental data at three muscle activation levels. The simulation and experimental results demonstrate that SDSS accurately decomposes the intrinsic and reflex torques and provides accurate estimates of physiologically meaningful parameters. SDSS will be a valuable tool for studying joint stiffness under functionally important conditions. It has important clinical implications for the diagnosis, assessment, objective quantification, and monitoring of neuromuscular diseases that change the muscle tone.

  13. Teaching the Fundamentals of Energy Efficiency

    Science.gov (United States)

    Meier, Alan

    2010-02-01

    A course on energy efficiency is a surprisingly valuable complement to a student's education in physics and many other disciplines. The Univ. of California, Davis, offers a 1-quarter course on ``understanding the other side of the meter.'' Lectures begin by giving students a demand-side perspective on how, where, and why energy is used. Students measure energy use of appliances in their homes and then report results. This gives students a practical sense of the difference between energy and power and learn how appliances transform energy into useful services. Lectures introduce the types of direct conservation measures--reducing demand, reducing fixed consumptions, and increasing efficiency. Practical examples draw upon simple concepts in heat transfer, thermodynamics, and mechanics. Graphical techniques, strengthened through problem sets, explain the interdependence of conservation measures. Lectures then examine indirect energy savings from measures and consider questions like ``where can one achieve the greatest fuel savings in a car by removing one gram of mass?'' Finally, students learn about conservation measures that circumvent physical limits by adopting new processes. By the end of the course, students have a gained a new perspective on energy consumption and the opportunities to reduce it. )

  14. Wide-angle full-vector beam propagation method based on an alternating direction implicit preconditioner

    Science.gov (United States)

    Chui, Siu Lit; Lu, Ya Yan

    2004-03-01

    Wide-angle full-vector beam propagation methods (BPMs) for three-dimensional wave-guiding structures can be derived on the basis of rational approximants of a square root operator or its exponential (i.e., the one-way propagator). While the less accurate BPM based on the slowly varying envelope approximation can be efficiently solved by the alternating direction implicit (ADI) method, the wide-angle variants involve linear systems that are more difficult to handle. We present an efficient solver for these linear systems that is based on a Krylov subspace method with an ADI preconditioner. The resulting wide-angle full-vector BPM is used to simulate the propagation of wave fields in a Y branch and a taper.

  15. Fundamental Design Principles for Transcription-Factor-Based Metabolite Biosensors.

    Science.gov (United States)

    Mannan, Ahmad A; Liu, Di; Zhang, Fuzhong; Oyarzún, Diego A

    2017-10-20

    Metabolite biosensors are central to current efforts toward precision engineering of metabolism. Although most research has focused on building new biosensors, their tunability remains poorly understood and is fundamental for their broad applicability. Here we asked how genetic modifications shape the dose-response curve of biosensors based on metabolite-responsive transcription factors. Using the lac system in Escherichia coli as a model system, we built promoter libraries with variable operator sites that reveal interdependencies between biosensor dynamic range and response threshold. We developed a phenomenological theory to quantify such design constraints in biosensors with various architectures and tunable parameters. Our theory reveals a maximal achievable dynamic range and exposes tunable parameters for orthogonal control of dynamic range and response threshold. Our work sheds light on fundamental limits of synthetic biology designs and provides quantitative guidelines for biosensor design in applications such as dynamic pathway control, strain optimization, and real-time monitoring of metabolism.

  16. Peaks and Valleys : Experimental Asset Markets With Non-Monotonic Fundamentals

    NARCIS (Netherlands)

    Noussair, C.N.; Powell, O.R.

    2008-01-01

    We report the results of an experiment designed to measure how well asset market prices track fundamentals when the latter experience peaks and troughs. We observe greater price efficiency in markets in which fundamentals rise to a peak and then decline, than in markets in which fundamentals decline

  17. Quantum Gate Operations in Decoherence-Free Subspace with Superconducting Charge Qubits inside a Cavity

    International Nuclear Information System (INIS)

    Yi-Min, Wang; Yan-Li, Zhou; Lin-Mei, Liang; Cheng-Zu, Li

    2009-01-01

    We propose a feasible scheme to achieve universal quantum gate operations in decoherence-free subspace with superconducting charge qubits placed in a microwave cavity. Single-logic-qubit gates can be realized with cavity assisted interaction, which possesses the advantages of unconventional geometric gate operation. The two-logic-qubit controlled-phase gate between subsystems can be constructed with the help of a variable electrostatic transformer. The collective decoherence can be successfully avoided in our well-designed system. Moreover, GHZ state for logical qubits can also be easily produced in this system

  18. Hybrid Fundamental Solution Based Finite Element Method: Theory and Applications

    Directory of Open Access Journals (Sweden)

    Changyong Cao

    2015-01-01

    Full Text Available An overview on the development of hybrid fundamental solution based finite element method (HFS-FEM and its application in engineering problems is presented in this paper. The framework and formulations of HFS-FEM for potential problem, plane elasticity, three-dimensional elasticity, thermoelasticity, anisotropic elasticity, and plane piezoelectricity are presented. In this method, two independent assumed fields (intraelement filed and auxiliary frame field are employed. The formulations for all cases are derived from the modified variational functionals and the fundamental solutions to a given problem. Generation of elemental stiffness equations from the modified variational principle is also described. Typical numerical examples are given to demonstrate the validity and performance of the HFS-FEM. Finally, a brief summary of the approach is provided and future trends in this field are identified.

  19. Fast LCMV-based Methods for Fundamental Frequency Estimation

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Glentis, George-Othon; Christensen, Mads Græsbøll

    2013-01-01

    peaks and require matrix inversions for each point in the search grid. In this paper, we therefore consider fast implementations of LCMV-based fundamental frequency estimators, exploiting the estimators' inherently low displacement rank of the used Toeplitz-like data covariance matrices, using...... with several orders of magnitude, but, as we show, further computational savings can be obtained by the adoption of an approximative IAA-based data covariance matrix estimator, reminiscent of the recently proposed Quasi-Newton IAA technique. Furthermore, it is shown how the considered pitch estimators can...... as such either the classic time domain averaging covariance matrix estimator, or, if aiming for an increased spectral resolution, the covariance matrix resulting from the application of the recent iterative adaptive approach (IAA). The proposed exact implementations reduce the required computational complexity...

  20. Integrating fundamental movement skills in late childhood.

    Science.gov (United States)

    Gimenez, Roberto; Manoel, Edison de J; de Oliveira, Dalton Lustosa; Dantas, Luiz; Marques, Inara

    2012-04-01

    The study examined how children of different ages integrate fundamental movement skills, such as running and throwing, and whether their developmental status was related to the combination of these skills. Thirty children were divided into three groups (G1 = 6-year-olds, G2 = 9-year-olds, and G3 = 12-year-olds) and filmed performing three tasks: running, overarm throwing, and the combined task. Patterns were identified and described, and the efficiency of integration was calculated (distance differences of the ball thrown in two tasks, overarm throwing and combined task). Differences in integration were related to age: the 6-year-olds were less efficient in combining the two skills than the 9- and 12-year-olds. These differences may be indicative of a phase of integrating fundamental movement skills in the developmental sequence. This developmental status, particularly throwing, seems to be related to the competence to integrate skills, which suggests that fundamental movement skills may be developmental modules.

  1. Fundamentals of gas dynamics

    CERN Document Server

    Babu, V

    2014-01-01

    Fundamentals of Gas Dynamics, Second Edition isa comprehensively updated new edition and now includes a chapter on the gas dynamics of steam. It covers the fundamental concepts and governing equations of different flows, and includes end of chapter exercises based on the practical applications. A number of useful tables on the thermodynamic properties of steam are also included.Fundamentals of Gas Dynamics, Second Edition begins with an introduction to compressible and incompressible flows before covering the fundamentals of one dimensional flows and normal shock wav

  2. Necessary and sufficient condition for distillability with unit fidelity from finite copies of a mixed state: The most efficient purification protocol

    International Nuclear Information System (INIS)

    Chen Pingxing; Liang Linmei; Li Chengzu; Huang Mingqiu

    2002-01-01

    It is well known that any entangled mixed state in 2x2 systems can be purified via infinite copies of the mixed state. But, can one distill a pure maximally entangled state from finite copies of a mixed state in any bipartite system by local operation and classical communication? This is more meaningful in practical application. We give a necessary and sufficient condition for this distillability. This condition requires that there exist distillable subspaces. According to this condition, one can judge easily whether a mixed state is distillable or not. We also analyze some properties of distillable subspaces, and discuss the most efficient purification protocol. Finally, we discuss the distillable enanglement of a two-qubit system for the case of finite copies

  3. Introduction to the spectral distribution method. Application example to the subspaces with a large number of quasi particles

    International Nuclear Information System (INIS)

    Arvieu, R.

    The assumptions and principles of the spectral distribution method are reviewed. The object of the method is to deduce information on the nuclear spectra by constructing a frequency function which has the same first few moments, as the exact frequency function, these moments being then exactly calculated. The method is applied to subspaces containing a large number of quasi particles [fr

  4. Output-only cyclo-stationary linear-parameter time-varying stochastic subspace identification method for rotating machinery and spinning structures

    Science.gov (United States)

    Velazquez, Antonio; Swartz, R. Andrew

    2015-02-01

    stochastic subspace identification (SSI) and linear parameter time-varying (LPTV) techniques. Structural response is assumed to be stationary ambient excitation produced by a Gaussian (white) noise within the operative range bandwidth of the machinery or structure in study. ERA-OKID analysis is driven by correlation-function matrices from the stationary ambient response aiming to reduce noise effects. Singular value decomposition (SVD) and eigenvalue analysis are computed in a last stage to identify frequencies and complex-valued mode shapes. Proposed assumptions are carefully weighted to account for the uncertainty of the environment. A numerical example is carried out based a spinning finite element (SFE) model, and verified using ANSYS® Ver. 12. Finally, comments and observations are provided on how this subspace realization technique can be extended to the problem of modal-parameter identification using only ambient vibration data.

  5. Survey on efficient linear solvers for porous media flow models on recent hardware architectures

    International Nuclear Information System (INIS)

    Anciaux-Sedrakian, Ani; Gratien, Jean-Marc; Guignon, Thomas; Gottschling, Peter

    2014-01-01

    In the past few years, High Performance Computing (HPC) technologies led to General Purpose Processing on Graphics Processing Units (GPGPU) and many-core architectures. These emerging technologies offer massive processing units and are interesting for porous media flow simulators may used for CO 2 geological sequestration or Enhanced Oil Recovery (EOR) simulation. However the crucial point is 'are current algorithms and software able to use these new technologies efficiently?' The resolution of large sparse linear systems, almost ill-conditioned, constitutes the most CPU-consuming part of such simulators. This paper proposes a survey on various solver and pre-conditioner algorithms, analyzes their efficiency and performance regarding these distinct architectures. Furthermore it proposes a novel approach based on a hybrid programming model for both GPU and many-core clusters. The proposed optimization techniques are validated through a Krylov subspace solver; BiCGStab and some pre-conditioners like ILU0 on GPU, multi-core and many-core architectures, on various large real study cases in EOR simulation. (authors)

  6. Damage location and quantification of a pretensioned concrete beam using stochastic subspace identification

    Science.gov (United States)

    Cancelli, Alessandro; Micheli, Laura; Laflamme, Simon; Alipour, Alice; Sritharan, Sri; Ubertini, Filippo

    2017-04-01

    Stochastic subspace identification (SSID) is a first-order linear system identification technique enabling modal analysis through the time domain. Research in the field of structural health monitoring has demonstrated that SSID can be used to successfully retrieve modal properties, including modal damping ratios, using output-only measurements. In this paper, the utilization of SSID for indirectly retrieving structures' stiffness matrix was investigated, through the study of a simply supported reinforced concrete beam subjected to dynamic loads. Hence, by introducing a physical model of the structure, a second-order identification method is achieved. The reconstruction is based on system condensation methods, which enables calculation of reduced order stiffness, damping, and mass matrices for the structural system. The methods compute the reduced order matrices directly from the modal properties, obtained through the use of SSID. Lastly, the reduced properties of the system are used to reconstruct the stiffness matrix of the beam. The proposed approach is first verified through numerical simulations and then validated using experimental data obtained from a full-scale reinforced concrete beam that experienced progressive damage. Results show that the SSID technique can be used to diagnose, locate, and quantify damage through the reconstruction of the stiffness matrix.

  7. Generic mechanism of optimal energy transfer efficiency: a scaling theory of the mean first-passage time in exciton systems.

    Science.gov (United States)

    Wu, Jianlan; Silbey, Robert J; Cao, Jianshu

    2013-05-17

    An asymptotic scaling theory is presented using the conceptual basis of trapping-free subspace (i.e., orthogonal subspace) to establish the generic mechanism of optimal efficiency of excitation energy transfer in light-harvesting systems. A quantum state orthogonal to the trap will exhibit noise-assisted transfer, clarifying the significance of initial preparation. For such an initial state, the efficiency is enhanced in the weak damping limit (⟨t⟩ ∼ 1/Γ), and suppressed in the strong damping limit (⟨t⟩ ∼ Γ), analogous to Kramers turnover in classical rate theory. An interpolating expression ⟨t⟩ = A/Γ + B + CΓ quantitatively describes the trapping time over the entire range of the dissipation strength, and predicts the optimal efficiency at Γ(opt) ∼ J for homogenous systems. In the presence of static disorder, the scaling law of transfer time with respect to dephasing rate changes from linear to square root, suggesting a weaker dependence on the environment. The prediction of the scaling theory is verified in a symmetric dendrimer system by numerically exact quantum calculations. Though formulated in the context of excitation energy transfer, the analysis and conclusions apply in general to open quantum processes, including electron transfer, fluorescence emission, and heat conduction.

  8. The implicit restarted Arnoldi method, an efficient alternative to solve the neutron diffusion equation

    Energy Technology Data Exchange (ETDEWEB)

    Verdu, G.; Miro, R. [Departamento de Ingenieria Quimica y Nuclear, Universidad Politecnica de Valencia, Valencia (Spain); Ginestar, D. [Departamento de Matematica Aplicada, Universidad Politecnica de Valencia, Valencia (Spain); Vidal, V. [Departamento de Sistemas Informaticos y Computacion, Universidad Politecnica de Valencia, Valencia (Spain)

    1999-05-01

    To calculate the neutronic steady state of a nuclear power reactor core and its subcritical modes, it is necessary to solve a partial eigenvalue problem. In this paper, an implicit restarted Arnoldi method is presented as an advantageous alternative to classical methods as the Power Iteration method and the Subspace Iteration method. The efficiency of these methods, has been compared calculating the dominant Lambda modes of several configurations of the Three Mile Island reactor core.

  9. Islamic fundamentalism in Indonesia

    OpenAIRE

    Nagy, Sandra L.

    1996-01-01

    This is a study of Islamic fundamentalism in Indonesia. Islamic fundamentalism is defined as the return to the foundations and principles of Islam including all movements based on the desire to create a more Islamic society. After describing the practices and beliefs of Islam, this thesis examines the three aspects of universal Islamic fundamentalism: revivalism, resurgence, and radicalism. It analyzes the role of Islam in Indonesia under Dutch colonial rule, an alien Christian imperialist po...

  10. Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator

    Directory of Open Access Journals (Sweden)

    Ronald Phlypo

    2007-01-01

    Full Text Available To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power. It is shown that the JSSE is able to estimate a component from simulated data that is superior with respect to methodological artefact suppression to those of FastICA, SOBI, pSVD, or JADE/COM1 algorithms used for blind source separation (BSS. Interference and distortion suppression are of comparable order when compared with the above-mentioned methods. Results on patient data demonstrate that the method is able to suppress blinking and saccade artefacts in a fully automated way.

  11. Parallelised Krylov subspace method for reactor kinetics by IQS approach

    International Nuclear Information System (INIS)

    Gupta, Anurag; Modak, R.S.; Gupta, H.P.; Kumar, Vinod; Bhatt, K.

    2005-01-01

    Nuclear reactor kinetics involves numerical solution of space-time-dependent multi-group neutron diffusion equation. Two distinct approaches exist for this purpose: the direct (implicit time differencing) approach and the improved quasi-static (IQS) approach. Both the approaches need solution of static space-energy-dependent diffusion equations at successive time-steps; the step being relatively smaller for the direct approach. These solutions are usually obtained by Gauss-Seidel type iterative methods. For a faster solution, the Krylov sub-space methods have been tried and also parallelised by many investigators. However, these studies seem to have been done only for the direct approach. In the present paper, parallelised Krylov methods are applied to the IQS approach in addition to the direct approach. It is shown that the speed-up obtained for IQS is higher than that for the direct approach. The reasons for this are also discussed. Thus, the use of IQS approach along with parallelised Krylov solvers seems to be a promising scheme

  12. Joint fundamental frequency and order estimation using optimal filtering

    Directory of Open Access Journals (Sweden)

    Jakobsson Andreas

    2011-01-01

    Full Text Available Abstract In this paper, the problem of jointly estimating the number of harmonics and the fundamental frequency of periodic signals is considered. We show how this problem can be solved using a number of methods that either are or can be interpreted as filtering methods in combination with a statistical model selection criterion. The methods in question are the classical comb filtering method, a maximum likelihood method, and some filtering methods based on optimal filtering that have recently been proposed, while the model selection criterion is derived herein from the maximum a posteriori principle. The asymptotic properties of the optimal filtering methods are analyzed and an order-recursive efficient implementation is derived. Finally, the estimators have been compared in computer simulations that show that the optimal filtering methods perform well under various conditions. It has previously been demonstrated that the optimal filtering methods perform extremely well with respect to fundamental frequency estimation under adverse conditions, and this fact, combined with the new results on model order estimation and efficient implementation, suggests that these methods form an appealing alternative to classical methods for analyzing multi-pitch signals.

  13. High-efficiency frequency doubling of continuous-wave laser light.

    Science.gov (United States)

    Ast, Stefan; Nia, Ramon Moghadas; Schönbeck, Axel; Lastzka, Nico; Steinlechner, Jessica; Eberle, Tobias; Mehmet, Moritz; Steinlechner, Sebastian; Schnabel, Roman

    2011-09-01

    We report on the observation of high-efficiency frequency doubling of 1550 nm continuous-wave laser light in a nonlinear cavity containing a periodically poled potassium titanyl phosphate crystal (PPKTP). The fundamental field had a power of 1.10 W and was converted into 1.05 W at 775 nm, yielding a total external conversion efficiency of 95±1%. The latter value is based on the measured depletion of the fundamental field being consistent with the absolute values derived from numerical simulations. According to our model, the conversion efficiency achieved was limited by the nonperfect mode matching into the nonlinear cavity and by the nonperfect impedance matching for the maximum input power available. Our result shows that cavity-assisted frequency conversion based on PPKTP is well suited for low-decoherence frequency conversion of quantum states of light.

  14. Efficient modified Jacobi relaxation for minimizing the energy functional

    International Nuclear Information System (INIS)

    Park, C.H.; Lee, I.; Chang, K.J.

    1993-01-01

    We present an efficient scheme of diagonalizing large Hamiltonian matrices in a self-consistent manner. In the framework of the preconditioned conjugate gradient minimization of the energy functional, we replace the modified Jacobi relaxation for preconditioning and use for band-by-band minimization the restricted-block Davidson algorithm, in which only the previous wave functions and the relaxation vectors are included additionally for subspace diagonalization. Our scheme is found to be comparable with the preconditioned conjugate gradient method for both large ordered and disordered Si systems, while it is more rapidly converged for systems with transition-metal elements

  15. Preconditioners based on the Alternating-Direction-Implicit algorithm for the 2D steady-state diffusion equation with orthotropic heterogeneous coefficients

    KAUST Repository

    Gao, Longfei; Calo, Victor M.

    2015-01-01

    In this paper, we combine the Alternating Direction Implicit (ADI) algorithm with the concept of preconditioning and apply it to linear systems discretized from the 2D steady-state diffusion equations with orthotropic heterogeneous coefficients by the finite element method assuming tensor product basis functions. Specifically, we adopt the compound iteration idea and use ADI iterations as the preconditioner for the outside Krylov subspace method that is used to solve the preconditioned linear system. An efficient algorithm to perform each ADI iteration is crucial to the efficiency of the overall iterative scheme. We exploit the Kronecker product structure in the matrices, inherited from the tensor product basis functions, to achieve high efficiency in each ADI iteration. Meanwhile, in order to reduce the number of Krylov subspace iterations, we incorporate partially the coefficient information into the preconditioner by exploiting the local support property of the finite element basis functions. Numerical results demonstrated the efficiency and quality of the proposed preconditioner. © 2014 Elsevier B.V. All rights reserved.

  16. Perturbed invariant subspaces and approximate generalized functional variable separation solution for nonlinear diffusion-convection equations with weak source

    Science.gov (United States)

    Xia, Ya-Rong; Zhang, Shun-Li; Xin, Xiang-Peng

    2018-03-01

    In this paper, we propose the concept of the perturbed invariant subspaces (PISs), and study the approximate generalized functional variable separation solution for the nonlinear diffusion-convection equation with weak source by the approximate generalized conditional symmetries (AGCSs) related to the PISs. Complete classification of the perturbed equations which admit the approximate generalized functional separable solutions (AGFSSs) is obtained. As a consequence, some AGFSSs to the resulting equations are explicitly constructed by way of examples.

  17. Marketing fundamentals.

    Science.gov (United States)

    Redmond, W H

    2001-01-01

    This chapter outlines current marketing practice from a managerial perspective. The role of marketing within an organization is discussed in relation to efficiency and adaptation to changing environments. Fundamental terms and concepts are presented in an applied context. The implementation of marketing plans is organized around the four P's of marketing: product (or service), promotion (including advertising), place of delivery, and pricing. These are the tools with which marketers seek to better serve their clients and form the basis for competing with other organizations. Basic concepts of strategic relationship management are outlined. Lastly, alternate viewpoints on the role of advertising in healthcare markets are examined.

  18. A massively-parallel electronic-structure calculations based on real-space density functional theory

    International Nuclear Information System (INIS)

    Iwata, Jun-Ichi; Takahashi, Daisuke; Oshiyama, Atsushi; Boku, Taisuke; Shiraishi, Kenji; Okada, Susumu; Yabana, Kazuhiro

    2010-01-01

    Based on the real-space finite-difference method, we have developed a first-principles density functional program that efficiently performs large-scale calculations on massively-parallel computers. In addition to efficient parallel implementation, we also implemented several computational improvements, substantially reducing the computational costs of O(N 3 ) operations such as the Gram-Schmidt procedure and subspace diagonalization. Using the program on a massively-parallel computer cluster with a theoretical peak performance of several TFLOPS, we perform electronic-structure calculations for a system consisting of over 10,000 Si atoms, and obtain a self-consistent electronic-structure in a few hundred hours. We analyze in detail the costs of the program in terms of computation and of inter-node communications to clarify the efficiency, the applicability, and the possibility for further improvements.

  19. Fundamental energy limits of SET-based Brownian NAND and half-adder circuits. Preliminary findings from a physical-information-theoretic methodology

    Science.gov (United States)

    Ercan, İlke; Suyabatmaz, Enes

    2018-06-01

    The saturation in the efficiency and performance scaling of conventional electronic technologies brings about the development of novel computational paradigms. Brownian circuits are among the promising alternatives that can exploit fluctuations to increase the efficiency of information processing in nanocomputing. A Brownian cellular automaton, where signals propagate randomly and are driven by local transition rules, can be made computationally universal by embedding arbitrary asynchronous circuits on it. One of the potential realizations of such circuits is via single electron tunneling (SET) devices since SET technology enable simulation of noise and fluctuations in a fashion similar to Brownian search. In this paper, we perform a physical-information-theoretic analysis on the efficiency limitations in a Brownian NAND and half-adder circuits implemented using SET technology. The method we employed here establishes a solid ground that enables studying computational and physical features of this emerging technology on an equal footing, and yield fundamental lower bounds that provide valuable insights into how far its efficiency can be improved in principle. In order to provide a basis for comparison, we also analyze a NAND gate and half-adder circuit implemented in complementary metal oxide semiconductor technology to show how the fundamental bound of the Brownian circuit compares against a conventional paradigm.

  20. Joint DOA and Fundamental Frequency Estimation Methods based on 2-D Filtering

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Jensen, Søren Holdt

    2010-01-01

    of the fundamental frequency and the DOA of spatio-temporarily sampled periodic signals. The first and simplest method is based on the 2-D periodogram, whereas the second method is a generalization of the 2-D Capon method. In the experimental part, both qualitative and quantitative measurements show that the proposed...

  1. Generation and confirmation of a (100 x 100)-dimensional entangled quantum system.

    Science.gov (United States)

    Krenn, Mario; Huber, Marcus; Fickler, Robert; Lapkiewicz, Radek; Ramelow, Sven; Zeilinger, Anton

    2014-04-29

    Entangled quantum systems have properties that have fundamentally overthrown the classical worldview. Increasing the complexity of entangled states by expanding their dimensionality allows the implementation of novel fundamental tests of nature, and moreover also enables genuinely new protocols for quantum information processing. Here we present the creation of a (100 × 100)-dimensional entangled quantum system, using spatial modes of photons. For its verification we develop a novel nonlinear criterion which infers entanglement dimensionality of a global state by using only information about its subspace correlations. This allows very practical experimental implementation as well as highly efficient extraction of entanglement dimensionality information. Applications in quantum cryptography and other protocols are very promising.

  2. Generation and confirmation of a (100 × 100)-dimensional entangled quantum system

    Science.gov (United States)

    Krenn, Mario; Huber, Marcus; Fickler, Robert; Lapkiewicz, Radek; Ramelow, Sven; Zeilinger, Anton

    2014-01-01

    Entangled quantum systems have properties that have fundamentally overthrown the classical worldview. Increasing the complexity of entangled states by expanding their dimensionality allows the implementation of novel fundamental tests of nature, and moreover also enables genuinely new protocols for quantum information processing. Here we present the creation of a (100 × 100)-dimensional entangled quantum system, using spatial modes of photons. For its verification we develop a novel nonlinear criterion which infers entanglement dimensionality of a global state by using only information about its subspace correlations. This allows very practical experimental implementation as well as highly efficient extraction of entanglement dimensionality information. Applications in quantum cryptography and other protocols are very promising. PMID:24706902

  3. Optimal Design of Large Dimensional Adaptive Subspace Detectors

    KAUST Repository

    Ben Atitallah, Ismail; Kammoun, Abla; Alouini, Mohamed-Slim; Alnaffouri, Tareq Y.

    2016-01-01

    This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

  4. Optimal Design of Large Dimensional Adaptive Subspace Detectors

    KAUST Repository

    Ben Atitallah, Ismail

    2016-05-27

    This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

  5. The fundamental parameter approach of quantitative XRFA- investigation of photoelectric absorption coefficients

    International Nuclear Information System (INIS)

    Shaltout, A.

    2003-06-01

    of edge positions, description of M4 and M5-ranges of elements from Z=61 upwards by sawtooth responses and reduction of data by introduction of least squares fits of fifth order. In order to calculate the mass absorption coefficients from Scofield's data base, coherent and incoherent scattering cross sections have to be taken from Elam's numerical data. This revised version of Scofield's data base is recommended for quantitative x-ray analysis programs. The detector efficiency of both EPMA and EDXRFA was calculated theoretically. Consequently, a comparison between experimental and theoretical x-ray tube spectra at 30 keV indicated the validity of the computed detector efficiencies. A precise calculation of correction factor and solid angle is performed in order to decrease the systematic errors in the fundamental parameter programs. By using the mentioned new set of fundamental parameters, a quantitative analysis of binary and ternary alloys was performed. The comparison between computed and expected compositions confirms the quality of the modified fundamental parameters. (author)

  6. Ressource efficient IT in schools. Options of an energie-efficient and material-efficient use in information technology; Ressourceneffiziente IT in Schulen. Optionen des energie- und materialeffizienten Einsatzes von Informationstechnik (IT)

    Energy Technology Data Exchange (ETDEWEB)

    Clausen, Jens; Fichter, Klaus [Borderstep Insitut, Berlin (Germany)

    2009-12-15

    The number of computers in schools increases continuously. This requires a use of material-efficient and energy-efficient IT technologies. As an alternative to traditional large desktop personal computers (PC), there are three types of computer solutions with a significant improvement: Mini PCs, notebooks and Thin Client and Server Based Computing. Schools need to reflect fundamentally on more material-efficient and more energy-efficient IT solutions and consider the system change to server-based computing as an alternative. Thus, the information and training of IT personnel in schools plays as a central role such as the expansion of the competence of advising and supervising system houses. This is the only way to reduce material costs, energy consumption and administration costs despite an increasing number of computer devices and to exploit existing potentials for resource efficiency.

  7. Current-flow efficiency of networks

    Science.gov (United States)

    Liu, Kai; Yan, Xiaoyong

    2018-02-01

    Many real-world networks, from infrastructure networks to social and communication networks, can be formulated as flow networks. How to realistically measure the transport efficiency of these networks is of fundamental importance. The shortest-path-based efficiency measurement has limitations, as it assumes that flow travels only along those shortest paths. Here, we propose a new metric named current-flow efficiency, in which we calculate the average reciprocal effective resistance between all pairs of nodes in the network. This metric takes the multipath effect into consideration and is more suitable for measuring the efficiency of many real-world flow equilibrium networks. Moreover, this metric can handle a disconnected graph and can thus be used to identify critical nodes and edges from the efficiency-loss perspective. We further analyze how the topological structure affects the current-flow efficiency of networks based on some model and real-world networks. Our results enable a better understanding of flow networks and shed light on the design and improvement of such networks with higher transport efficiency.

  8. Development of Computer-Based Training to Supplement Lessons in Fundamentals of Electronics

    Directory of Open Access Journals (Sweden)

    Ian P. Benitez

    2016-05-01

    Full Text Available Teaching Fundamentals of Electronics allow students to familiarize with basic electronics concepts, acquire skills in the use of multi-meter test instrument, and develop mastery in testing basic electronic components. Actual teaching and doing observations during practical activities on components pin identification and testing showed that the lack of skills of new students in testing components can lead to incorrect fault diagnosis and wrong pin connection during in-circuit replacement of the defective parts. With the aim of reinforcing students with concrete understanding of the concepts of components applied in the actual test and measurement, a Computer-Based Training was developed. The proponent developed the learning modules (courseware utilizing concept mapping and storyboarding instructional design. Developing a courseware as simulated, activity-based and interactive as possible was the primary goal to resemble the real-world process. A Local area network (LAN-based learning management system was also developed to use in administering the learning modules. The Paired Sample T-Test based on the pretest and post-test result was used to determine whether the students achieved learning after taking the courseware. The result revealed that there is a significant achievement of the students after studying the learning module. The E-learning content was validated by the instructors in terms of contents, activities, assessment and format with a grand weighted mean of 4.35 interpreted as Sufficient. Based from the evaluation result, supplementing with the proposed computer-based training can enhance the teachinglearning process in electronic fundamentals.

  9. Radiation-induced segregation: A microchemical gauge to quantify fundamental defect parameters

    International Nuclear Information System (INIS)

    Simonen, E.P.; Bruemmer, S.M.

    1994-12-01

    Defect Kinetic are evaluated for austenitic stainless alloys by comparing model predictions to measured responses for radiation-induced grain boundary segregation. Heavy-ions, neutrons and proton irradiations having substantial statistical bases are examined. The combined modeling and measurement approach is shown to be useful for quantifying fundamental defect parameters. The mechanism evaluation indicates vacancy, migration energies of 1.15 eV or less and a vacancy formation energy at grain boundaries of 1.5 eV. Damage efficiencies of about 0.03 were established for heavy-ions and for light-water reactor neutrons. Inferred proton damage efficiencies were about 0.15. Segregation measured in an advanced gas-cooled reactor component was much greater than expected using the above parameters

  10. Resource allocation based on cost efficiency

    DEFF Research Database (Denmark)

    Dehnokhalaji, Akram; Ghiyasi, Mojtaba; Korhonen, Pekka

    2017-01-01

    -objective linear programming problem using two different strategies. First, we propose an RA model which keeps the cost efficiencies of units unchanged. This is done assuming fixed technical and allocative efficiencies. The approach is based on the assumption that the decision maker (DM) may not have big changes......In this paper, we consider a resource allocation (RA) problem and develop an approach based on cost (overall) efficiency. The aim is to allocate some inputs among decision making units (DMUs) in such way that their cost efficiencies improve or stay unchanged after RA. We formulate a multi...... in the structure of DMUs within a short term. The second strategy does not impose any restrictions on technical and allocative efficiencies. It guarantees that none of the cost efficiencies of DMUs get worse after RA, and the improvement for units is possible if it is feasible and beneficial. Two numerical...

  11. Fundamentals of electromagnetic levitation engineering sustainability through efficiency

    CERN Document Server

    Sangster, Alan J

    2012-01-01

    This book aims to make aspiring and existing electrical engineers aware of the efficiency implications of frictionless machines and how important this may be in a post fossil-fuel world in which the energy available from renewable sources is strictly limited.

  12. Generalized subspace correction methods

    Energy Technology Data Exchange (ETDEWEB)

    Kolm, P. [Royal Institute of Technology, Stockholm (Sweden); Arbenz, P.; Gander, W. [Eidgenoessiche Technische Hochschule, Zuerich (Switzerland)

    1996-12-31

    A fundamental problem in scientific computing is the solution of large sparse systems of linear equations. Often these systems arise from the discretization of differential equations by finite difference, finite volume or finite element methods. Iterative methods exploiting these sparse structures have proven to be very effective on conventional computers for a wide area of applications. Due to the rapid development and increasing demand for the large computing powers of parallel computers, it has become important to design iterative methods specialized for these new architectures.

  13. An efficient diagnostic technique for distribution systems based on under fault voltages and currents

    Energy Technology Data Exchange (ETDEWEB)

    Campoccia, A.; Di Silvestre, M.L.; Incontrera, I.; Riva Sanseverino, E. [Dipartimento di Ingegneria Elettrica elettronica e delle Telecomunicazioni, Universita degli Studi di Palermo, viale delle Scienze, 90128 Palermo (Italy); Spoto, G. [Centro per la Ricerca Elettronica in Sicilia, Monreale, Via Regione Siciliana 49, 90046 Palermo (Italy)

    2010-10-15

    Service continuity is one of the major aspects in the definition of the quality of the electrical energy, for this reason the research in the field of faults diagnostic for distribution systems is spreading ever more. Moreover the increasing interest around modern distribution systems automation for management purposes gives faults diagnostics more tools to detect outages precisely and in short times. In this paper, the applicability of an efficient fault location and characterization methodology within a centralized monitoring system is discussed. The methodology, appropriate for any kind of fault, is based on the use of the analytical model of the network lines and uses the fundamental components rms values taken from the transient measures of line currents and voltages at the MV/LV substations. The fault location and identification algorithm, proposed by the authors and suitably restated, has been implemented on a microprocessor-based device that can be installed at each MV/LV substation. The speed and precision of the algorithm have been tested against the errors deriving from the fundamental extraction within the prescribed fault clearing times and against the inherent precision of the electronic device used for computation. The tests have been carried out using Matlab Simulink for simulating the faulted system. (author)

  14. Efficient response spectrum analysis of a reactor using Model Order Reduction

    International Nuclear Information System (INIS)

    Oh, Jin Ho; Choi, Jin Bok; Ryu, Jeong Soo

    2012-01-01

    A response spectrum analysis (RSA) has been widely used to evaluate the structural integrity of various structural components in the nuclear industry. However, solving the large and complex structural systems numerically using the RSA requires a considerable amount of computational resources and time. To overcome this problem, this paper proposes the RSA based on the model order reduction (MOR) technique achieved by applying a projection from a higher order to a lower order space using Krylov subspaces generated by the Arnoldi algorithm. The dynamic characteristics of the final reduced system are almost identical with those of the full system by matching the moments of the reduced system with those of the full system up to the required nth order. It is remarkably efficient in terms of computation time and does not require a global system. Numerical examples demonstrate that the proposed method saves computational costs effectively, and provides a reduced system framework that predicts the accurate responses of a global system

  15. An Angle Criterion for Riesz Bases

    DEFF Research Database (Denmark)

    Lindner, Alexander M; Bittner, B.

    1999-01-01

    We present a characterization of Riesz bases in terms ofthe angles between certain finite dimensional subspaces. Correlationsbetween the bounds of the Riesz basis and the size of the angles arederived....

  16. Land Prices and Fundamentals

    OpenAIRE

    Koji Nakamura; Yumi Saita

    2007-01-01

    This paper examines the long-term relationship between macro economic fundamentals and the weighted-average land price indicators, which are supposed to be more appropriate than the official land price indicators when analyzing their impacts on the macro economy. In many cases, we find the cointegrating relationships between the weighted-average land price indicators and the discounted present value of land calculated based on the macro economic fundamentals indicators. We also find that the ...

  17. Smart grids fundamentals and technologies in electricity networks

    CERN Document Server

    Buchholz, Bernd M

    2014-01-01

    Efficient transmission and distribution of electricity is a fundamental requirement for sustainable development and prosperity. The world is facing great challenges regarding the reliable grid integration of renewable energy sources in the 21st century. The electric power systems of the future require fundamental innovations and enhancements to meet these challenges. The European Union's "Smart Grid" vision provides a first overview of the appropriate deep-paradigm changes in the transmission, distribution and supply of electricity.The book brings together common themes beginning with Smart Gr

  18. A fundamental parameter-based calibration model for an intrinsic germanium X-ray fluorescence spectrometer

    International Nuclear Information System (INIS)

    Christensen, L.H.; Pind, N.

    1982-01-01

    A matrix-independent fundamental parameter-based calibration model for an energy-dispersive X-ray fluorescence spectrometer has been developed. This model, which is part of a fundamental parameter approach quantification method, accounts for both the excitation and detection probability. For each secondary target a number of relative calibration constants are calculated on the basis of knowledge of the irradiation geometry, the detector specifications, and tabulated fundamental physical parameters. The absolute calibration of the spectrometer is performed by measuring one pure element standard per secondary target. For sample systems where all elements can be analyzed by means of the same secondary target the absolute calibration constant can be determined during the iterative solution of the basic equation. Calculated and experimentally determined relative calibration constants agree to within 5-10% of each other and so do the results obtained from the analysis of an NBS certified alloy using the two sets of constants. (orig.)

  19. An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

    Directory of Open Access Journals (Sweden)

    Dongdong Yang

    2015-02-01

    Full Text Available Synthetic aperture radar (SAR image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous nonlocal mean (NLM filter is introduced in this study to suppress the multiplicative speckle noise in SAR image. Furthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the searching window are projected into a lower dimensional subspace by principal component analysis (PCA. Thus, the nonlocal mean filter is implemented in the subspace. Afterwards, a multi-objective clustering algorithm is proposed using the principals of artificial immune system (AIS and kernel-induced distance measures. The multi-objective clustering has been shown to discover the data distribution with different characteristics and the kernel methods can improve its robustness to noise and outliers. Experiments demonstrate that the proposed method is able to partition the SAR image robustly and accurately than the conventional approaches.

  20. Chemical Industry R&D Roadmap for Nanomaterials By Design. From Fundamentals to Function

    Energy Technology Data Exchange (ETDEWEB)

    none,

    2003-12-01

    Vision2020 agreed to join NNI and the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (DOE/EERE) in sponsoring the "Nanomaterials and the Chemical Industry Roadmap Workshop" on September 30-October 2, 2002. This roadmap, Chemical Industry R&D Roadmap for Nanomaterials By Design: From Fundamentals to Function, is based on the scientific priorities expressed by workshop participants from the chemical industry, universities, and government laboratories.

  1. Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

    Science.gov (United States)

    Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

    In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

  2. Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle

    Directory of Open Access Journals (Sweden)

    Junpeng Shi

    2017-02-01

    Full Text Available In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS method for two-dimensional direction of arrival (2D DOA estimation with uniform rectangular arrays (URAs in a low-grazing angle (LGA condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions.

  3. Fundamental ecology is fundamental.

    Science.gov (United States)

    Courchamp, Franck; Dunne, Jennifer A; Le Maho, Yvon; May, Robert M; Thébaud, Christophe; Hochberg, Michael E

    2015-01-01

    The primary reasons for conducting fundamental research are satisfying curiosity, acquiring knowledge, and achieving understanding. Here we develop why we believe it is essential to promote basic ecological research, despite increased impetus for ecologists to conduct and present their research in the light of potential applications. This includes the understanding of our environment, for intellectual, economical, social, and political reasons, and as a major source of innovation. We contend that we should focus less on short-term, objective-driven research and more on creativity and exploratory analyses, quantitatively estimate the benefits of fundamental research for society, and better explain the nature and importance of fundamental ecology to students, politicians, decision makers, and the general public. Our perspective and underlying arguments should also apply to evolutionary biology and to many of the other biological and physical sciences. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background

    Directory of Open Access Journals (Sweden)

    Ding Hao

    2017-06-01

    Full Text Available Considering an inverse Gamma prior distribution model for texture, the adaptive detection problems for both first order Gaussian and second order Gaussian subspace targets are researched in a compound Gaussian sea clutter. Test statistics are derived on the basis of the two-step generalized likelihood ratio test. From these tests, new adaptive detectors are proposed by substituting the covariance matrix with estimation results from the Sample Covariance Matrix (SCM, normalized SCM, and fixed point estimator. The proposed detectors consider the prior distribution model for sea clutter during the design stage, and they model parameters that match the working environment during the detection stage. Analysis and validation results indicate that the detection performance of the proposed detectors out performs existing AMF (Adaptive Matched Filter, AMF and ANMF (Adaptive Normalized Matched Filter, ANMF detectors.

  5. Fundamentals of structural dynamics

    CERN Document Server

    Craig, Roy R

    2006-01-01

    From theory and fundamentals to the latest advances in computational and experimental modal analysis, this is the definitive, updated reference on structural dynamics.This edition updates Professor Craig's classic introduction to structural dynamics, which has been an invaluable resource for practicing engineers and a textbook for undergraduate and graduate courses in vibrations and/or structural dynamics. Along with comprehensive coverage of structural dynamics fundamentals, finite-element-based computational methods, and dynamic testing methods, this Second Edition includes new and e

  6. Efficient boiler operations sourcebook

    Energy Technology Data Exchange (ETDEWEB)

    Payne, F.W. (comp.)

    1985-01-01

    This book emphasizes the practical aspects of industrial and commercial boiler operations. It starts with a comprehensive review of general combustion and boiler fundamentals and then deals with specific efficiency improvement methods, and the cost savings which result. The book has the following chapter headings: boiler combustion fundamentals; boiler efficiency goals; major factors controlling boiler efficiency; boiler efficiency calculations; heat loss; graphical solutions; preparation for boiler testing; boiler test procedures; efficiency-related boiler maintenance procedures; boiler tune-up; boiler operational modifications; effect of water side and gas side scale deposits; load management; auxillary equipment to increase boiler efficiency; air preheaters and economizers; other types of auxillary equipment; combustion control systems and instrumentation; boiler O/sub 2/ trim controls; should you purchase a new boiler.; financial evaluation procedures; case studies. The last chapter includes a case study of a boiler burning pulverized coal and a case study of stoker-fired coal.

  7. Fundamental study of droplet spray characteristics in photomask cleaning for advanced lithography

    Science.gov (United States)

    Lu, C. L.; Yu, C. H.; Liu, W. H.; Hsu, Luke; Chin, Angus; Lee, S. C.; Yen, Anthony; Lee, Gaston; Dress, Peter; Singh, Sherjang; Dietze, Uwe

    2010-09-01

    The fundamentals of droplet-based cleaning of photomasks are investigated and performance regimes that enable the use of binary spray technologies in advanced mask cleaning are identified. Using phase Doppler anemometry techniques, the effect of key performance parameters such as liquid and gas flow rates and temperature, nozzle design, and surface distance on droplet size, velocity, and distributions were studied. The data are correlated to particle removal efficiency (PRE) and feature damage results obtained on advanced photomasks for 193-nm immersion lithography.

  8. Maximum Efficiency per Torque Control of Permanent-Magnet Synchronous Machines

    Directory of Open Access Journals (Sweden)

    Qingbo Guo

    2016-12-01

    Full Text Available High-efficiency permanent-magnet synchronous machine (PMSM drive systems need not only optimally designed motors but also efficiency-oriented control strategies. However, the existing control strategies only focus on partial loss optimization. This paper proposes a novel analytic loss model of PMSM in either sine-wave pulse-width modulation (SPWM or space vector pulse width modulation (SVPWM which can take into account both the fundamental loss and harmonic loss. The fundamental loss is divided into fundamental copper loss and fundamental iron loss which is estimated by the average flux density in the stator tooth and yoke. In addition, the harmonic loss is obtained from the Bertotti iron loss formula by the harmonic voltages of the three-phase inverter in either SPWM or SVPWM which are calculated by double Fourier integral analysis. Based on the analytic loss model, this paper proposes a maximum efficiency per torque (MEPT control strategy which can minimize the electromagnetic loss of PMSM in the whole operation range. As the loss model of PMSM is too complicated to obtain the analytical solution of optimal loss, a golden section method is applied to achieve the optimal operation point accurately, which can make PMSM work at maximum efficiency. The optimized results between SPWM and SVPWM show that the MEPT in SVPWM has a better effect on the optimization performance. Both the theory analysis and experiment results show that the MEPT control can significantly improve the efficiency performance of the PMSM in each operation condition with a satisfied dynamic performance.

  9. Mean of the typical decoding rates: a new translation efficiency index based on the analysis of ribosome profiling data.

    Science.gov (United States)

    Dana, Alexandra; Tuller, Tamir

    2014-12-01

    Gene translation modeling and prediction is a fundamental problem that has numerous biomedical implementations. In this work we present a novel, user-friendly tool/index for calculating the mean of the typical decoding rates that enables predicting translation elongation efficiency of protein coding genes for different tissue types, developmental stages, and experimental conditions. The suggested translation efficiency index is based on the analysis of the organism's ribosome profiling data. This index could be used for example to predict changes in translation elongation efficiency of lowly expressed genes that usually have relatively low and/or biased ribosomal densities and protein levels measurements, or can be used for example for predicting translation efficiency of new genetically engineered genes. We demonstrate the usability of this index via the analysis of six organisms in different tissues and developmental stages. Distributable cross platform application and guideline are available for download at: http://www.cs.tau.ac.il/~tamirtul/MTDR/MTDR_Install.html. Copyright © 2015 Dana and Tuller.

  10. Fundamental Perspectives on Supply Chain Management

    NARCIS (Netherlands)

    Omta, S.W.F.; Hoenen, S.J.

    2012-01-01

    The aim of the present literature study is to find the fundamental perspectives/models in the realm of supply chain management and to investigate whether they can be extended based on recent literature findings. The fundamental perspectives were found using a two-tier snowball collection method,

  11. Fault Detection Algorithm based on Null-Space Analysis for On-Line Structural Health Monitoring

    OpenAIRE

    Yan, Ai-Min; Golinval, Jean-Claude; Marin, Frédéric

    2005-01-01

    Early diagnosis of structural damages or machinery malfunctions allows to reduce the maintenance cost of systems and to increase their reliability and safety. This paper addresses the damage detection problem by statistical analysis on output-only measurements of structures. The developed method is based on subspace analysis of the Hankel matrices constructed by vibration measurement data. The column active subspace of the Hankel matrix defined by the first principal components is orthonormal...

  12. Country Fundamentals and Currency Excess Returns

    Directory of Open Access Journals (Sweden)

    Daehwan Kim

    2014-06-01

    Full Text Available We examine whether country fundamentals help explain the cross-section of currency excess returns. For this purpose, we consider fundamental variables such as default risk, foreign exchange rate regime, capital control as well as interest rate in the multi-factor model framework. Our empirical results show that fundamental factors explain a large part of the cross-section of currency excess returns. The zero-intercept restriction of the factor model is not rejected for most currencies. They also reveal that our factor model with country fundamentals performs better than a factor model with usual investment-style factors. Our main empirical results are based on 2001-2010 balanced panel data of 19 major currencies. This paper may fill the gap between country fundamentals and practitioners' strategies on currency investment.

  13. Model-based Clustering of High-Dimensional Data in Astrophysics

    Science.gov (United States)

    Bouveyron, C.

    2016-05-01

    The nature of data in Astrophysics has changed, as in other scientific fields, in the past decades due to the increase of the measurement capabilities. As a consequence, data are nowadays frequently of high dimensionality and available in mass or stream. Model-based techniques for clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappointing behavior in high-dimensional spaces which is mainly due to their dramatical over-parametrization. The recent developments in model-based classification overcome these drawbacks and allow to efficiently classify high-dimensional data, even in the "small n / large p" situation. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in Astrophysics using R packages.

  14. Fundamental Issues in Manufacturing Photovoltaic Modules Beyond the Current Generation of Materials

    Directory of Open Access Journals (Sweden)

    G. F. Alapatt

    2012-01-01

    Full Text Available Many methods to improve the solar cell’s efficiency beyond current generation of bulk and thin film of photovoltaic (PV devices have been reported during the last five decades. Concepts such as multiple exciton generations (MEG, carrier multiplication (CM, hot carrier extraction, and intermediate band solar cells have fundamental flaws, and there is no experimental evidence of fabricating practical higher efficiency solar cells based on the proposed concepts. To take advantages of quantum features of nanostructures for higher performance PV devices, self-assembly-based bottom-up processing techniques are not suitable for manufacturing due to inherent problems of variability, defects, reliability, and yield. For processing nanostructures, new techniques need to be invented with the features of critical dimensional control, structural homogeneity, and lower cost of ownership as compared to the processing tools used in current generations of bulk and thin-film solar cells.

  15. Designs of two and three cavity gyroklystron amplifiers operating at fundamental, second, and fourth harmonics

    International Nuclear Information System (INIS)

    Saraph, G.P.; Lawson, W.; Latham, P.E.; Cheng, J.; Castle, M.

    1995-01-01

    Two and three cavity, co-axial, relativistic gyroklystron amplifiers are investigated for driving future linear colliders. Detailed designs of gyroklystrons operating at fundamental (8.568 GHz), second (17.136 GHz), and fourth harmonic (34.272 GHz) frequencies are presented. Numerical simulations predict over 40% efficiency, 45-50 dB gain, and 100-160 MW power level for the fundamental and second harmonic designs. It is shown that introducing a penultimate (buncher) cavity significantly improves efficiency and gain of the second harmonic amplifier. The fourth harmonic design has a modest efficiency of 10-15%

  16. Adaptation hypothesis of biological efficiency of ionizing radiation

    International Nuclear Information System (INIS)

    Kudritskij, Yu.K.; Georgievskij, A.B.; Karpov, V.I.

    1992-01-01

    Adaptation hypothesis of biological efficiency of ionizing radiation is based on acknowledgement of invariance of fundamental laws and principles of biology related to unity of biota and media, evolution and adaptation for radiobiology. The basic arguments for adaptation hypothesis validity, its correspondence to the requirements imposed on scientific hypothes are presented

  17. High-accuracy mass spectrometry for fundamental studies.

    Science.gov (United States)

    Kluge, H-Jürgen

    2010-01-01

    Mass spectrometry for fundamental studies in metrology and atomic, nuclear and particle physics requires extreme sensitivity and efficiency as well as ultimate resolving power and accuracy. An overview will be given on the global status of high-accuracy mass spectrometry for fundamental physics and metrology. Three quite different examples of modern mass spectrometric experiments in physics are presented: (i) the retardation spectrometer KATRIN at the Forschungszentrum Karlsruhe, employing electrostatic filtering in combination with magnetic-adiabatic collimation-the biggest mass spectrometer for determining the smallest mass, i.e. the mass of the electron anti-neutrino, (ii) the Experimental Cooler-Storage Ring at GSI-a mass spectrometer of medium size, relative to other accelerators, for determining medium-heavy masses and (iii) the Penning trap facility, SHIPTRAP, at GSI-the smallest mass spectrometer for determining the heaviest masses, those of super-heavy elements. Finally, a short view into the future will address the GSI project HITRAP at GSI for fundamental studies with highly-charged ions.

  18. Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

    Science.gov (United States)

    Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

    2016-07-07

    Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

  19. A Case-Based Reasoning Method with Rank Aggregation

    Science.gov (United States)

    Sun, Jinhua; Du, Jiao; Hu, Jian

    2018-03-01

    In order to improve the accuracy of case-based reasoning (CBR), this paper addresses a new CBR framework with the basic principle of rank aggregation. First, the ranking methods are put forward in each attribute subspace of case. The ordering relation between cases on each attribute is got between cases. Then, a sorting matrix is got. Second, the similar case retrieval process from ranking matrix is transformed into a rank aggregation optimal problem, which uses the Kemeny optimal. On the basis, a rank aggregation case-based reasoning algorithm, named RA-CBR, is designed. The experiment result on UCI data sets shows that case retrieval accuracy of RA-CBR algorithm is higher than euclidean distance CBR and mahalanobis distance CBR testing.So we can get the conclusion that RA-CBR method can increase the performance and efficiency of CBR.

  20. BaTiO3-based piezoelectrics: Fundamentals, current status, and perspectives

    Science.gov (United States)

    Acosta, M.; Novak, N.; Rojas, V.; Patel, S.; Vaish, R.; Koruza, J.; Rossetti, G. A.; Rödel, J.

    2017-12-01

    We present a critical review that encompasses the fundamentals and state-of-the-art knowledge of barium titanate-based piezoelectrics. First, the essential crystallography, thermodynamic relations, and concepts necessary to understand piezoelectricity and ferroelectricity in barium titanate are discussed. Strategies to optimize piezoelectric properties through microstructure control and chemical modification are also introduced. Thereafter, we systematically review the synthesis, microstructure, and phase diagrams of barium titanate-based piezoelectrics and provide a detailed compilation of their functional and mechanical properties. The most salient materials treated include the (Ba,Ca)(Zr,Ti)O3, (Ba,Ca)(Sn,Ti)O3, and (Ba,Ca)(Hf,Ti)O3 solid solution systems. The technological relevance of barium titanate-based piezoelectrics is also discussed and some potential market indicators are outlined. Finally, perspectives on productive lines of future research and promising areas for the applications of these materials are presented.

  1. Process-based organization design and hospital efficiency.

    Science.gov (United States)

    Vera, Antonio; Kuntz, Ludwig

    2007-01-01

    The central idea of process-based organization design is that organizing a firm around core business processes leads to cost reductions and quality improvements. We investigated theoretically and empirically whether the implementation of a process-based organization design is advisable in hospitals. The data came from a database compiled by the Statistical Office of the German federal state of Rheinland-Pfalz and from a written questionnaire, which was sent to the chief executive officers (CEOs) of all 92 hospitals in this federal state. We used data envelopment analysis (DEA) to measure hospital efficiency, and factor analysis and regression analysis to test our hypothesis. Our principal finding is that a high degree of process-based organization has a moderate but significant positive effect on the efficiency of hospitals. The main implication is that hospitals should implement a process-based organization to improve their efficiency. However, to actually achieve positive effects on efficiency, it is of paramount importance to observe some implementation rules, in particular to mobilize physician participation and to create an adequate organizational culture.

  2. Projection methods for line radiative transfer in spherical media.

    Science.gov (United States)

    Anusha, L. S.; Nagendra, K. N.

    An efficient numerical method called the Preconditioned Bi-Conjugate Gradient (Pre-BiCG) method is presented for the solution of radiative transfer equation in spherical geometry. A variant of this method called Stabilized Preconditioned Bi-Conjugate Gradient (Pre-BiCG-STAB) is also presented. These methods are based on projections on the subspaces of the n dimensional Euclidean space mathbb {R}n called Krylov subspaces. The methods are shown to be faster in terms of convergence rate compared to the contemporary iterative methods such as Jacobi, Gauss-Seidel and Successive Over Relaxation (SOR).

  3. Constant physics and characteristics of fundamental constant

    International Nuclear Information System (INIS)

    Tarrach, R.

    1998-01-01

    We present some evidence which supports a surprising physical interpretation of the fundamental constants. First, we relate two of them through the renormalization group. This leaves as many fundamental constants as base units. Second, we introduce and a dimensional system of units without fundamental constants. Third, and most important, we find, while interpreting the units of the a dimensional system, that is all cases accessible to experimentation the fundamental constants indicate either discretization at small values or boundedness at large values of the corresponding physical quantity. (Author) 12 refs

  4. New Parallel Algorithms for Structural Analysis and Design of Aerospace Structures

    Science.gov (United States)

    Nguyen, Duc T.

    1998-01-01

    Subspace and Lanczos iterations have been developed, well documented, and widely accepted as efficient methods for obtaining p-lowest eigen-pair solutions of large-scale, practical engineering problems. The focus of this paper is to incorporate recent developments in vectorized sparse technologies in conjunction with Subspace and Lanczos iterative algorithms for computational enhancements. Numerical performance, in terms of accuracy and efficiency of the proposed sparse strategies for Subspace and Lanczos algorithm, is demonstrated by solving for the lowest frequencies and mode shapes of structural problems on the IBM-R6000/590 and SunSparc 20 workstations.

  5. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    Science.gov (United States)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Park, June; Jhon, Young Min; Seong, Maeng-Je; Hong, Seunghun

    2010-02-01

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ~1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  6. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun [Department of Physics and Astronomy, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742 (Korea, Republic of); Park, June; Seong, Maeng-Je [Department of Physics, Chung-Ang University, Heukseok-Dong, Dongjak-Gu, Seoul 156-756 (Korea, Republic of); Jhon, Young Min, E-mail: mseong@cau.ac.kr, E-mail: shong@phya.snu.ac.kr [Korea Institute of Science and Technology, Hawolgok-Dong, Seongbuk-Gu, Seoul 136-791 (Korea, Republic of)

    2010-02-05

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of {approx}1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  7. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    International Nuclear Information System (INIS)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun; Park, June; Seong, Maeng-Je; Jhon, Young Min

    2010-01-01

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ∼1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  8. A description of the location and structure of the essential spectrum of a model operator in a subspace of a Fock space

    Energy Technology Data Exchange (ETDEWEB)

    Yodgorov, G R [Navoi State Pedagogical Institute, Navoi (Uzbekistan); Ismail, F [Universiti Putra Malaysia, Selangor (Malaysia); Muminov, Z I [Malaysia – Japan International Institute of Technology, Kuala Lumpur (Malaysia)

    2014-12-31

    We consider a certain model operator acting in a subspace of a fermionic Fock space. We obtain an analogue of Faddeev's equation. We describe the location of the essential spectrum of the operator under consideration and show that the essential spectrum consists of the union of at most four segments. Bibliography: 19 titles.

  9. Gender classification in children based on speech characteristics: using fundamental and formant frequencies of Malay vowels.

    Science.gov (United States)

    Zourmand, Alireza; Ting, Hua-Nong; Mirhassani, Seyed Mostafa

    2013-03-01

    Speech is one of the prevalent communication mediums for humans. Identifying the gender of a child speaker based on his/her speech is crucial in telecommunication and speech therapy. This article investigates the use of fundamental and formant frequencies from sustained vowel phonation to distinguish the gender of Malay children aged between 7 and 12 years. The Euclidean minimum distance and multilayer perceptron were used to classify the gender of 360 Malay children based on different combinations of fundamental and formant frequencies (F0, F1, F2, and F3). The Euclidean minimum distance with normalized frequency data achieved a classification accuracy of 79.44%, which was higher than that of the nonnormalized frequency data. Age-dependent modeling was used to improve the accuracy of gender classification. The Euclidean distance method obtained 84.17% based on the optimal classification accuracy for all age groups. The accuracy was further increased to 99.81% using multilayer perceptron based on mel-frequency cepstral coefficients. Copyright © 2013 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

  10. Fundamentals of metasurface lasers based on resonant dark states

    International Nuclear Information System (INIS)

    Droulias, Sotiris; Technology - Hellas; Jain, Aditya; Koschny, Thomas; Soukoulis, Costas M.; Technology - Hellas; Ames Laboratory and Iowa State University, Ames, IA

    2017-01-01

    Recently, our group proposed a metamaterial laser design based on explicitly coupled dark resonant states in low-loss dielectrics, which conceptually separates the gain-coupled resonant photonic state responsible for macroscopic stimulated emission from the coupling to specific free-space propagating modes, allowing independent adjustment of the lasing state and its coherent radiation output. Due to this functionality, it is now possible to make lasers that can overcome the trade-off between system dimensions and Q factor, especially for surface emitting lasers with deeply subwavelength thickness. In this paper, we give a detailed discussion of the key functionality and benefits of this design, such as radiation damping tunability, directionality, subwavelength integration, and simple layer-by-layer fabrication. Finally, we examine in detail the fundamental design tradeoffs that establish the principle of operation and must be taken into account and give guidance for realistic implementations.

  11. Skill-Based and Planned Active Play Versus Free-Play Effects on Fundamental Movement Skills in Preschoolers.

    Science.gov (United States)

    Roach, Lindsay; Keats, Melanie

    2018-01-01

    Fundamental movement skill interventions are important for promoting physical activity, but the optimal intervention model for preschool children remains unclear. We compared two 8-week interventions, a structured skill-station and a planned active play approach, to a free-play control condition on pre- and postintervention fundamental movement skills. We also collected data regarding program attendance and perceived enjoyment. We found a significant interaction effect between intervention type and time. A Tukey honest significant difference analysis supported a positive intervention effect showing a significant difference between both interventions and the free-play control condition. There was a significant between-group difference in group attendance such that mean attendance was higher for both the free-play and planned active play groups relative to the structured skill-based approach. There were no differences in attendance between free-play and planned active play groups, and there were no differences in enjoyment ratings between the two intervention groups. In sum, while both interventions led to improved fundamental movement skills, the active play approach offered several logistical advantages. Although these findings should be replicated, they can guide feasible and sustainable fundamental movement skill programs within day care settings.

  12. Fundamentals of plasma physics

    CERN Document Server

    Bittencourt, J A

    1986-01-01

    A general introduction designed to present a comprehensive, logical and unified treatment of the fundamentals of plasma physics based on statistical kinetic theory. Its clarity and completeness make it suitable for self-learning and self-paced courses. Problems are included.

  13. Overview of hybrid subspace methods for uncertainty quantification, sensitivity analysis

    International Nuclear Information System (INIS)

    Abdel-Khalik, Hany S.; Bang, Youngsuk; Wang, Congjian

    2013-01-01

    Highlights: ► We overview the state-of-the-art in uncertainty quantification and sensitivity analysis. ► We overview new developments in above areas using hybrid methods. ► We give a tutorial introduction to above areas and the new developments. ► Hybrid methods address the explosion in dimensionality in nonlinear models. ► Representative numerical experiments are given. -- Abstract: The role of modeling and simulation has been heavily promoted in recent years to improve understanding of complex engineering systems. To realize the benefits of modeling and simulation, concerted efforts in the areas of uncertainty quantification and sensitivity analysis are required. The manuscript intends to serve as a pedagogical presentation of the material to young researchers and practitioners with little background on the subjects. We believe this is important as the role of these subjects is expected to be integral to the design, safety, and operation of existing as well as next generation reactors. In addition to covering the basics, an overview of the current state-of-the-art will be given with particular emphasis on the challenges pertaining to nuclear reactor modeling. The second objective will focus on presenting our own development of hybrid subspace methods intended to address the explosion in the computational overhead required when handling real-world complex engineering systems.

  14. Study of I11-conditioning of Linac stereotactic irradiation subspaces using singular values decomposition analysis

    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.)

  15. Energy efficiency in France (1990-2000). Report based on the ODYSSEE data base on energy efficiency indicators and the MURE data base on energy efficiency policy measures with the support from SAVE

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2002-04-01

    This report presents an analysis of energy efficiency trends in France on the basis of energy efficiency indicators extracted from the Odyssee data base, maintained and updated in the framework of the SAVE program. This analysis focuses on the period 1990-2000. It also examines the policies and measures implemented in the field of energy efficiency, with a focus on the years 2000-2001. The full list and description of the policy measures are presented in detail in an annex. They are extracted from the MURE data base updated within the SAVE program. (author)

  16. Experiential Learning of Robotics Fundamentals Based on a Case Study of Robot-Assisted Stereotactic Neurosurgery

    Science.gov (United States)

    Faria, Carlos; Vale, Carolina; Machado, Toni; Erlhagen, Wolfram; Rito, Manuel; Monteiro, Sérgio; Bicho, Estela

    2016-01-01

    Robotics has been playing an important role in modern surgery, especially in procedures that require extreme precision, such as neurosurgery. This paper addresses the challenge of teaching robotics to undergraduate engineering students, through an experiential learning project of robotics fundamentals based on a case study of robot-assisted…

  17. DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays.

    Science.gov (United States)

    Li, Jianfeng; Wang, Feng; Jiang, Defu

    2017-03-20

    A fast direction of arrival (DOA) estimation method using a real-valued cross-correlation matrix (CCM) of coprime subarrays is proposed. Firstly, real-valued CCM with extended aperture is constructed to obtain the signal subspaces corresponding to the two subarrays. By analysing the relationship between the two subspaces, DOA estimations from the two subarrays are simultaneously obtained with automatic pairing. Finally, unique DOA is determined based on the common results from the two subarrays. Compared to partial spectral search (PSS) method and estimation of signal parameter via rotational invariance (ESPRIT) based method for coprime arrays, the proposed algorithm has lower complexity but achieves better DOA estimation performance and handles more sources. Simulation results verify the effectiveness of the approach.

  18. YIN, a fundamental frequency estimator for speech and music

    Science.gov (United States)

    de Cheveigné, Alain; Kawahara, Hideki

    2002-04-01

    An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.

  19. Man or machine? Rational trading without information about fundamentals

    OpenAIRE

    Rossi, Stefano; Tinn, Katrin

    2014-01-01

    18/09/13 MEB, Working paper, not yet pub. Systematic trading contingent on observed prices by agents uninformed about fundamentals has long been considered at odds with efficient markets populated by rational agents. In this paper we show that price-contingent trading is the equilibrium strategy of rational agents in efficient markets in which there is uncertainty about whether a large trader is informed. In this environment, knowing his own type and past trades (or lack of them) will be e...

  20. Efficient hierarchical trans-dimensional Bayesian inversion of magnetotelluric data

    Science.gov (United States)

    Xiang, Enming; Guo, Rongwen; Dosso, Stan E.; Liu, Jianxin; Dong, Hao; Ren, Zhengyong

    2018-06-01

    This paper develops an efficient hierarchical trans-dimensional (trans-D) Bayesian algorithm to invert magnetotelluric (MT) data for subsurface geoelectrical structure, with unknown geophysical model parameterization (the number of conductivity-layer interfaces) and data-error models parameterized by an auto-regressive (AR) process to account for potential error correlations. The reversible-jump Markov-chain Monte Carlo algorithm, which adds/removes interfaces and AR parameters in birth/death steps, is applied to sample the trans-D posterior probability density for model parameterization, model parameters, error variance and AR parameters, accounting for the uncertainties of model dimension and data-error statistics in the uncertainty estimates of the conductivity profile. To provide efficient sampling over the multiple subspaces of different dimensions, advanced proposal schemes are applied. Parameter perturbations are carried out in principal-component space, defined by eigen-decomposition of the unit-lag model covariance matrix, to minimize the effect of inter-parameter correlations and provide effective perturbation directions and length scales. Parameters of new layers in birth steps are proposed from the prior, instead of focused distributions centred at existing values, to improve birth acceptance rates. Parallel tempering, based on a series of parallel interacting Markov chains with successively relaxed likelihoods, is applied to improve chain mixing over model dimensions. The trans-D inversion is applied in a simulation study to examine the resolution of model structure according to the data information content. The inversion is also applied to a measured MT data set from south-central Australia.

  1. A new subspace based approach to iterative learning control

    NARCIS (Netherlands)

    Nijsse, G.; Verhaegen, M.; Doelman, N.J.

    2001-01-01

    This paper1 presents an iterative learning control (ILC) procedure based on an inverse model of the plant under control. Our first contribution is that we formulate the inversion procedure as a Kalman smoothing problem: based on a compact state space model of a possibly non-minimum phase system,

  2. Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency.

    Science.gov (United States)

    Mohamed, Omar; Wang, Jihong; Khalil, Ashraf; Limhabrash, Marwan

    2016-01-01

    This paper presents a novel strategy for implementing model predictive control (MPC) to a large gas turbine power plant as a part of our research progress in order to improve plant thermal efficiency and load-frequency control performance. A generalized state space model for a large gas turbine covering the whole steady operational range is designed according to subspace identification method with closed loop data as input to the identification algorithm. Then the model is used in developing a MPC and integrated into the plant existing control strategy. The strategy principle is based on feeding the reference signals of the pilot valve, natural gas valve, and the compressor pressure ratio controller with the optimized decisions given by the MPC instead of direct application of the control signals. If the set points for the compressor controller and turbine valves are sent in a timely manner, there will be more kinetic energy in the plant to release faster responses on the output and the overall system efficiency is improved. Simulation results have illustrated the feasibility of the proposed application that has achieved significant improvement in the frequency variations and load following capability which are also translated to be improvements in the overall combined cycle thermal efficiency of around 1.1 % compared to the existing one.

  3. Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor

    Directory of Open Access Journals (Sweden)

    Chen Gao

    2017-03-01

    Full Text Available Tool fault diagnosis in numerical control (NC machines plays a significant role in ensuring manufacturing quality. However, current methods of tool fault diagnosis lack accuracy. Therefore, in the present paper, a fault diagnosis method was proposed based on stationary subspace analysis (SSA and least squares support vector machine (LS-SVM using only a single sensor. First, SSA was used to extract stationary and non-stationary sources from multi-dimensional signals without the need for independency and without prior information of the source signals, after the dimensionality of the vibration signal observed by a single sensor was expanded by phase space reconstruction technique. Subsequently, 10 dimensionless parameters in the time-frequency domain for non-stationary sources were calculated to generate samples to train the LS-SVM. Finally, the measured vibration signals from tools of an unknown state and their non-stationary sources were separated by SSA to serve as test samples for the trained SVM. The experimental validation demonstrated that the proposed method has better diagnosis accuracy than three previous methods based on LS-SVM alone, Principal component analysis and LS-SVM or on SSA and Linear discriminant analysis.

  4. CAMS: OLAPing Multidimensional Data Streams Efficiently

    Science.gov (United States)

    Cuzzocrea, Alfredo

    In the context of data stream research, taming the multidimensionality of real-life data streams in order to efficiently support OLAP analysis/mining tasks is a critical challenge. Inspired by this fundamental motivation, in this paper we introduce CAMS (C ube-based A cquisition model for M ultidimensional S treams), a model for efficiently OLAPing multidimensional data streams. CAMS combines a set of data stream processing methodologies, namely (i) the OLAP dimension flattening process, which allows us to obtain dimensionality reduction of multidimensional data streams, and (ii) the OLAP stream aggregation scheme, which aggregates data stream readings according to an OLAP-hierarchy-based membership approach. We complete our analytical contribution by means of experimental assessment and analysis of both the efficiency and the scalability of OLAPing capabilities of CAMS on synthetic multidimensional data streams. Both analytical and experimental results clearly connote CAMS as an enabling component for next-generation Data Stream Management Systems.

  5. Fundamental limitations of non-thermal plasma processing for internal combustion engine NOx control

    International Nuclear Information System (INIS)

    Penetrante, B.M.

    1993-01-01

    This paper discusses the physics and chemistry of non-thermal plasma processing for post-combustion NO x control in internal combustion engines. A comparison of electron beam and electrical discharge processing is made regarding their power consumption, radical production, NO x removal mechanisms, and by product formation. Can non-thermal deNO x operate efficiently without additives or catalysts? How much electrical power does it cost to operate? What are the by-products of the process? This paper addresses these fundamental issues based on an analysis of the electron-molecule processes and chemical kinetics

  6. Mode-field half-widths of Gaussian approximation for the fundamental mode of two kinds of optical waveguides

    International Nuclear Information System (INIS)

    Lian-Huang, Li; Fu-Yuan, Guo

    2009-01-01

    This paper analyzes the characteristic of matching efficiency between the fundamental mode of two kinds of optical waveguides and its Gaussian approximate field. Then, it presents a new method where the mode-field half-width of Gaussian approximation for the fundamental mode should be defined according to the maximal matching efficiency method. The relationship between the mode-field half-width of the Gaussian approximate field obtained from the maximal matching efficiency and normalized frequency is studied; furthermore, two formulas of mode-field half-widths as a function of normalized frequency are proposed

  7. Measuring sustainability by Energy Efficiency Analysis for Korean Power Companies: A Sequential Slacks-Based Efficiency Measure

    Directory of Open Access Journals (Sweden)

    Ning Zhang

    2014-03-01

    Full Text Available Improving energy efficiency has been widely regarded as one of the most cost-effective ways to improve sustainability and mitigate climate change. This paper presents a sequential slack-based efficiency measure (SSBM application to model total-factor energy efficiency with undesirable outputs. This approach simultaneously takes into account the sequential environmental technology, total input slacks, and undesirable outputs for energy efficiency analysis. We conduct an empirical analysis of energy efficiency incorporating greenhouse gas emissions of Korean power companies during 2007–2011. The results indicate that most of the power companies are not performing at high energy efficiency. Sequential technology has a significant effect on the energy efficiency measurements. Some policy suggestions based on the empirical results are also presented.

  8. Constitutive relations in multidimensional isotropic elasticity and their restrictions to subspaces of lower dimensions

    Science.gov (United States)

    Georgievskii, D. V.

    2017-07-01

    The mechanical meaning and the relationships among material constants in an n-dimensional isotropic elastic medium are discussed. The restrictions of the constitutive relations (Hooke's law) to subspaces of lower dimension caused by the conditions that an m-dimensional strain state or an m-dimensional stress state (1 ≤ m < n) is realized in the medium. Both the terminology and the general idea of the mathematical construction are chosen by analogy with the case n = 3 and m = 2, which is well known in the classical plane problem of elasticity theory. The quintuples of elastic constants of the same medium that enter both the n-dimensional relations and the relations written out for any m-dimensional restriction are expressed in terms of one another. These expressions in terms of the known constants, for example, of a three-dimensional medium, i.e., the classical elastic constants, enable us to judge the material properties of this medium immersed in a space of larger dimension.

  9. A non-iterative sampling approach using noise subspace projection for EIT

    International Nuclear Information System (INIS)

    Bellis, Cédric; Constantinescu, Andrei; Coquet, Thomas; Jaravel, Thomas; Lechleiter, Armin

    2012-01-01

    This study concerns the problem of the reconstruction of inclusions embedded in a conductive medium in the context of electrical impedance tomography (EIT), which is investigated within the framework of a non-iterative sampling approach. This type of identification strategy relies on the construction of a special indicator function that takes, roughly speaking, small values outside the inclusion and large values inside. Such a function is constructed in this paper from the projection of a fundamental singular solution onto the space spanned by the singular vectors associated with some of the smallest singular values of the data-to-measurement operator. The behavior of the novel indicator function is analyzed. For a subsequent implementation in a discrete setting, the quality of classical finite-dimensional approximations of the measurement operator is discussed. The robustness of this approach is also analyzed when only noisy spectral information is available. Finally, this identification method is implemented numerically and experimentally, and its efficiency is discussed on a set of, partly experimental, examples. (paper)

  10. Delivery Time Minimization in Edge Caching: Synergistic Benefits of Subspace Alignment and Zero Forcing

    KAUST Repository

    Kakar, Jaber; Alameer, Alaa; Chaaban, Anas; Sezgin, Aydin; Paulraj, Arogyaswami

    2017-01-01

    the fundamental limits of a cache-aided wireless network consisting of one central base station, $M$ transceivers and $K$ receivers from a latency-centric perspective. We use the normalized delivery time (NDT) to capture the per-bit latency for the worst-case file

  11. DOE-EFRC Center on Nanostructuring for Efficient Energy Conversion (CNEEC)

    Energy Technology Data Exchange (ETDEWEB)

    Prinz, Friedrich B. [Stanford Univ., CA (United States). Mechanical Engineering. Materials Science and Engineering; Bent, Stacey F. [Stanford Univ., CA (United States). Chemical Engineering

    2015-10-22

    CNEEC’s mission has been to understand how nanostructuring of materials can enhance efficiency for solar energy conversion to produce hydrogen fuel and to solve fundamental cross-cutting problems. The overarching hypothesis underlying CNEEC research was that controlling, synthesizing and modifying materials at the nanometer scale increases the efficiency of energy conversion and storage devices and systems. In this pursuit, we emphasized the development of functional nanostructures that are based primarily on earth abundant and inexpensive materials.

  12. FUNDAMENTALS OF BIOMECHANICS

    Directory of Open Access Journals (Sweden)

    Duane Knudson

    2007-09-01

    Full Text Available DESCRIPTION This book provides a broad and in-depth theoretical and practical description of the fundamental concepts in understanding biomechanics in the qualitative analysis of human movement. PURPOSE The aim is to bring together up-to-date biomechanical knowledge with expert application knowledge. Extensive referencing for students is also provided. FEATURES This textbook is divided into 12 chapters within four parts, including a lab activities section at the end. The division is as follows: Part 1 Introduction: 1.Introduction to biomechanics of human movement; 2.Fundamentals of biomechanics and qualitative analysis; Part 2 Biological/Structural Bases: 3.Anatomical description and its limitations; 4.Mechanics of the musculoskeletal system; Part 3 Mechanical Bases: 5.Linear and angular kinematics; 6.Linear kinetics; 7.Angular kinetics; 8.Fluid mechanics; Part 4 Application of Biomechanics in Qualitative Analysis :9.Applying biomechanics in physical education; 10.Applying biomechanics in coaching; 11.Applying biomechanics in strength and conditioning; 12.Applying biomechanics in sports medicine and rehabilitation. AUDIENCE This is an important reading for both student and educators in the medicine, sport and exercise-related fields. For the researcher and lecturer it would be a helpful guide to plan and prepare more detailed experimental designs or lecture and/or laboratory classes in exercise and sport biomechanics. ASSESSMENT The text provides a constructive fundamental resource for biomechanics, exercise and sport-related students, teachers and researchers as well as anyone interested in understanding motion. It is also very useful since being clearly written and presenting several ways of examples of the application of biomechanics to help teach and apply biomechanical variables and concepts, including sport-related ones

  13. System Efficiency Improvement for Electric Vehicles Adopting a Permanent Magnet Synchronous Motor Direct Drive System

    Directory of Open Access Journals (Sweden)

    Chengming Zhang

    2017-12-01

    Full Text Available To improve the endurance mileage of electric vehicles (EVs, it is important to decrease the energy consumption of the Permanent Magnet Synchronous Motor (PMSM drive system. This paper proposes a novel loss optimization control strategy named system efficiency improvement control which can optimize both inverter and motor losses. A nonlinear power converter loss model is built to fit the nonlinear characteristics of power devices. This paper uses double Fourier integral analysis to analytically calculate the fundamental and harmonic components of motor current by which the fundamental motor loss and harmonic motor loss can be accurately analyzed. From these loss models, a whole-frequency-domain system loss model is derived and presented. Based on the system loss model, the system efficiency improvement control method applies the genetic algorithm to adjust the motor current and PWM frequency together to optimize the inverter and motor losses by which the system efficiency can be significantly improved without seriously influence on the system stability over the whole operation range of EVs. The optimal effects of system efficiency is verified by the experimental results in both Si-IGBT-based PMSM system and SiC-MOSFET-based system.

  14. Default Bayesian Estimation of the Fundamental Frequency

    DEFF Research Database (Denmark)

    Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt

    2013-01-01

    Joint fundamental frequency and model order esti- mation is an important problem in several applications. In this paper, a default estimation algorithm based on a minimum of prior information is presented. The algorithm is developed in a Bayesian framework, and it can be applied to both real....... Moreover, several approximations of the posterior distributions on the fundamental frequency and the model order are derived, and one of the state-of-the-art joint fundamental frequency and model order estimators is demonstrated to be a special case of one of these approximations. The performance...

  15. Relativities of fundamentality

    Science.gov (United States)

    McKenzie, Kerry

    2017-08-01

    S-dualities have been held to have radical implications for our metaphysics of fundamentality. In particular, it has been claimed that they make the fundamentality status of a physical object theory-relative in an important new way. But what physicists have had to say on the issue has not been clear or consistent, and in particular seems to be ambiguous between whether S-dualities demand an anti-realist interpretation of fundamentality talk or merely a revised realism. This paper is an attempt to bring some clarity to the matter. After showing that even antecedently familiar fundamentality claims are true only relative to a raft of metaphysical, physical, and mathematical assumptions, I argue that the relativity of fundamentality inherent in S-duality nevertheless represents something new, and that part of the reason for this is that it has both realist and anti-realist implications for fundamentality talk. I close by discussing the broader significance that S-dualities have for structuralist metaphysics and for fundamentality metaphysics more generally.

  16. DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays

    Directory of Open Access Journals (Sweden)

    Jianfeng Li

    2017-03-01

    Full Text Available A fast direction of arrival (DOA estimation method using a real-valued cross-correlation matrix (CCM of coprime subarrays is proposed. Firstly, real-valued CCM with extended aperture is constructed to obtain the signal subspaces corresponding to the two subarrays. By analysing the relationship between the two subspaces, DOA estimations from the two subarrays are simultaneously obtained with automatic pairing. Finally, unique DOA is determined based on the common results from the two subarrays. Compared to partial spectral search (PSS method and estimation of signal parameter via rotational invariance (ESPRIT based method for coprime arrays, the proposed algorithm has lower complexity but achieves better DOA estimation performance and handles more sources. Simulation results verify the effectiveness of the approach.

  17. An Efficient Catalyst for the Synthesis of Schiff Bases

    International Nuclear Information System (INIS)

    Fareed, G.; Afza, N.; Kalhoro, M.A.

    2013-01-01

    An efficient high yielding synthesis of Schiff bases (1-17) is derived from condensation of 2-fluorenamine and 4-amino phenol with a variety of aldehydes catalyzed by dodecatungstosilicic acid P/sub 2/O/sub 5/ under solvent free conditions at room temperature. The catayst is found to be more efficient in terms of ease of reaction workup and high yields. This methodology contributes to an energy efficient, facile and environamental friendly synthesis for the preparation of Schiff bases. The structures of afforded Schiff bases were characterized by spectroscopic data and elemental analysis. (author)

  18. Fundamental limit of nanophotonic light trapping in solar cells.

    Science.gov (United States)

    Yu, Zongfu; Raman, Aaswath; Fan, Shanhui

    2010-10-12

    Establishing the fundamental limit of nanophotonic light-trapping schemes is of paramount importance and is becoming increasingly urgent for current solar cell research. The standard theory of light trapping demonstrated that absorption enhancement in a medium cannot exceed a factor of 4n(2)/sin(2)θ, where n is the refractive index of the active layer, and θ is the angle of the emission cone in the medium surrounding the cell. This theory, however, is not applicable in the nanophotonic regime. Here we develop a statistical temporal coupled-mode theory of light trapping based on a rigorous electromagnetic approach. Our theory reveals that the conventional limit can be substantially surpassed when optical modes exhibit deep-subwavelength-scale field confinement, opening new avenues for highly efficient next-generation solar cells.

  19. 4th International Conference on Trapped Charged Particles and Fundamental Physics

    CERN Document Server

    Comyn, M; Thomson, J; Gwinner, G; TCP'06; TCP 2006

    2007-01-01

    The TCP06 conference in Parksville on Vancouver Island showcased the impressive progress in the study of fundamental physics using trapped charged particles. Atom and ion trapping has revolutionized atomic physics and related fields. It has proven to be particularly useful for fundamental physics experiments, as the tight control over the particles' degrees of freedom leads to increased precision and efficient use of exotic species such as radioactive atoms or anti-matter. The topics of the meeting included fundamental interactions and symmetries, quantum electrodynamics, quantum state manipulation and quantum information, precision spectroscopy and frequency standards, storage ring physics, highly charged ions in traps, traps for radioactive isotopes, plasmas and collective behaviour, and anti-hydrogen. Highlights from related fields such as fundamental physics studies with neutral, trapped atoms were also presented. The combination of overview articles by leaders in the field and detailed reports on recent ...

  20. Effects of fundamentals acquisition and strategy switch on stock price dynamics

    Science.gov (United States)

    Wu, Songtao; He, Jianmin; Li, Shouwei

    2018-02-01

    An agent-based artificial stock market is developed to simulate trading behavior of investors. In the market, acquisition and employment of information about fundamentals and strategy switch are investigated to explain stock price dynamics. Investors could obtain the information from both market and neighbors resided on their social networks. Depending on information status and performances of different strategies, an informed investor may switch to the strategy of fundamentalist. This in turn affects the information acquisition process, since fundamentalists are more inclined to search and spread the information than chartists. Further investigation into price dynamics generated from three typical networks, i.e. regular lattice, small-world network and random graph, are conducted after general relation between network structures and price dynamics is revealed. In each network, integrated effects of different combinations of information efficiency and switch intensity are investigated. Results have shown that, along with increasing switch intensity, market and social information efficiency play different roles in the formation of price distortion, standard deviation and kurtosis of returns.

  1. Different Variants of Fundamental Portfolio

    Directory of Open Access Journals (Sweden)

    Tarczyński Waldemar

    2014-06-01

    Full Text Available The paper proposes the fundamental portfolio of securities. This portfolio is an alternative for the classic Markowitz model, which combines fundamental analysis with portfolio analysis. The method’s main idea is based on the use of the TMAI1 synthetic measure and, in limiting conditions, the use of risk and the portfolio’s rate of return in the objective function. Different variants of fundamental portfolio have been considered under an empirical study. The effectiveness of the proposed solutions has been related to the classic portfolio constructed with the help of the Markowitz model and the WIG20 market index’s rate of return. All portfolios were constructed with data on rates of return for 2005. Their effectiveness in 2006- 2013 was then evaluated. The studied period comprises the end of the bull market, the 2007-2009 crisis, the 2010 bull market and the 2011 crisis. This allows for the evaluation of the solutions’ flexibility in various extreme situations. For the construction of the fundamental portfolio’s objective function and the TMAI, the study made use of financial and economic data on selected indicators retrieved from Notoria Serwis for 2005.

  2. Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure

    International Nuclear Information System (INIS)

    Choi, Yongrok; Zhang, Ning; Zhou, P.

    2012-01-01

    Highlights: ► We employ a slacks-based DEA model to estimate the energy efficiency and shadow prices of CO 2 emissions in China. ► The empirical study shows that China was not performing CO 2 -efficiently. ► The average of estimated shadow prices of CO 2 emissions is about $7.2. -- Abstract: This paper uses nonparametric efficiency analysis technique to estimate the energy efficiency, potential emission reductions and marginal abatement costs of energy-related CO 2 emissions in China. We employ a non-radial slacks-based data envelopment analysis (DEA) model for estimating the potential reductions and efficiency of CO 2 emissions for China. The dual model of the slacks-based DEA model is then used to estimate the marginal abatement costs of CO 2 emissions. An empirical study based on China’s panel data (2001–2010) is carried out and some policy implications are also discussed.

  3. Fundamental Rights and the EU Internal Market: Just how Fundamental are the EU Treaty Freedoms?
    A Normative Enquiry Based on John Rawls’ Political Philosophy

    Directory of Open Access Journals (Sweden)

    Nik J. de Boer

    2013-01-01

    Full Text Available This article assesses whether the EU Treaty freedoms - the free movement of goods, persons, services and capital - should be considered as fundamental rights which are hierarchically equal to other fundamental rights. It uses the political philosophy of John Rawls to assess why we should attach priority to certain rights and which rights should therefore be considered fundamental rights. On this basis it is argued that we should recognise two main types of fundamental rights, namely basic rights and liberties associated with Rawls' first principle of justice and the rights associated with the principle of fair equality of opportunity. This is followed by an analysis of the interpretation that the European Court of Justice (CJEU gives to the Treaty freedoms. On the basis of the normative framework, it is argued that the Treaty freedoms can be seen as fundamental rights insofar as they embody the value of equality of opportunity. Nonetheless, the CJEU increasingly seems to rely on a broader market access approach rather than an equal treatment approach in interpreting the Treaty freedoms. It is argued that where equal treatment is not at stake, the Treaty freedoms should not be seen as fundamental rights. Therefore, in cases where there is a conflict between a fundamental right and a Treaty freedom the CJEU should carefully distinguish between these two different interpretations of the Treaty freedoms. In cases where it is merely market access that is at stake, the CJEU should regard the protection of fundamental rights as more important, and be very careful in allowing a restriction of fundamental rights in order to protect the exercise of the Treaty freedom. On the other hand, in cases where the Treaty freedoms can be seen as protecting equality of opportunity and where they conflict with other fundamental rights, the Court is justified in construing the conflict as a right-right conflict in which a fair balance has to be sought.

  4. A fast algorithm for parabolic PDE-based inverse problems based on Laplace transforms and flexible Krylov solvers

    International Nuclear Information System (INIS)

    Bakhos, Tania; Saibaba, Arvind K.; Kitanidis, Peter K.

    2015-01-01

    We consider the problem of estimating parameters in large-scale weakly nonlinear inverse problems for which the underlying governing equations is a linear, time-dependent, parabolic partial differential equation. A major challenge in solving these inverse problems using Newton-type methods is the computational cost associated with solving the forward problem and with repeated construction of the Jacobian, which represents the sensitivity of the measurements to the unknown parameters. Forming the Jacobian can be prohibitively expensive because it requires repeated solutions of the forward and adjoint time-dependent parabolic partial differential equations corresponding to multiple sources and receivers. We propose an efficient method based on a Laplace transform-based exponential time integrator combined with a flexible Krylov subspace approach to solve the resulting shifted systems of equations efficiently. Our proposed solver speeds up the computation of the forward and adjoint problems, thus yielding significant speedup in total inversion time. We consider an application from Transient Hydraulic Tomography (THT), which is an imaging technique to estimate hydraulic parameters related to the subsurface from pressure measurements obtained by a series of pumping tests. The algorithms discussed are applied to a synthetic example taken from THT to demonstrate the resulting computational gains of this proposed method

  5. A fast algorithm for parabolic PDE-based inverse problems based on Laplace transforms and flexible Krylov solvers

    Energy Technology Data Exchange (ETDEWEB)

    Bakhos, Tania, E-mail: taniab@stanford.edu [Institute for Computational and Mathematical Engineering, Stanford University (United States); Saibaba, Arvind K. [Department of Electrical and Computer Engineering, Tufts University (United States); Kitanidis, Peter K. [Institute for Computational and Mathematical Engineering, Stanford University (United States); Department of Civil and Environmental Engineering, Stanford University (United States)

    2015-10-15

    We consider the problem of estimating parameters in large-scale weakly nonlinear inverse problems for which the underlying governing equations is a linear, time-dependent, parabolic partial differential equation. A major challenge in solving these inverse problems using Newton-type methods is the computational cost associated with solving the forward problem and with repeated construction of the Jacobian, which represents the sensitivity of the measurements to the unknown parameters. Forming the Jacobian can be prohibitively expensive because it requires repeated solutions of the forward and adjoint time-dependent parabolic partial differential equations corresponding to multiple sources and receivers. We propose an efficient method based on a Laplace transform-based exponential time integrator combined with a flexible Krylov subspace approach to solve the resulting shifted systems of equations efficiently. Our proposed solver speeds up the computation of the forward and adjoint problems, thus yielding significant speedup in total inversion time. We consider an application from Transient Hydraulic Tomography (THT), which is an imaging technique to estimate hydraulic parameters related to the subsurface from pressure measurements obtained by a series of pumping tests. The algorithms discussed are applied to a synthetic example taken from THT to demonstrate the resulting computational gains of this proposed method.

  6. HSM: Heterogeneous Subspace Mining in High Dimensional Data

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Seidl, Thomas

    2009-01-01

    Heterogeneous data, i.e. data with both categorical and continuous values, is common in many databases. However, most data mining algorithms assume either continuous or categorical attributes, but not both. In high dimensional data, phenomena due to the "curse of dimensionality" pose additional...... challenges. Usually, due to locally varying relevance of attributes, patterns do not show across the full set of attributes. In this paper we propose HSM, which defines a new pattern model for heterogeneous high dimensional data. It allows data mining in arbitrary subsets of the attributes that are relevant...... for the respective patterns. Based on this model we propose an efficient algorithm, which is aware of the heterogeneity of the attributes. We extend an indexing structure for continuous attributes such that HSM indexing adapts to different attribute types. In our experiments we show that HSM efficiently mines...

  7. Construction of Quasi-Cyclic LDPC Codes Based on Fundamental Theorem of Arithmetic

    Directory of Open Access Journals (Sweden)

    Hai Zhu

    2018-01-01

    Full Text Available Quasi-cyclic (QC LDPC codes play an important role in 5G communications and have been chosen as the standard codes for 5G enhanced mobile broadband (eMBB data channel. In this paper, we study the construction of QC LDPC codes based on an arbitrary given expansion factor (or lifting degree. First, we analyze the cycle structure of QC LDPC codes and give the necessary and sufficient condition for the existence of short cycles. Based on the fundamental theorem of arithmetic in number theory, we divide the integer factorization into three cases and present three classes of QC LDPC codes accordingly. Furthermore, a general construction method of QC LDPC codes with girth of at least 6 is proposed. Numerical results show that the constructed QC LDPC codes perform well over the AWGN channel when decoded with the iterative algorithms.

  8. Some fundamental technical concepts about cost based transmission pricing

    International Nuclear Information System (INIS)

    Shirmohammadi, D.; Filho, X.V.; Gorenstin, B.; Pereira, M.V.P.

    1996-01-01

    In this paper the authors describe the basic technical concepts involved in developing cost based transmission prices. They introduce the concepts of transmission pricing paradigms and methodologies to better illustrate how transmission costs are transformed into transmission prices. The authors also briefly discuss the role of these paradigms and methodologies in promoting ''economic efficiency'' which is narrowly defined in this paper. They conclude the paper with an example of the application of some of these paradigms and methodologies for pricing transmission services in Brazil

  9. A procedure for denoising dual-axis swallowing accelerometry signals

    International Nuclear Information System (INIS)

    Sejdić, Ervin; Chau, Tom; Steele, Catriona M

    2010-01-01

    Dual-axis swallowing accelerometry is an emerging tool for the assessment of dysphagia (swallowing difficulties). These signals however can be very noisy as a result of physiological and motion artifacts. In this note, we propose a novel scheme for denoising those signals, i.e. a computationally efficient search for the optimal denoising threshold within a reduced wavelet subspace. To determine a viable subspace, the algorithm relies on the minimum value of the estimated upper bound for the reconstruction error. A numerical analysis of the proposed scheme using synthetic test signals demonstrated that the proposed scheme is computationally more efficient than minimum noiseless description length (MNDL)-based denoising. It also yields smaller reconstruction errors than MNDL, SURE and Donoho denoising methods. When applied to dual-axis swallowing accelerometry signals, the proposed scheme exhibits improved performance for dry, wet and wet chin tuck swallows. These results are important for the further development of medical devices based on dual-axis swallowing accelerometry signals. (note)

  10. Efficient Variational Approaches for Deformable Registration of Images

    Directory of Open Access Journals (Sweden)

    Mehmet Ali Akinlar

    2012-01-01

    Full Text Available Dirichlet, anisotropic, and Huber regularization terms are presented for efficient registration of deformable images. Image registration, an ill-posed optimization problem, is solved using a gradient-descent-based method and some fundamental theorems in calculus of variations. Euler-Lagrange equations with homogeneous Neumann boundary conditions are obtained. These equations are discretized by multigrid and finite difference numerical techniques. The method is applied to the registration of brain MR images of size 65×65. Computational results indicate that the presented method is quite fast and efficient in the registration of deformable medical images.

  11. Spectral Efficiency Analysis for Multicarrier Based 4G Systems

    DEFF Research Database (Denmark)

    Silva, Nuno; Rahman, Muhammad Imadur; Frederiksen, Flemming Bjerge

    2006-01-01

    In this paper, a spectral efficiency definition is proposed. Spectral efficiency for multicarrier based multiaccess techniques, such as OFDMA, MC-CDMA and OFDMA-CDM, is analyzed. Simulations for different indoor and outdoor scenarios are carried out. Based on the simulations, we have discussed ho...

  12. Enhanced H-filter based on Fåhræus-Lindqvist effect for efficient and robust dialysis without membrane.

    Science.gov (United States)

    Zheng, Wei-Chao; Xie, Rui; He, Li-Qun; Xi, Yue-Heng; Liu, Ying-Mei; Meng, Zhi-Jun; Wang, Wei; Ju, Xiao-Jie; Chen, Gang; Chu, Liang-Yin

    2015-07-01

    A novel microfluidic device for highly efficient and robust dialysis without membrane is highly desired for the development of portable or wearable microdialyzer. Here we report an enhanced H-filter with pillar array based on Fåhræus-Lindqvist effect (F-L effect) for highly efficient and robust membraneless dialysis of simplified blood for the first time. The H-filter employs two fluids laminarly flowing in the microchannel for continuously membraneless dialysis. With pillar array in the microchannel, the two laminar flows, with one containing blood cells and small molecules and another containing dialyzate solution, can form a cell-free layer at the interface as selective zones for separation. This provides enhanced mixing yet extremely low shear for extraction of small molecules from the blood-cell-containing flow into the dialyzate flow, resulting in robust separation with reduced cell loss and improved efficiency. We demonstrate this by first using Chlorella pyrenoidosa as model cells to quantitatively study the separation performances, and then using simplified human blood for dialysis. The advanced H-filter, with highly efficient and robust performance for membraneless dialysis, shows great potential as promising candidate for rapid blood analysis/separation, and as fundamental structure for portable dialyzer.

  13. A Note for Graphing Calculators in the Fundamental Finance Course

    Science.gov (United States)

    Chen, Jeng-Hong

    2011-01-01

    The financial calculator is incorporated in finance education. In class, the instructor shows students how to use the financial calculator's function keys to solve time value of money (TVM) related problems efficiently. The fundamental finance course is required for all majors in the business school. Some students, especially…

  14. Eigenvector Weighting Function in Face Recognition

    Directory of Open Access Journals (Sweden)

    Pang Ying Han

    2011-01-01

    Full Text Available Graph-based subspace learning is a class of dimensionality reduction technique in face recognition. The technique reveals the local manifold structure of face data that hidden in the image space via a linear projection. However, the real world face data may be too complex to measure due to both external imaging noises and the intra-class variations of the face images. Hence, features which are extracted by the graph-based technique could be noisy. An appropriate weight should be imposed to the data features for better data discrimination. In this paper, a piecewise weighting function, known as Eigenvector Weighting Function (EWF, is proposed and implemented in two graph based subspace learning techniques, namely Locality Preserving Projection and Neighbourhood Preserving Embedding. Specifically, the computed projection subspace of the learning approach is decomposed into three partitions: a subspace due to intra-class variations, an intrinsic face subspace, and a subspace which is attributed to imaging noises. Projected data features are weighted differently in these subspaces to emphasize the intrinsic face subspace while penalizing the other two subspaces. Experiments on FERET and FRGC databases are conducted to show the promising performance of the proposed technique.

  15. Efficient approach for reliability-based optimization based on weighted importance sampling approach

    International Nuclear Information System (INIS)

    Yuan, Xiukai; Lu, Zhenzhou

    2014-01-01

    An efficient methodology is presented to perform the reliability-based optimization (RBO). It is based on an efficient weighted approach for constructing an approximation of the failure probability as an explicit function of the design variables which is referred to as the ‘failure probability function (FPF)’. It expresses the FPF as a weighted sum of sample values obtained in the simulation-based reliability analysis. The required computational effort for decoupling in each iteration is just single reliability analysis. After the approximation of the FPF is established, the target RBO problem can be decoupled into a deterministic one. Meanwhile, the proposed weighted approach is combined with a decoupling approach and a sequential approximate optimization framework. Engineering examples are given to demonstrate the efficiency and accuracy of the presented methodology

  16. Semi-analytical Formulas for the Fundamental Parameters of Galactic Early B Supergiants

    Directory of Open Access Journals (Sweden)

    Zaninetti, L.

    2009-12-01

    Full Text Available The publication of new tables of calibration of some fundamental parameters of Galactic B0-B5 supergiants in the two classes $I_mathrm{a}$ and $I_mathrm{b} $ allows to particularize the eight parameters conjecture that model five fundamental parameters. The numerical expressions for visual magnitude, radius, mass, luminosity and surface gravity are derived for supergiants in the range of temperature between 29700 K and 15200 K. The availability of accurate tables of calibration allows us to estimate the efficiency of the derived formulas in reproducing the observed values. The average efficiency of the new formulas, expressed in percent, is 94 for the visual magnitude, 81 for the mass, 96 for the radius, 99 for the logarithm of the luminosity and 97 for the logarithm of the surface gravity.

  17. Impact of the phase-mismatch in the SHG crystal and consequential self-action of the fundamental wave by cascaded second-order effects on the THG efficiency of a Q-switched 1342 nm Nd:YVO₄ laser.

    Science.gov (United States)

    Koch, Peter; Bartschke, Juergen; L'huillier, Johannes A

    2015-05-18

    We report on the influence of self-focusing and self-defocusing in the phase-mismatched frequency doubling crystal on the third harmonic generation (THG) efficiency in a two crystal frequency tripling scheme. By detuning the temperature of the doubling crystal, the impact of a phase-mismatch in second harmonic generation (SHG) on the subsequent sum frequency mixing process was investigated. It was found that adjusting the temperature not only affected the power ratio of the second harmonic to the fundamental but also the beam diameter of the fundamental beam in the THG crystal, which was caused by self-focusing and self-defocusing of the fundamental beam, respectively. This self-action was induced by a cascaded χ(2) : χ(2) process in the phase-mismatched SHG crystal. Self-defocusing was observable for positive detuning and self-focusing for negative detuning of the phase-matching temperature. Hence, the THG efficiency was not symmetric with respect to the point of optimum phase-matching. Optimum THG was obtained for positive detuning and the resulting self-defocusing in combination with the focusing lens in front of the THG stage was also beneficial for the beam quality of the third harmonic.

  18. Integration of LCoS-SLM and LabVIEW based software to simulate fundamental optics, wave optics, and Fourier optics

    Science.gov (United States)

    Lyu, Bo-Han; Wang, Chen; Tsai, Chun-Wei

    2017-08-01

    Jasper Display Corp. (JDC) offer high reflectivity, high resolution Liquid Crystal on Silicon - Spatial Light Modulator (LCoS-SLM) which include an associated controller ASIC and LabVIEW based modulation software. Based on this LCoS-SLM, also called Education Kit (EDK), we provide a training platform which includes a series of optical theory and experiments to university students. This EDK not only provides a LabVIEW based operation software to produce Computer Generated Holograms (CGH) to generate some basic diffraction image or holographic image, but also provides simulation software to verity the experiment results simultaneously. However, we believe that a robust LCoSSLM, operation software, simulation software, training system, and training course can help students to study the fundamental optics, wave optics, and Fourier optics more easily. Based on these fundamental knowledges, they could develop their unique skills and create their new innovations on the optoelectronic application in the future.

  19. The Theoretical and Applied Fundamentals of Management of Tolling Operations

    Directory of Open Access Journals (Sweden)

    Melnyk Olha H.

    2018-01-01

    Full Text Available The article is concerned with identification of the unified essence of tolling operations in the scientific terminological apparatus; substantiation of conceptual bases of balanced management of tolling operations; characterization of the current status and tendencies of development of tolling operations at domestic enterprises; allocation and analyzing of factors of influence on tolling operations, and also economic assessment of tolling operations of the processing enterprise. The concept of balanced management of tolling operations has been substantiated, on parity basis considering interests of the customer and executor of processing works. The most essential internal and external factors of influence on tolling operations have been identified and their influence has been interpreted by means of the developed correlation-regression dependencies. The complex model of economic diagnostics of tolling operations of processing enterprise has been improved, allowing to assess efficiency of their implementation both operatively and fundamentally. Prospect for further research is development of an instrumentarium of motivation of participants of tolling operations in order to increase motivation of employees to the efficient implementation of these operations.

  20. O sofrimento psíquico na perspectiva da psicopatologia fundamental Psychic suffering from the fundamental psychopathology perspective

    Directory of Open Access Journals (Sweden)

    Paulo Ceccarelli

    2005-12-01

    Full Text Available Partindo da palavra PSICOPATOLOGIA, o autor mostra, de forma resumida, como cada contexto histórico tentou "decompor" o sofrimento psíquico em seus elementos de base para classificá-lo, estudá-lo e tratá-lo. Após uma breve apresentação da psicopatologia na contemporaneidade, o autor introduz os pressupostos da Psicopatologia Fundamental e suas contribuições na compreensão do sofrimento psíquico. Ainda que não seja objetivo do texto participar do debate atual sobre as diretrizes curriculares que norteiam a formação do psicólogo, o autor toma o estudo do conhecimento (logos da alma (psyché - a psicologia - como exemplo de um dos campos de aplicação da Psicopatologia Fundamental.From the word PSYCHOPATHOLOGY the author briefly shows how each historical context had its own way to decompose psychic suffering in order to classify, study and search for its cure. After a short discussion about psychopathology in contemporaneity the author introduces the theoretical bases of Fundamental Psychopathology and its contributions to understanding psychic suffering. Although this text does not claim to participate in the debate about psychology students training, the author exemplifies through the study of the soul (psyche knowledge (logos one of the applications of Fundamental Psychopathology.

  1. Measurement of energy efficiency based on economic foundations

    International Nuclear Information System (INIS)

    Filippini, Massimo; Hunt, Lester C.

    2015-01-01

    Energy efficiency policy is seen as a very important activity by almost all policy makers. In practical energy policy analysis, the typical indicator used as a proxy for energy efficiency is energy intensity. However, this simple indicator is not necessarily an accurate measure given changes in energy intensity are a function of changes in several factors as well as ‘true’ energy efficiency; hence, it is difficult to make conclusions for energy policy based upon simple energy intensity measures. Related to this, some published academic papers over the last few years have attempted to use empirical methods to measure the efficient use of energy based on the economic theory of production. However, these studies do not generally provide a systematic discussion of the theoretical basis nor the possible parametric empirical approaches that are available for estimating the level of energy efficiency. The objective of this paper, therefore, is to sketch out and explain from an economic perspective the theoretical framework as well as the empirical methods for measuring the level of energy efficiency. Additionally, in the second part of the paper, some of the empirical studies that have attempted to measure energy efficiency using such an economics approach are summarized and discussed.

  2. Subarray Processing for Projection-based RFI Mitigation in Radio Astronomical Interferometers

    Science.gov (United States)

    Burnett, Mitchell C.; Jeffs, Brian D.; Black, Richard A.; Warnick, Karl F.

    2018-04-01

    Radio Frequency Interference (RFI) is a major problem for observations in Radio Astronomy (RA). Adaptive spatial filtering techniques such as subspace projection are promising candidates for RFI mitigation; however, for radio interferometric imaging arrays, these have primarily been used in engineering demonstration experiments rather than mainstream scientific observations. This paper considers one reason that adoption of such algorithms is limited: RFI decorrelates across the interferometric array because of long baseline lengths. This occurs when the relative RFI time delay along a baseline is large compared to the frequency channel inverse bandwidth used in the processing chain. Maximum achievable excision of the RFI is limited by covariance matrix estimation error when identifying interference subspace parameters, and decorrelation of the RFI introduces errors that corrupt the subspace estimate, rendering subspace projection ineffective over the entire array. In this work, we present an algorithm that overcomes this challenge of decorrelation by applying subspace projection via subarray processing (SP-SAP). Each subarray is designed to have a set of elements with high mutual correlation in the interferer for better estimation of subspace parameters. In an RFI simulation scenario for the proposed ngVLA interferometric imaging array with 15 kHz channel bandwidth for correlator processing, we show that compared to the former approach of applying subspace projection on the full array, SP-SAP improves mitigation of the RFI on the order of 9 dB. An example of improved image synthesis and reduced RFI artifacts for a simulated image “phantom” using the SP-SAP algorithm is presented.

  3. Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods

    Science.gov (United States)

    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.

  4. Finite-dimensional linear algebra

    CERN Document Server

    Gockenbach, Mark S

    2010-01-01

    Some Problems Posed on Vector SpacesLinear equationsBest approximationDiagonalizationSummaryFields and Vector SpacesFields Vector spaces Subspaces Linear combinations and spanning sets Linear independence Basis and dimension Properties of bases Polynomial interpolation and the Lagrange basis Continuous piecewise polynomial functionsLinear OperatorsLinear operatorsMore properties of linear operatorsIsomorphic vector spaces Linear operator equations Existence and uniqueness of solutions The fundamental theorem; inverse operatorsGaussian elimination Newton's method Linear ordinary differential eq

  5. The water, fundamental ecological base?

    International Nuclear Information System (INIS)

    Bolivar, Luis Humberto

    1994-01-01

    To speak of ecology and the man's interaction with the environment takes, in fact implicit many elements that, actuating harmoniously generates a conducive entropy to a better to be, however it is necessary to hierarchy the importance of these elements, finding that the water, not alone to constitute sixty five percent of the total volume of the planet, or sixty percent of the human body, but to be the well called molecule of the life, it is constituted in the main element to consider in the study of the ecology. The water circulates continually through the endless hydrological cycle of condensation, precipitation, filtration, retention, evaporation, precipitation and so forth; however, due to the quick growth of the cities, its expansion of the green areas or its border lands, result of a demographic behavior and of inadequate social establishment; or of the advance industrial excessive, they produce irreparable alterations in the continuous processes of the water production, for this reason it is fundamental to know some inherent problems to the sources of water. The water, the most important in the renewable natural resources, essential for the life and for the achievement of good part of the man's goals in their productive function, it is direct or indirectly the natural resource more threatened by the human action

  6. An efficient technique for the point reactor kinetics equations with Newtonian temperature feedback effects

    International Nuclear Information System (INIS)

    Nahla, Abdallah A.

    2011-01-01

    Highlights: → An efficient technique for the nonlinear reactor kinetics equations is presented. → This method is based on Backward Euler or Crank Nicholson and fundamental matrix. → Stability of efficient technique is defined and discussed. → This method is applied to point kinetics equations of six-groups of delayed neutrons. → Step, ramp, sinusoidal and temperature feedback reactivities are discussed. - Abstract: The point reactor kinetics equations of multi-group of delayed neutrons in the presence Newtonian temperature feedback effects are a system of stiff nonlinear ordinary differential equations which have not any exact analytical solution. The efficient technique for this nonlinear system is based on changing this nonlinear system to a linear system by the predicted value of reactivity and solving this linear system using the fundamental matrix of the homogenous linear differential equations. The nonlinear point reactor kinetics equations are rewritten in the matrix form. The solution of this matrix form is introduced. This solution contains the exponential function of a variable coefficient matrix. This coefficient matrix contains the unknown variable, reactivity. The predicted values of reactivity in the explicit form are determined replacing the exponential function of the coefficient matrix by two kinds, Backward Euler and Crank Nicholson, of the rational approximations. The nonlinear point kinetics equations changed to a linear system of the homogenous differential equations. The fundamental matrix of this linear system is calculated using the eigenvalues and the corresponding eigenvectors of the coefficient matrix. Stability of the efficient technique is defined and discussed. The efficient technique is applied to the point kinetics equations of six-groups of delayed neutrons with step, ramp, sinusoidal and the temperature feedback reactivities. The results of these efficient techniques are compared with the traditional methods.

  7. Measurements of the vacuum-plasma response in EXTRAP T2R using generic closed-loop subspace system identification

    Energy Technology Data Exchange (ETDEWEB)

    Olofsson, K. Erik J., E-mail: erik.olofsson@ee.kth.se [School of Electrical Engineering (EES), Royal Institute of Technology (KTH), Stockholm (Sweden); Brunsell, Per R.; Drake, James R. [School of Electrical Engineering (EES), Royal Institute of Technology (KTH), Stockholm (Sweden)

    2012-12-15

    Highlights: Black-Right-Pointing-Pointer Unstable plasma response safely measured using special signal processing techniques. Black-Right-Pointing-Pointer Prediction-capable MIMO models obtained. Black-Right-Pointing-Pointer Computational statistics employed to show physical content of these models. Black-Right-Pointing-Pointer Multifold cross-validation applied for the supervised learning problem. - Abstract: A multibatch formulation of a multi-input multi-output closed-loop subspace system identification method is employed for the purpose of obtaining control-relevant models of the vacuum-plasma response in the magnetic confinement fusion experiment EXTRAP T2R. The accuracy of the estimate of the plant dynamics is estimated by computing bootstrap replication statistics of the dataset. It is seen that the thus identified models exhibit both predictive capabilities and physical spectral properties.

  8. Semiclassical analysis of the weak-coupling limit of SU(2) lattice gauge theory: The subspace of constant fields

    International Nuclear Information System (INIS)

    Bartels, J.; Wu, T.T.

    1988-01-01

    This paper contains the first part of a systematic semiclassical analysis of the weak-coupling limit of lattice gauge theories, using the Hamiltonian formulation. The model consists of an N 3 cubic lattice of pure SU(2) Yang-Mills theory, and in this first part we limit ourselves to the subspace of constant field configurations. We investigate the flow of classical trajectories, with a particular emphasis on the existence and location of caustics. There the ground-state wave function is expected to peak. It is found that regions densely filled with caustics are very close to the origin, i.e., in the domain of weak field configurations. This strongly supports the expectation that caustics are essential for quantities of physical interest

  9. Organic Thin Films Deposited by Emulsion-Based, Resonant Infrared, Matrix-Assisted Pulsed Laser Evaporation: Fundamentals and Applications

    Science.gov (United States)

    Ge, Wangyao

    Thin film deposition techniques are indispensable to the development of modern technologies as thin film based optical coatings, optoelectronic devices, sensors, and biological implants are the building blocks of many complicated technologies, and their performance heavily depends on the applied deposition technique. Particularly, the emergence of novel solution-processed materials, such as soft organic molecules, inorganic compounds and colloidal nanoparticles, facilitates the development of flexible and printed electronics that are inexpensive, light weight, green and smart, and these thin film devices represent future trends for new technologies. One appealing feature of solution-processed materials is that they can be deposited into thin films using solution-processed deposition techniques that are straightforward, inexpensive, high throughput and advantageous to industrialize thin film based devices. However, solution-processed techniques rely on wet deposition, which has limitations in certain applications, such as multi-layered film deposition of similar materials and blended film deposition of dissimilar materials. These limitations cannot be addressed by traditional, vacuum-based deposition techniques because these dry approaches are often too energetic and can degrade soft materials, such as polymers, such that the performance of resulting thin film based devices is compromised. The work presented in this dissertation explores a novel thin film deposition technique, namely emulsion-based, resonant infrared, matrix-assisted pulsed laser evaporation (RIR-MAPLE), which combines characteristics of wet and dry deposition techniques for solution-processed materials. Previous studies have demonstrated the feasibility of emulsion-based RIR-MAPLE to deposit uniform and continuous organic, nanoparticle and blended films, as well as hetero-structures that otherwise are difficult to achieve. However, fundamental understanding of the growth mechanisms that govern

  10. Density scaling for multiplets

    International Nuclear Information System (INIS)

    Nagy, A

    2011-01-01

    Generalized Kohn-Sham equations are presented for lowest-lying multiplets. The way of treating non-integer particle numbers is coupled with an earlier method of the author. The fundamental quantity of the theory is the subspace density. The Kohn-Sham equations are similar to the conventional Kohn-Sham equations. The difference is that the subspace density is used instead of the density and the Kohn-Sham potential is different for different subspaces. The exchange-correlation functional is studied using density scaling. It is shown that there exists a value of the scaling factor ζ for which the correlation energy disappears. Generalized OPM and Krieger-Li-Iafrate (KLI) methods incorporating correlation are presented. The ζKLI method, being as simple as the original KLI method, is proposed for multiplets.

  11. 6DoF object pose measurement by a monocular manifold-based pattern recognition technique

    International Nuclear Information System (INIS)

    Kouskouridas, Rigas; Charalampous, Konstantinos; Gasteratos, Antonios

    2012-01-01

    In this paper, a novel solution to the compound problem of object recognition and 3D pose estimation is presented. An accurate measurement of the geometrical configuration of a recognized target, relative to a known coordinate system, is of fundamental importance and constitutes a prerequisite for several applications such as robot grasping or obstacle avoidance. The proposed method lays its foundations on the following assumptions: (a) the same object captured under varying viewpoints and perspectives represents data that could be projected onto a well-established and highly distinguishable subspace; (b) totally different objects observed under the same viewpoints and perspectives share identical 3D pose that can be sufficiently modeled to produce a generalized model. Toward this end, we propose an advanced architecture that allows both recognizing patterns and providing efficient solution for 6DoF pose estimation. We employ a manifold modeling architecture that is grounded on a part-based representation of an object, which in turn, is accomplished via an unsupervised clustering of the extracted visual cues. The main contributions of the proposed framework are: (a) the proposed part-based architecture requires minimum supervision, compared to other contemporary solutions, whilst extracting new features encapsulating both appearance and geometrical attributes of the objects; (b) contrary to related projects that extract high-dimensional data, thus, increasing the complexity of the system, the proposed manifold modeling approach makes use of low dimensionality input vectors; (c) the formulation of a novel input–output space mapping that outperforms the existing dimensionality reduction schemes. Experimental results justify our theoretical claims and demonstrate the superiority of our method comparing to other related contemporary projects. (paper)

  12. Fundamentals of universality in one-way quantum computation

    International Nuclear Information System (INIS)

    Nest, M van den; Duer, W; Miyake, A; Briegel, H J

    2007-01-01

    In this paper, we build a framework allowing for a systematic investigation of the fundamental issue: 'Which quantum states serve as universal resources for measurement-based (one-way) quantum computation?' We start our study by re-examining what is exactly meant by 'universality' in quantum computation, and what the implications are for universal one-way quantum computation. Given the framework of a measurement-based quantum computer, where quantum information is processed by local operations only, we find that the most general universal one-way quantum computer is one which is capable of accepting arbitrary classical inputs and producing arbitrary quantum outputs-we refer to this property as CQ-universality. We then show that a systematic study of CQ-universality in one-way quantum computation is possible by identifying entanglement features that are required to be present in every universal resource. In particular, we find that a large class of entanglement measures must reach its supremum on every universal resource. These insights are used to identify several families of states as being not universal, such as one-dimensional (1D) cluster states, Greenberger-Horne-Zeilinger (GHZ) states, W states, and ground states of non-critical 1D spin systems. Our criteria are strengthened by considering the efficiency of a quantum computation, and we find that entanglement measures must obey a certain scaling law with the system size for all efficient universal resources. This again leads to examples of non-universal resources, such as, e.g. ground states of critical 1D spin systems. On the other hand, we provide several examples of efficient universal resources, namely graph states corresponding to hexagonal, triangular and Kagome lattices. Finally, we consider the more general notion of encoded CQ-universality, where quantum outputs are allowed to be produced in an encoded form. Again we provide entanglement-based criteria for encoded universality. Moreover, we present a

  13. Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration

    CERN Document Server

    Noureldin, Aboelmagd; Georgy, Jacques

    2013-01-01

    Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration is an introduction to the field of Integrated Navigation Systems. It serves as an excellent reference for working engineers as well as textbook for beginners and students new to the area. The book is easy to read and understand with minimum background knowledge. The authors explain the derivations in great detail. The intermediate steps are thoroughly explained so that a beginner can easily follow the material. The book shows a step-by-step implementation of navigation algorithms and provides all the necessary details. It provides detailed illustrations for an easy comprehension. The book also demonstrates real field experiments and in-vehicle road test results with professional discussions and analysis. This work is unique in discussing the different INS/GPS integration schemes in an easy to understand and straightforward way. Those schemes include loosely vs tightly coupled, open loop vs closed loop, and many more.

  14. Portfolio optimization using fundamental indicators based on multi-objective EA

    CERN Document Server

    Silva, Antonio Daniel; Horta, Nuno

    2016-01-01

    This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two different chromosomes are used for representing different investment models with real constraints equivalents to the ones faced by managers of mutual funds, hedge funds, and pension funds. To validate the present solution two case studies are presented for the SP&500 for the period June 2010 until end of 2012. The simulations demonstrates that stock selection based on financial ratios is a combination that can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. In this case the optimizer found stocks with high return on investment in a conjunction with high rate of growth of the net income and a high profit margin. To obtain s...

  15. Fundamentalism and science

    Directory of Open Access Journals (Sweden)

    Massimo Pigliucci

    2006-06-01

    Full Text Available The many facets of fundamentalism. There has been much talk about fundamentalism of late. While most people's thought on the topic go to the 9/11 attacks against the United States, or to the ongoing war in Iraq, fundamentalism is affecting science and its relationship to society in a way that may have dire long-term consequences. Of course, religious fundamentalism has always had a history of antagonism with science, and – before the birth of modern science – with philosophy, the age-old vehicle of the human attempt to exercise critical thinking and rationality to solve problems and pursue knowledge. “Fundamentalism” is defined by the Oxford Dictionary of the Social Sciences1 as “A movement that asserts the primacy of religious values in social and political life and calls for a return to a 'fundamental' or pure form of religion.” In its broadest sense, however, fundamentalism is a form of ideological intransigence which is not limited to religion, but includes political positions as well (for example, in the case of some extreme forms of “environmentalism”.

  16. Fundamental design bases for independent core cooling in Swedish nuclear power reactors

    International Nuclear Information System (INIS)

    Jelinek, Tomas

    2015-01-01

    New regulations on design and construction of nuclear power plants came into force in 2005. The need of an independent core cooling system and if the regulations should include such a requirement was discussed. The Swedish Radiation Safety authority (SSM) decided to not include such a requirement because of open questions about the water balance and started to investigate the consequences of an independent core cooling system. The investigation is now finished and SSM is also looking at the lessons learned from the accident in Fukushima 2011. One of the most important measures in the Swedish national action plan is the implementation of an independent core cooling function for all Swedish power plants. SSM has investigated the basic design criteria for such a function where some important questions are the level of defence in depth and the acceptance criteria. There is also a question about independence between the levels of defence in depth that SSM have included in the criteria. Another issue that has to be taken into account is the complexity of the system and the need of automation where independence and simplicity are very strong criteria. In the beginning of 2014 a memorandum was finalized regarding fundamental design bases for independent core cooling in Swedish nuclear power reactors. A decision based on this memorandum with an implementation plan will be made in the first half of 2014. Sweden is also investigating the possibility to have armed personnel on site, which is not allowed currently. The result from the investigation will have impact on the possibility to use mobile equipment and the level of protection of permanent equipment. In this paper, SSM will present the memorandum for design bases for independent core cooling in Swedish nuclear power reactors that was finalized in March 20147 that also describe SSM's position regarding independence and automation of the independent core cooling function. This memorandum describes the Swedish

  17. Fundamental safety principles. Safety fundamentals

    International Nuclear Information System (INIS)

    2007-01-01

    This publication states the fundamental safety objective and ten associated safety principles, and briefly describes their intent and purpose. The fundamental safety objective - to protect people and the environment from harmful effects of ionizing radiation - applies to all circumstances that give rise to radiation risks. The safety principles are applicable, as relevant, throughout the entire lifetime of all facilities and activities - existing and new - utilized for peaceful purposes, and to protective actions to reduce existing radiation risks. They provide the basis for requirements and measures for the protection of people and the environment against radiation risks and for the safety of facilities and activities that give rise to radiation risks, including, in particular, nuclear installations and uses of radiation and radioactive sources, the transport of radioactive material and the management of radioactive waste

  18. Fundamental safety principles. Safety fundamentals

    International Nuclear Information System (INIS)

    2006-01-01

    This publication states the fundamental safety objective and ten associated safety principles, and briefly describes their intent and purpose. The fundamental safety objective - to protect people and the environment from harmful effects of ionizing radiation - applies to all circumstances that give rise to radiation risks. The safety principles are applicable, as relevant, throughout the entire lifetime of all facilities and activities - existing and new - utilized for peaceful purposes, and to protective actions to reduce existing radiation risks. They provide the basis for requirements and measures for the protection of people and the environment against radiation risks and for the safety of facilities and activities that give rise to radiation risks, including, in particular, nuclear installations and uses of radiation and radioactive sources, the transport of radioactive material and the management of radioactive waste

  19. Spatio-temporal evolution of the 2011 Prague, Oklahoma aftershock sequence revealed using subspace detection and relocation

    Science.gov (United States)

    McMahon, Nicole D; Aster, Richard C.; Yeck, William; McNamara, Daniel E.; Benz, Harley M.

    2017-01-01

    The 6 November 2011 Mw 5.7 earthquake near Prague, Oklahoma is the second largest earthquake ever recorded in the state. A Mw 4.8 foreshock and the Mw 5.7 mainshock triggered a prolific aftershock sequence. Utilizing a subspace detection method, we increase by fivefold the number of precisely located events between 4 November and 5 December 2011. We find that while most aftershock energy is released in the crystalline basement, a significant number of the events occur in the overlying Arbuckle Group, indicating that active Meeker-Prague faulting extends into the sedimentary zone of wastewater disposal. Although the number of aftershocks in the Arbuckle Group is large, comprising ~40% of the aftershock catalog, the moment contribution of Arbuckle Group earthquakes is much less than 1% of the total aftershock moment budget. Aftershock locations are sparse in patches that experienced large slip during the mainshock.

  20. Simulation Based Data Reconciliation for Monitoring Power Plant Efficiency

    International Nuclear Information System (INIS)

    Park, Sang Jun; Heo, Gyun Young

    2010-01-01

    Power plant efficiency is analyzed by using measured values, mass/energy balance principles, and several correlations. Since the measured values can have uncertainty depending on the accuracy of instrumentation, the results of plant efficiency should definitely have uncertainty. The certainty may occur due to either the randomness or the malfunctions of a process. In order to improve the accuracy of efficiency analysis, the data reconciliation (DR) is expected as a good candidate because the mathematical algorithm of the DR is based on the first principles such as mass and energy balance considering the uncertainty of instrumentation. It should be noted that the mass and energy balance model for analyzing power plant efficiency is equivalent to a steady-state simulation of a plant system. Therefore the DR for efficiency analysis necessitates the simulation which can deal with the uncertainty of instrumentation. This study will propose the algorithm of the simulation based DR which is applicable to power plant efficiency monitoring

  1. Effect of facade components on energy efficiency in office buildings

    International Nuclear Information System (INIS)

    Ihara, Takeshi; Gustavsen, Arild; Jelle, Bjørn Petter

    2015-01-01

    Highlights: • Investigation of facade properties for energy efficiency of Tokyo office buildings. • Higher reflectance for opaque parts may slightly reduce energy demand. • Lower window U-value and solar heat gain coefficient are potential solutions. • Decreased heating due to insulation did not always compensate increased cooling. • Fundamental data for adjustment of facade properties of buildings are provided. - Abstract: Properties of facade materials should be considered to determine which of them strongly affect building energy performance, regardless of the building shapes, scales, ideal locations, and building types, and thus may be able to promote energy efficiency in buildings. In this study, the effects of four fundamental facade properties related to the energy efficiency of office buildings in Tokyo, Japan, were investigated with the purpose of reducing the heating and cooling energy demands. Some fundamental design factors such as volume and shape were also considered. It was found that the reduction in both the solar heat gain coefficient and window U-value and increase in the solar reflectance of the opaque parts are promising measures for reducing the energy demand. Conversely, the reduction in the U-value of the opaque parts decreased the heating energy demand, and this was accompanied by an increase in the cooling energy demand in some cases because the total energy demands were predominantly for cooling. The above-mentioned promising measures for reducing building energy demands are thus recommended for use, and an appropriate U-value should be applied to the opaque parts based on careful considerations. This study provides some fundamental ideas to adjust the facade properties of buildings.

  2. Credit cycles and macro fundamentals

    NARCIS (Netherlands)

    Koopman, S.J.; Kraeussl, R.G.W.; Lucas, A.; Monteiro, A.

    2009-01-01

    We use an intensity-based framework to study the relation between macroeconomic fundamentals and cycles in defaults and rating activity. Using Standard and Poor's U.S. corporate rating transition and default data over the period 1980-2005, we directly estimate the default and rating cycle from micro

  3. Fundamental rights and the EU internal market: just how fundamental are the EU treaty freedoms? A normative enquiry based on John Rawls' political philosophy

    NARCIS (Netherlands)

    de Boer, N.J.

    2013-01-01

    This article assesses whether the EU Treaty freedoms - the free movement of goods, persons, services and capital - should be considered as fundamental rights which are hierarchically equal to other fundamental rights. It uses the political philosophy of John Rawls to assess why we should attach

  4. Escola de ensino fundamental(s em movimento – movimento na escola de ensino fundamental

    Directory of Open Access Journals (Sweden)

    Reiner Hildebrandt-Stramann

    2007-12-01

    Full Text Available A escola de ensino fundamental na Alemanha sofreu movimento nos últimos 15 anos, porque, entre outros motivos, entrou movimento nessas escolas. Esse jogo de palavras chama atenção a duas linhas de trabalho que determinam a discussão na atual pedagogia escolar. O presente trabalho revela essas duas perspectivas. Uma das linhas está relacionada ao atual processo de mudança na pedagogia escolar. Essa prediz que a escola de ensino fundamental deve ser um lugar de aprendizagem e de vivência para as crianças. A outra linha tem a ver com o jogo de palavras ancorado a esses processos da pedagogia do movimento, a qual ganha cada vez maiores dimensões. A escola de ensino fundamental deve ser vista sob a perspectiva do movimento e transformada em um lugar de movimento.

  5. THE FUNDAMENTS OF EXPLANATORY CAUSES

    Directory of Open Access Journals (Sweden)

    Lavinia Mihaela VLĂDILĂ

    2015-07-01

    Full Text Available The new Criminal Code in the specter of the legal life the division of causes removing the criminal feature of the offence in explanatory causes and non-attributable causes. This dichotomy is not without legal and factual fundaments and has been subjected to doctrinaire debates even since the period when the Criminal Code of 1969 was still in force. From our perspective, one of the possible legal fundaments of the explanatory causes results from that the offence committed is based on the protection of a right at least equal with the one prejudiced by the action of aggression, salvation, by the legal obligation imposed or by the victim’s consent.

  6. EFFICIENT POLYMER PHOTOVOLTAIC DEVICES BASED ON POLYMER D-A BLENDS

    Institute of Scientific and Technical Information of China (English)

    Xian-yu Deng; Li-ping Zheng; Yue-qi Mo; Gang Yu; Wei Yang; Wen-hua Weng; Yong Cao

    2001-01-01

    Recent work demonstrated that efficient solar-energy conversion could be achieved in polymer photovoltaic cells (PVCs) based on interpenetrating bi-continuous networks[1,2]. In this paper we present a comprehensive study on improving energy conversion efficiencies of PVCs based on composite films of MEHPPV and fullerene derivatives. Carrier collection efficiency of ca. 30% el/ph and energy conversion efficiency of 3.9% were achieved at 500 nm. At reverse bias of 15 V, the photosensitivity reached 0.8 A/W, corresponding to a quantum efficiency over 100% el/ph. These results suggest that high efficiency photoelectric conversion can be achieved in polymer devices with M-P-M structure. These devices are promising for practical applications such as plastic solar cells and plastic photodetectors.

  7. Uncertainty about fundamentals and herding behavior in the FOREX market

    Science.gov (United States)

    Kaltwasser, Pablo Rovira

    2010-03-01

    It is traditionally assumed in finance models that the fundamental value of assets is known with certainty. Although this is an appealing simplifying assumption it is by no means based on empirical evidence. A simple heterogeneous agent model of the exchange rate is presented. In the model, traders do not observe the true underlying fundamental exchange rate and as a consequence they base their trades on beliefs about this variable. Despite the fact that only fundamentalist traders operate in the market, the model belongs to the heterogeneous agent literature, as traders have different beliefs about the fundamental rate.

  8. Integrating the Fundamentals of Care framework in baccalaureate nursing education

    DEFF Research Database (Denmark)

    Voldbjerg, Siri; Laugesen, Britt; Bahnsen, Iben Bøgh

    2018-01-01

    AIM AND OBJECTIVES: To describe and discuss the process of integrating the Fundamentals of Care framework in a baccalaureate nursing education at a School of Nursing in Denmark. BACKGROUND: Nursing education plays an essential role in educating nurses to work within health care systems in which...... Fundamentals of Care framework has been integrated in nursing education at a School of Nursing in Denmark. DESIGN AND METHODS: Discursive paper using an adjusted descriptive case study design for describing and discussing the process of integrating the conceptual Fundamentals of Care Framework in nursing...... education. RESULTS: The process of integrating the Fundamentals of Care framework is illuminated through a description of the context, in which the process occurs including the faculty members, lectures, case-based work and simulation lab in nursing education. Based on this description, opportunities...

  9. Fracture analysis of a transversely isotropic high temperature superconductor strip based on real fundamental solutions

    International Nuclear Information System (INIS)

    Gao, Zhiwen; Zhou, Youhe

    2015-01-01

    Highlights: • We studied fracture problem in HTS based on real fundamental solutions. • When the thickness of HTS strip increases the SIF decrease. • A higher applied field leads to a larger stress intensity factor. • The greater the critical current density is, the smaller values of the SIF is. - Abstract: Real fundamental solution for fracture problem of transversely isotropic high temperature superconductor (HTS) strip is obtained. The superconductor E–J constitutive law is characterized by the Bean model where the critical current density is independent of the flux density. Fracture analysis is performed by the methods of singular integral equations which are solved numerically by Gauss–Lobatto–Chybeshev (GSL) collocation method. To guarantee a satisfactory accuracy, the convergence behavior of the kernel function is investigated. Numerical results of fracture parameters are obtained and the effects of the geometric characteristics, applied magnetic field and critical current density on the stress intensity factors (SIF) are discussed

  10. DOE/OER-sponsored basic research in high-efficiency photovoltaics

    Energy Technology Data Exchange (ETDEWEB)

    Deb, S.K.; Benner, J.P. [National Renewable Energy Lab., Golden, CO (United States)

    1996-05-01

    A high-efficiency photovoltaic project involving many of the national laboratories and several universities has been initiated under the umbrella of the U.S. Department of Energy (DOE) Center of Excellence for the Synthesis and Processing of Advanced Materials. The objectives of this project are to generate advances in fundamental scientific understanding that will impact the efficiency, cost and reliability of thin-film photovoltaic cells. The project is focused on two areas. (1) Silicon-Based Thin Films, in which key scientific and technological problems involving amorphous and polycrystalline silicon thin films will be addressed, and (2) Next-Generation Thin-Film Photovoltaics, which will be concerned with the possibilities of new advances and breakthroughs in the materials and physics of photovoltaics using non-silicon-based materials.

  11. Tariff-based incentives for improving coal-power-plant efficiencies in India

    International Nuclear Information System (INIS)

    Chikkatur, Ananth P.; Sagar, Ambuj D.; Abhyankar, Nikit; Sreekumar, N.

    2007-01-01

    Improving the efficiency of coal-based power plants plays an important role in improving the performance of India's power sector. It allows for increased consumer benefits through cost reduction, while enhancing energy security and helping reduce local and global pollution through more efficient coal use. A focus on supply-side efficiency also complements other ongoing efforts on end-use efficiency. The recent restructuring of the Indian electricity sector offers an important route to improving power plant efficiency, through regulatory mechanisms that allow for an independent tariff setting process for bulk purchases of electricity from generators. Current tariffs based on normative benchmarks for performance norms are hobbled by information asymmetry (where regulators do not have access to detailed performance data). Hence, we propose a new incentive scheme that gets around the asymmetry problem by setting performance benchmarks based on actual efficiency data, rather than on a normative basis. The scheme provides direct tariff-based incentives for efficiency improvements, while benefiting consumers by reducing electricity costs in the long run. This proposal might also be useful for regulators in other countries to incorporate similar incentives for efficiency improvement in power generation

  12. Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure

    International Nuclear Information System (INIS)

    Xu, Xin; Cui, Qiang

    2017-01-01

    This paper focuses on evaluating airline energy efficiency, which is firstly divided into four stages: Operations Stage, Fleet Maintenance Stage, Services Stage and Sales Stage. The new four-stage network structure of airline energy efficiency is a modification of existing models. A new approach, integrated with Network Epsilon-based Measure and Network Slacks-based Measure, is applied to assess the overall energy efficiency and divisional efficiency of 19 international airlines from 2008 to 2014. The influencing factors of airline energy efficiency are analyzed through the regression analysis. The results indicate the followings: 1. The integrated model can identify the benchmarking airlines in the overall system and stages. 2. Most airlines' energy efficiencies keep steady during the period, except for some sharply fluctuations. The efficiency decreases mainly centralized in the year 2008–2011, affected by the financial crisis in the USA. 3. The average age of fleet is positively correlated with the overall energy efficiency, and each divisional efficiency has different significant influencing factors. - Highlights: • An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure is developed. • 19 airlines' energy efficiencies are evaluated. • Garuda Indonesia has the highest overall energy efficiency.

  13. Energy-efficiency based classification of the manufacturing workstation

    Science.gov (United States)

    Frumuşanu, G.; Afteni, C.; Badea, N.; Epureanu, A.

    2017-08-01

    EU Directive 92/75/EC established for the first time an energy consumption labelling scheme, further implemented by several other directives. As consequence, nowadays many products (e.g. home appliances, tyres, light bulbs, houses) have an EU Energy Label when offered for sale or rent. Several energy consumption models of manufacturing equipments have been also developed. This paper proposes an energy efficiency - based classification of the manufacturing workstation, aiming to characterize its energetic behaviour. The concept of energy efficiency of the manufacturing workstation is defined. On this base, a classification methodology has been developed. It refers to specific criteria and their evaluation modalities, together to the definition & delimitation of energy efficiency classes. The energy class position is defined after the amount of energy needed by the workstation in the middle point of its operating domain, while its extension is determined by the value of the first coefficient from the Taylor series that approximates the dependence between the energy consume and the chosen parameter of the working regime. The main domain of interest for this classification looks to be the optimization of the manufacturing activities planning and programming. A case-study regarding an actual lathe classification from energy efficiency point of view, based on two different approaches (analytical and numerical) is also included.

  14. Energy efficient thermal management of data centers

    CERN Document Server

    Kumar, Pramod

    2012-01-01

    Energy Efficient Thermal Management of Data Centers examines energy flow in today's data centers. Particular focus is given to the state-of-the-art thermal management and thermal design approaches now being implemented across the multiple length scales involved. The impact of future trends in information technology hardware, and emerging software paradigms such as cloud computing and virtualization, on thermal management are also addressed. The book explores computational and experimental characterization approaches for determining temperature and air flow patterns within data centers. Thermodynamic analyses using the second law to improve energy efficiency are introduced and used in proposing improvements in cooling methodologies. Reduced-order modeling and robust multi-objective design of next generation data centers are discussed. This book also: Provides in-depth treatment of energy efficiency ideas based on  fundamental heat transfer, fluid mechanics, thermodynamics, controls, and computer science Focus...

  15. Secure and Efficient Protocol for Vehicular Ad Hoc Network with Privacy Preservation

    Directory of Open Access Journals (Sweden)

    Choi Hyoung-Kee

    2011-01-01

    Full Text Available Security is a fundamental issue for promising applications in a VANET. Designing a secure protocol for a VANET that accommodates efficiency, privacy, and traceability is difficult because of the contradictions between these qualities. In this paper, we present a secure yet efficient protocol for a VANET that satisfies these security requirements. Although much research has attempted to address similar issues, we contend that our proposed protocol outperforms other proposals that have been advanced. This claim is based on observations that show that the proposed protocol has such strengths as light computational load, efficient storage management, and dependability.

  16. Fundamental volatility and stock returns : does fundamental volatility explain stock returns?

    OpenAIRE

    Selboe, Guner K.; Virdee, Jaspal Singh

    2017-01-01

    In this thesis, we investigate whether the fundamental uncertainty can explain the crosssection of stock returns. To measure the fundamental uncertainty, we estimate rolling standard deviations and accounting betas of four different fundamentals: revenues, gross profit, earnings and cash flows. The standard deviation and the beta of revenues significantly explain returns in the Fama-Macbeth procedure, but only appears significant among smaller stocks in the portfolio formation ...

  17. Photonic Design: From Fundamental Solar Cell Physics to Computational Inverse Design

    Science.gov (United States)

    Miller, Owen Dennis

    Photonic innovation is becoming ever more important in the modern world. Optical systems are dominating shorter and shorter communications distances, LED's are rapidly emerging for a variety of applications, and solar cells show potential to be a mainstream technology in the energy space. The need for novel, energy-efficient photonic and optoelectronic devices will only increase. This work unites fundamental physics and a novel computational inverse design approach towards such innovation. The first half of the dissertation is devoted to the physics of high-efficiency solar cells. As solar cells approach fundamental efficiency limits, their internal physics transforms. Photonic considerations, instead of electronic ones, are the key to reaching the highest voltages and efficiencies. Proper photon management led to Alta Device's recent dramatic increase of the solar cell efficiency record to 28.3%. Moreover, approaching the Shockley-Queisser limit for any solar cell technology will require light extraction to become a part of all future designs. The second half of the dissertation introduces inverse design as a new computational paradigm in photonics. An assortment of techniques (FDTD, FEM, etc.) have enabled quick and accurate simulation of the "forward problem" of finding fields for a given geometry. However, scientists and engineers are typically more interested in the inverse problem: for a desired functionality, what geometry is needed? Answering this question breaks from the emphasis on the forward problem and forges a new path in computational photonics. The framework of shape calculus enables one to quickly find superior, non-intuitive designs. Novel designs for optical cloaking and sub-wavelength solar cell applications are presented.

  18. A physiological foundation for the nutrition-based efficiency wage model

    DEFF Research Database (Denmark)

    Dalgaard, Carl-Johan Lars; Strulik, Holger

    2011-01-01

    Drawing on recent research on allometric scaling and energy consumption, the present paper develops a nutrition-based efficiency wage model from first principles. The biologically micro-founded model allows us to address empirical criticism of the original nutrition-based efficiency wage model...

  19. Fundamental Frequency Estimation of the Speech Signal Compressed by MP3 Algorithm Using PCC Interpolation

    Directory of Open Access Journals (Sweden)

    MILIVOJEVIC, Z. N.

    2010-02-01

    Full Text Available In this paper the fundamental frequency estimation results of the MP3 modeled speech signal are analyzed. The estimation of the fundamental frequency was performed by the Picking-Peaks algorithm with the implemented Parametric Cubic Convolution (PCC interpolation. The efficiency of PCC was tested for Catmull-Rom, Greville and Greville two-parametric kernel. Depending on MSE, a window that gives optimal results was chosen.

  20. Area-based management and fishing efficiency

    DEFF Research Database (Denmark)

    Marchal, P.; Ulrich, Clara; Pastoors, M.

    2002-01-01

    The scope of this study is to investigate the extent to which area-based management may have influenced the fishing efficiency of the Danish and Dutch demersal fleets harvesting cod, plaice and sole in the North Sea. Special consideration is given to the 'plaice box', a restricted area where...... fishing is prohibited to towed-gear fleets of horsepower exceeding 300 hp. An index of fishing power is calculated as the log-ratio between the catch per unit effort (CPUE) of any vessel and some survey abundance index. Annual trends in fishing are calculated as the year-effect derived from a general...... linear model (GLM) analysis of the index of fishing power. The fishing efficiency of Danish gill-netters and, to some extent, Danish seiners, has overall increased inside the 'plaice box', whilst remaining relatively stable outside. However, the fishing efficiency of the other exemption fleets has...

  1. An Economics-Based Second Law Efficiency

    Directory of Open Access Journals (Sweden)

    John H. Lienhard

    2013-07-01

    Full Text Available Second Law efficiency is a useful parameter for characterizing the energy requirements of a system in relation to the limits of performance prescribed by the Laws of Thermodynamics. However, since energy costs typically represent less than 50% of the overall cost of product for many large-scale plants (and, in particular, for desalination plants, it is useful to have a parameter that can characterize both energetic and economic effects. In this paper, an economics-based Second Law efficiency is defined by analogy to the exergetic Second Law efficiency and is applied to several desalination systems. It is defined as the ratio of the minimum cost of producing a product divided by the actual cost of production. The minimum cost of producing the product is equal to the cost of the primary source of energy times the minimum amount of energy required, as governed by the Second Law. The analogy is used to show that thermodynamic irreversibilities can be assigned costs and compared directly to non-energetic costs, such as capital expenses, labor and other operating costs. The economics-based Second Law efficiency identifies costly sources of irreversibility and places these irreversibilities in context with the overall system costs. These principles are illustrated through three case studies. First, a simple analysis of multistage flash and multiple effect distillation systems is performed using available data. Second, a complete energetic and economic model of a reverse osmosis plant is developed to show how economic costs are influenced by energetics. Third, a complete energetic and economic model of a solar powered direct contact membrane distillation system is developed to illustrate the true costs associated with so-called free energy sources.

  2. The constitutive compatibility method for identification of material parameters based on full-field measurements

    KAUST Repository

    Moussawi, Ali; Lubineau, Gilles; Florentin, É ric; Blaysat, Benoî t

    2013-01-01

    We revisit here the concept of the constitutive relation error for the identification of elastic material parameters based on image correlation. An additional concept, so called constitutive compatibility of stress, is introduced defining a subspace

  3. Energy Landscape of Pentapeptides in a Higher-Order (ϕ,ψ Conformational Subspace

    Directory of Open Access Journals (Sweden)

    Karim M. ElSawy

    2016-01-01

    Full Text Available The potential energy landscape of pentapeptides was mapped in a collective coordinate principal conformational subspace derived from principal component analysis of a nonredundant representative set of protein structures from the PDB. Three pentapeptide sequences that are known to be distinct in terms of their secondary structure characteristics, (Ala5, (Gly5, and Val.Asn.Thr.Phe.Val, were considered. Partitioning the landscapes into different energy valleys allowed for calculation of the relative propensities of the peptide secondary structures in a statistical mechanical framework. The distribution of the observed conformations of pentapeptide data showed good correspondence to the topology of the energy landscape of the (Ala5 sequence where, in accord with reported trends, the α-helix showed a predominant propensity at 298 K. The topography of the landscapes indicates that the stabilization of the α-helix in the (Ala5 sequence is enthalpic in nature while entropic factors are important for stabilization of the β-sheet in the Val.Asn.Thr.Phe.Val sequence. The results indicate that local interactions within small pentapeptide segments can lead to conformational preference of one secondary structure over the other where account of conformational entropy is important in order to reveal such preference. The method, therefore, can provide critical structural information for ab initio protein folding methods.

  4. An Efficient Sleepy Algorithm for Particle-Based Fluids

    Directory of Open Access Journals (Sweden)

    Xiao Nie

    2014-01-01

    Full Text Available We present a novel Smoothed Particle Hydrodynamics (SPH based algorithm for efficiently simulating compressible and weakly compressible particle fluids. Prior particle-based methods simulate all fluid particles; however, in many cases some particles appearing to be at rest can be safely ignored without notably affecting the fluid flow behavior. To identify these particles, a novel sleepy strategy is introduced. By utilizing this strategy, only a portion of the fluid particles requires computational resources; thus an obvious performance gain can be achieved. In addition, in order to resolve unphysical clumping issue due to tensile instability in SPH based methods, a new artificial repulsive force is provided. We demonstrate that our approach can be easily integrated with existing SPH based methods to improve the efficiency without sacrificing visual quality.

  5. Chunk-Based Energy Efficient Resource Allocation in OFDMA Systems

    Directory of Open Access Journals (Sweden)

    Yong Li

    2013-01-01

    Full Text Available Energy efficiency (EE capacity analysis of the chunk-based resource allocation is presented by considering the minimum spectrum efficiency (SE constraint in downlink multiuser orthogonal frequency division multiplexing (OFDM systems. Considering the minimum SE requirement, an optimization problem to maximize EE with limited transmit power is formulated over frequency selective channels. Based on this model, a low-complexity energy efficient resource allocation is proposed. The effects of system parameters, such as the average channel gain-to-noise ratio (CNR and the number of subcarriers per chunk, are evaluated. Numerical results demonstrate the effectiveness of the proposed scheme for balancing the EE and SE.

  6. Cadaver-Based Simulation Increases Resident Confidence, Initial Exposure to Fundamental Techniques, and May Augment Operative Autonomy.

    Science.gov (United States)

    Kim, Steven C; Fisher, Jeremy G; Delman, Keith A; Hinman, Johanna M; Srinivasan, Jahnavi K

    Surgical simulation is an important adjunct in surgical education. The majority of operative procedures can be simplified to core components. This study aimed to quantify a cadaver-based simulation course utility in improving exposure to fundamental maneuvers, resident and attending confidence in trainee capability, and if this led to earlier operative independence. A list of fundamental surgical procedures was established by a faculty panel. Residents were assigned to a group led by a chief resident. Residents performed skills on cadavers appropriate for PGY level. A video-recorded examination where they narrated and demonstrated a task independently was then graded by attendings using standardized rubrics. Participants completed surveys regarding improvements in knowledge and confidence. The course was conducted at the Emory University School of Medicine and the T3 Laboratories in Atlanta, GA. A total of 133 residents and 41 attendings participated in the course. 133 (100%) participating residents and 32 (78%) attendings completed surveys. Resident confidence in completing the assigned skill independently increased from 3 (2-3) to 4 (3-4), p 80%), p < 0.04. Attendings were more likely to grant autonomy in the operating room after this exercise (4 [3-5]). A cadaveric skills course focused on fundamental maneuvers with objective confirmation of success is a viable adjunct to clinical operative experience. Residents were formally exposed to fundamental surgical maneuvers earlier as a result of this course. This activity improved both resident and attending confidence in trainee operative skill, resulting in increased attending willingness to grant a higher level of autonomy in the operating room. Copyright © 2016 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

  7. Experimental measurements of competition between fundamental and second harmonic emission in a quasi-optical gyrotron

    International Nuclear Information System (INIS)

    Alberti, S.; Pedrozzi, M.; Tran, M.Q.; Hogge, J.P.; Tran, T.M.; Muggli, P.; Joedicke, B.; Mathews, H.G.

    1990-04-01

    A quasi-optical gyrotron (QOG) designed for operation at the fundamental (Ω ce ≅100 GHz) exhibits simultaneous emission at Ω ce and 2Ω ce (second harmonic). For a beam current of 4 A, 20% of the total RF power is emitted at the second harmonic. The experimental measurements show that the excitation of the second harmonic is only possible when the fundamental is present. The frequency of the second harmonic is locked by the frequency of the fundamental. Experimental evidence shows that when the second harmonic is not excited, total efficiency is enhanced. (author) 6 refs., 5 figs., 1 tab

  8. Fundamentally Flawed: Extension Administrative Practice (Part 1).

    Science.gov (United States)

    Patterson, Thomas F., Jr.

    1997-01-01

    Extension's current administrative techniques are based on the assumptions of classical management from the early 20th century. They are fundamentally flawed and inappropriate for the contemporary workplace. (SK)

  9. Area-based management and fishing efficiency

    NARCIS (Netherlands)

    Marchal, P.; Ulrich, C.; Pastoors, M.

    2002-01-01

    The scope of this study is to investigate the extent to which area-based management may have influenced the fishing efficiency of the Danish and Dutch demersal fleets harvesting cod, plaice and sole in the North Sea. Special consideration is given to the `plaice box', a restricted area where fishing

  10. Fundamental aspects affecting the return on investment from solar power plants

    International Nuclear Information System (INIS)

    Cintula, B.; Viglas, D.

    2012-01-01

    The article deals with fundamental parameters of solar cells-conversion efficiency of solar radiation into electricity and price of solar cells. These two aspects affect each other, so it is important to deal with both at once. In introduction are described the theoretical solutions about efficiency analysis. Furthermore the article is focused on a description of materials used in the photovoltaic cells. In addition, the article shows the price trend of photovoltaic cells for the last year. Finally, these two aspects are evaluated for return on investment in photovoltaic power plants. (Authors)

  11. Indoor Modelling from Slam-Based Laser Scanner: Door Detection to Envelope Reconstruction

    Science.gov (United States)

    Díaz-Vilariño, L.; Verbree, E.; Zlatanova, S.; Diakité, A.

    2017-09-01

    Updated and detailed indoor models are being increasingly demanded for various applications such as emergency management or navigational assistance. The consolidation of new portable and mobile acquisition systems has led to a higher availability of 3D point cloud data from indoors. In this work, we explore the combined use of point clouds and trajectories from SLAM-based laser scanner to automate the reconstruction of building indoors. The methodology starts by door detection, since doors represent transitions from one indoor space to other, which constitutes an initial approach about the global configuration of the point cloud into building rooms. For this purpose, the trajectory is used to create a vertical point cloud profile in which doors are detected as local minimum of vertical distances. As point cloud and trajectory are related by time stamp, this feature is used to subdivide the point cloud into subspaces according to the location of the doors. The correspondence between subspaces and building rooms is not unambiguous. One subspace always corresponds to one room, but one room is not necessarily depicted by just one subspace, for example, in case of a room containing several doors and in which the acquisition is performed in a discontinue way. The labelling problem is formulated as combinatorial approach solved as a minimum energy optimization. Once the point cloud is subdivided into building rooms, envelop (conformed by walls, ceilings and floors) is reconstructed for each space. The connectivity between spaces is included by adding the previously detected doors to the reconstructed model. The methodology is tested in a real case study.

  12. Catalyst design for enhanced sustainability through fundamental surface chemistry.

    Science.gov (United States)

    Personick, Michelle L; Montemore, Matthew M; Kaxiras, Efthimios; Madix, Robert J; Biener, Juergen; Friend, Cynthia M

    2016-02-28

    Decreasing energy consumption in the production of platform chemicals is necessary to improve the sustainability of the chemical industry, which is the largest consumer of delivered energy. The majority of industrial chemical transformations rely on catalysts, and therefore designing new materials that catalyse the production of important chemicals via more selective and energy-efficient processes is a promising pathway to reducing energy use by the chemical industry. Efficiently designing new catalysts benefits from an integrated approach involving fundamental experimental studies and theoretical modelling in addition to evaluation of materials under working catalytic conditions. In this review, we outline this approach in the context of a particular catalyst-nanoporous gold (npAu)-which is an unsupported, dilute AgAu alloy catalyst that is highly active for the selective oxidative transformation of alcohols. Fundamental surface science studies on Au single crystals and AgAu thin-film alloys in combination with theoretical modelling were used to identify the principles which define the reactivity of npAu and subsequently enabled prediction of new reactive pathways on this material. Specifically, weak van der Waals interactions are key to the selectivity of Au materials, including npAu. We also briefly describe other systems in which this integrated approach was applied. © 2016 The Author(s).

  13. MARKOWITZ' MODEL WITH FUNDAMENTAL AND TECHNICAL ANALYSIS – COMPLEMENTARY METHODS OR NOT

    Directory of Open Access Journals (Sweden)

    Branka Marasović

    2011-02-01

    Full Text Available As it is well known there are few “starting points” in portfolio optimization process, i.e. in the stock selection process. Famous Markowitz’ optimization model is unavoidable in this job. On the other side, someone may say that the indicators of the fundamental analysis must be the starting point. Beside that, the suggestions of the technical analysis must be taken into consideration. There are really numerous studies of the each approach separately, but it is almost impossible to find researches combining these approaches in logic and efficient unity. The main task of the paper is to find out if these approaches are complementary and if they are, how to apply them as efficient unit process. The empirical part of the study uses share sample from the Croatian stock market. Beside Markowitz’ MV model, fundamental and technical analysis, big role in the paper has an original multi-criterion approach.

  14. Efficient, Broadband and Wide-Angle Hot-Electron Transduction using Metal-Semiconductor Hyperbolic Metamaterials

    KAUST Repository

    Sakhdari, Maryam

    2016-05-20

    Hot-electron devices are emerging as promising candidates for the transduction of optical radiation into electrical current, as they enable photodetection and solar/infrared energy harvesting at sub-bandgap wavelengths. Nevertheless, poor photoconversion quantum yields and low bandwidth pose fundamental challenge to fascinating applications of hot-electron optoelectronics. Based on a novel hyperbolic metamaterial (HMM) structure, we theoretically propose a vertically-integrated hot-electron device that can efficiently couple plasmonic excitations into electron flows, with an external quantum efficiency approaching the physical limit. Further, this metamaterial-based device can have a broadband and omnidirectional response at infrared and visible wavelengths. We believe that these findings may shed some light on designing practical devices for energy-efficient photodetection and energy harvesting beyond the bandgap spectral limit.

  15. Another argument against fundamental scalars

    International Nuclear Information System (INIS)

    Joglekar, S.D.

    1990-01-01

    An argument, perhaps not as strong, which is based on the inclusion of interaction with external gravity into a theory describing strong, electromagnetic and weak interactions is presented. The argument is related to the basis of the common belief which favours a renormalizable action against a non-renormalizable action as a candidate for a fundamental theory. (author). 12 refs

  16. Fundamentals of electronics

    CERN Document Server

    Schubert, Thomas F

    2015-01-01

    This book, Electronic Devices and Circuit Application, is the first of four books of a larger work, Fundamentals of Electronics. It is comprised of four chapters describing the basic operation of each of the four fundamental building blocks of modern electronics: operational amplifiers, semiconductor diodes, bipolar junction transistors, and field effect transistors. Attention is focused on the reader obtaining a clear understanding of each of the devices when it is operated in equilibrium. Ideas fundamental to the study of electronic circuits are also developed in the book at a basic level to

  17. Ligand efficiency based approach for efficient virtual screening of compound libraries.

    Science.gov (United States)

    Ke, Yi-Yu; Coumar, Mohane Selvaraj; Shiao, Hui-Yi; Wang, Wen-Chieh; Chen, Chieh-Wen; Song, Jen-Shin; Chen, Chun-Hwa; Lin, Wen-Hsing; Wu, Szu-Huei; Hsu, John T A; Chang, Chung-Ming; Hsieh, Hsing-Pang

    2014-08-18

    Here we report for the first time the use of fit quality (FQ), a ligand efficiency (LE) based measure for virtual screening (VS) of compound libraries. The LE based VS protocol was used to screen an in-house database of 125,000 compounds to identify aurora kinase A inhibitors. First, 20 known aurora kinase inhibitors were docked to aurora kinase A crystal structure (PDB ID: 2W1C); and the conformations of docked ligand were used to create a pharmacophore (PH) model. The PH model was used to screen the database compounds, and rank (PH rank) them based on the predicted IC50 values. Next, LE_Scale, a weight-dependant LE function, was derived from 294 known aurora kinase inhibitors. Using the fit quality (FQ = LE/LE_Scale) score derived from the LE_Scale function, the database compounds were reranked (PH_FQ rank) and the top 151 (0.12% of database) compounds were assessed for aurora kinase A inhibition biochemically. This VS protocol led to the identification of 7 novel hits, with compound 5 showing aurora kinase A IC50 = 1.29 μM. Furthermore, testing of 5 against a panel of 31 kinase reveals that it is selective toward aurora kinase A & B, with <50% inhibition for other kinases at 10 μM concentrations and is a suitable candidate for further development. Incorporation of FQ score in the VS protocol not only helped identify a novel aurora kinase inhibitor, 5, but also increased the hit rate of the VS protocol by improving the enrichment factor (EF) for FQ based screening (EF = 828), compared to PH based screening (EF = 237) alone. The LE based VS protocol disclosed here could be applied to other targets for hit identification in an efficient manner. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  18. Promotion of energy efficiency in enterprises

    International Nuclear Information System (INIS)

    Beltrani, G.; Schelske, O.; Peter, D.; Oettli, B.

    2003-01-01

    This comprehensive report for the Swiss Federal Office of Energy (SFOE) presents the results of a study made within the framework of the research programme on energy-economics fundamentals on how the energy efficiency of enterprises can be improved. The report first examines the present state of affairs in Swiss enterprises and looks into the interaction of energy efficiency and environmental management systems. ISO 14001 certification is discussed and examples are given of the responses of various enterprises to a survey concerning the role of energy efficiency in environmental management. Both hindrances and success factors for the embedding of energy-efficiency measures in environmental management activities are discussed and examples are given. Instruments available in Switzerland and from abroad that can be used to promote energy efficiency in enterprises are discussed. Four particular instruments are presented; guidelines and computer-based tools that help in the making of energy-relevant investment decisions, incentives to take part in an energy-benchmark system for small and medium-sized enterprises (SME), low-interest loans for investments in energy-efficiency for SMEs and the closer definition of 'continuous improvement' of energy efficiency within the framework of ISO 14001. The results of a survey amongst those involved are discussed. The report is concluded with recommendations for the implementation of the guidelines and for improvements in the integration of energy efficiency in environmental management systems

  19. New generation of membrane efficient water-based drilling fluids: pragmatic and cost-effective solutions to borehole stability problems

    Energy Technology Data Exchange (ETDEWEB)

    Tare, U.A. [Haliburton, Calgary, AB (Canada); Mody, F.K. [Shell International E and P Inc., Calgary, AB (Canada); Tan, C.P. [CSIRO Petroleum, Kensington, WA (Australia)

    2002-06-01

    Drilling and completion operations in shales often suffer as a result of wellbore instability. Mechanical failure of the rock around a wellbore is the primary cause of shale instability. This process can be exacerbated by physico-chemical interactions between drilling fluids and shales. Water-based drilling fluids are used more and more due to environmental awareness that becomes more prevalent. Wellbore instability problems can however result from an improper application of water-based drilling fluids in those cases where drilling occurs in sensitive clay-rich formations. To meet the requirements of the petroleum industry, considerable collaborative efforts were expanded in the development of innovative environmentally acceptable water-based drilling fluids. In this paper, the authors describe the process that leads to the development of these drilling fluids. It is possible to achieve shale stability through an osmotic outflow of pore fluid and prevention/minimization of mud pressure penetration, as laboratory experiments on shale samples under realistic downhole conditions exposed to these drilling fluids prove. High membrane efficiencies, in excess of 80 per cent, were generated by this new generation of membrane efficient water-based drilling fluids. Drilling objectives resulting from an improved application of water-based drilling fluids are made possible by a fundamental understanding of the main drilling fluid-shale interaction mechanisms for shale stability and the application of experimental data to field conditions. The authors indicate that the achievement of trouble-free drilling of shales and notable reductions in non-productive time is accomplished by following the practical guidelines included in this paper for maintaining shale stability with the new generation of water-based drilling fluids. 8 refs., 2 tabs., 4 figs.

  20. Optimal projection of observations in a Bayesian setting

    KAUST Repository

    Giraldi, Loic

    2018-03-18

    Optimal dimensionality reduction methods are proposed for the Bayesian inference of a Gaussian linear model with additive noise in presence of overabundant data. Three different optimal projections of the observations are proposed based on information theory: the projection that minimizes the Kullback–Leibler divergence between the posterior distributions of the original and the projected models, the one that minimizes the expected Kullback–Leibler divergence between the same distributions, and the one that maximizes the mutual information between the parameter of interest and the projected observations. The first two optimization problems are formulated as the determination of an optimal subspace and therefore the solution is computed using Riemannian optimization algorithms on the Grassmann manifold. Regarding the maximization of the mutual information, it is shown that there exists an optimal subspace that minimizes the entropy of the posterior distribution of the reduced model; a basis of the subspace can be computed as the solution to a generalized eigenvalue problem; an a priori error estimate on the mutual information is available for this particular solution; and that the dimensionality of the subspace to exactly conserve the mutual information between the input and the output of the models is less than the number of parameters to be inferred. Numerical applications to linear and nonlinear models are used to assess the efficiency of the proposed approaches, and to highlight their advantages compared to standard approaches based on the principal component analysis of the observations.

  1. A Numerical Study of Quantization-Based Integrators

    Directory of Open Access Journals (Sweden)

    Barros Fernando

    2014-01-01

    Full Text Available Adaptive step size solvers are nowadays considered fundamental to achieve efficient ODE integration. While, traditionally, ODE solvers have been designed based on discrete time machines, new approaches based on discrete event systems have been proposed. Quantization provides an efficient integration technique based on signal threshold crossing, leading to independent and modular solvers communicating through discrete events. These solvers can benefit from the large body of knowledge on discrete event simulation techniques, like parallelization, to obtain efficient numerical integration. In this paper we introduce new solvers based on quantization and adaptive sampling techniques. Preliminary numerical results comparing these solvers are presented.

  2. Efficient Allocation of Resources for Defense of Spatially Distributed Networks Using Agent-Based Simulation.

    Science.gov (United States)

    Kroshl, William M; Sarkani, Shahram; Mazzuchi, Thomas A

    2015-09-01

    This article presents ongoing research that focuses on efficient allocation of defense resources to minimize the damage inflicted on a spatially distributed physical network such as a pipeline, water system, or power distribution system from an attack by an active adversary, recognizing the fundamental difference between preparing for natural disasters such as hurricanes, earthquakes, or even accidental systems failures and the problem of allocating resources to defend against an opponent who is aware of, and anticipating, the defender's efforts to mitigate the threat. Our approach is to utilize a combination of integer programming and agent-based modeling to allocate the defensive resources. We conceptualize the problem as a Stackelberg "leader follower" game where the defender first places his assets to defend key areas of the network, and the attacker then seeks to inflict the maximum damage possible within the constraints of resources and network structure. The criticality of arcs in the network is estimated by a deterministic network interdiction formulation, which then informs an evolutionary agent-based simulation. The evolutionary agent-based simulation is used to determine the allocation of resources for attackers and defenders that results in evolutionary stable strategies, where actions by either side alone cannot increase its share of victories. We demonstrate these techniques on an example network, comparing the evolutionary agent-based results to a more traditional, probabilistic risk analysis (PRA) approach. Our results show that the agent-based approach results in a greater percentage of defender victories than does the PRA-based approach. © 2015 Society for Risk Analysis.

  3. The thermodynamic efficiency of ATP synthesis in oxidative phosphorylation.

    Science.gov (United States)

    Nath, Sunil

    2016-12-01

    As the chief energy source of eukaryotic cells, it is important to determine the thermodynamic efficiency of ATP synthesis in oxidative phosphorylation (OX PHOS). Previous estimates of the thermodynamic efficiency of this vital process have ranged from Lehninger's original back-of-the-envelope calculation of 38% to the often quoted value of 55-60% in current textbooks of biochemistry, to high values of 90% from recent information theoretic considerations, and reports of realizations of close to ideal 100% efficiencies by single molecule experiments. Hence this problem has been reinvestigated from first principles. The overall thermodynamic efficiency of ATP synthesis in the mitochondrial energy transduction OX PHOS process has been found to lie between 40 and 41% from four different approaches based on a) estimation using structural and biochemical data, b) fundamental nonequilibrium thermodynamic analysis, c) novel insights arising from Nath's torsional mechanism of energy transduction and ATP synthesis, and d) the overall balance of cellular energetics. The torsional mechanism also offers an explanation for the observation of a thermodynamic efficiency approaching 100% in some experiments. Applications of the unique, molecular machine mode of functioning of F 1 F O -ATP synthase involving direct inter-conversion of chemical and mechanical energies in the design and fabrication of novel, man-made mechanochemical devices have been envisaged, and some new ways to exorcise Maxwell's demon have been proposed. It is hoped that analysis of the fundamental problem of energy transduction in OX PHOS from a fresh perspective will catalyze new avenues of research in this interdisciplinary field. Copyright © 2016 Elsevier B.V. All rights reserved.

  4. Exchange Rates and Fundamentals.

    Science.gov (United States)

    Engel, Charles; West, Kenneth D.

    2005-01-01

    We show analytically that in a rational expectations present-value model, an asset price manifests near-random walk behavior if fundamentals are I (1) and the factor for discounting future fundamentals is near one. We argue that this result helps explain the well-known puzzle that fundamental variables such as relative money supplies, outputs,…

  5. Method of Monitoring Urban Area Deformation Based on Differential TomoSAR

    Directory of Open Access Journals (Sweden)

    WANG Aichun

    2016-12-01

    Full Text Available While the use of differential TomoSAR based on compressive sensing (CS makes it possible to solve the layover problem and reconstruct the deformation information of an observed urban area scene acquired by moderate-high resolution SAR satellite, the performance of the reconstruction decreases for a sparse and structural observed scene due to ignoring the structural characteristics of the observed scene. To deal with this issue, the method for differential SAR tomography based on Khatri-Rao subspace and block compressive sensing (KRS-BCS is proposed. The proposed method changes the reconstruction of the sparse and structural observed scene into a BCS problem under Khatri-Rao subspace, using the structure information of the observed scene and Khatri-Rao product property of the reconstructed observation matrix for differential TomoSAR, such that the KRS-BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model, and the performance of resolution capability and reconstruction estimation is compared and analyzed qualitatively and quantitatively by the theoretical analysis and the simulation experiments, all of the results show the propose KRS-BCS method practicably overcomes the problems of CS method, as well as, quite maintains the high resolution characteristics, effectively reduces the probability of false scattering target and greatly improves the reconstruction accurate of scattering point. Finally, the application is taking the urban area of the Mobara(in Chiba, Japan as the test area and using 34 ENVISAT-ASAR images, the accuracy is verifying with the reference deformations derived from first level point data and GPS tracking data, the results show the trend is consistent and the overall deviation is small between reconstruction deformations of the propose KRS-BCS method and the reference deformations, and the accuracy is high in the estimation of the urban area deformation.

  6. Feedback Gating Control for Network Based on Macroscopic Fundamental Diagram

    Directory of Open Access Journals (Sweden)

    YangBeibei Ji

    2016-01-01

    Full Text Available Empirical data from Yokohama, Japan, showed that a macroscopic fundamental diagram (MFD of urban traffic provides for different network regions a unimodal low-scatter relationship between network vehicle density and network space-mean flow. This provides new tools for network congestion control. Based on MFD, this paper proposed a feedback gating control policy which can be used to mitigate network congestion by adjusting signal timings of gating intersections. The objective of the feedback gating control model is to maximize the outflow and distribute the allowed inflows properly according to external demand and capacity of each gating intersection. An example network is used to test the performance of proposed feedback gating control model. Two types of background signalization types for the intersections within the test network, fixed-time and actuated control, are considered. The results of extensive simulation validate that the proposed feedback gating control model can get a Pareto improvement since the performance of both gating intersections and the whole network can be improved significantly especially under heavy demand situations. The inflows and outflows can be improved to a higher level, and the delay and queue length at all gating intersections are decreased dramatically.

  7. Energy informatics: Fundamentals and standardization

    Directory of Open Access Journals (Sweden)

    Biyao Huang

    2017-06-01

    Full Text Available Based on international standardization and power utility practices, this paper presents a preliminary and systematic study on the field of energy informatics and analyzes boundary expansion of information and energy system, and the convergence of energy system and ICT. A comprehensive introduction of the fundamentals and standardization of energy informatics is provided, and several key open issues are identified.

  8. Radiology fundamentals

    CERN Document Server

    Singh, Harjit

    2011-01-01

    ""Radiology Fundamentals"" is a concise introduction to the dynamic field of radiology for medical students, non-radiology house staff, physician assistants, nurse practitioners, radiology assistants, and other allied health professionals. The goal of the book is to provide readers with general examples and brief discussions of basic radiographic principles and to serve as a curriculum guide, supplementing a radiology education and providing a solid foundation for further learning. Introductory chapters provide readers with the fundamental scientific concepts underlying the medical use of imag

  9. Theoretical and experimental study of fundamental differences in the noise suppression of high-speed SOA-based all-optical switches

    DEFF Research Database (Denmark)

    Nielsen, Mads Lønstrup; Mørk, Jesper; Suzuki, R.

    2005-01-01

    We identify a fundamental difference between the ASE noise filtering properties of different all-optical SOA-based switch configurations, and divide the switches into two classes. An in-band ASE suppression ratio quantifying the difference is derived theoretically and the impact of the ASE...

  10. SAVE - energy efficiency in Germany 1990-2000. Report based on the ODYSSEE data base on energy efficiency indicators and the MURE data base on energy efficiency policy measures with the support from SAVE. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Eichhammer, W.; Schlomann, B.

    2002-03-01

    This reports presents an analysis of energy efficiency trends in Germany on the basis of energy efficiency indicators extracted from the ODYSSEE data base, maintained and updated in the framework of the SAVE programme. This analysis focuses on the period 1990-2000. The year 1990 could however not be considered for all indicators, because most of the economic and some energy consumption data for Germany are only available since 1991. The analysis also examines the policies and measures implemented in the field of energy efficiency, with a focus on the latest years until February 2002. All these measures are extracted from the MURE data base also updated within the SAVE programme. The report starts with a review on data collection and the recent trends in the general context of energy efficiency, i. e. economic and energy consumption development, energy and environmental policy and energy price trends (Chapter 2). Afterwards, the energy efficiency trends are described both at the level of the whole economy and at sectoral level (Chapter 3). In Chapter 4 the development in one sector - transport - is described more detailed. For the other sectors (industry, residential, tertiary) Annex 2 presents a selection of commented graphs that show the trends for the main indicators. An overview of the most important measures in the field of energy efficiency policy in the end-use sectors in Germany is given in Annex 1. A more detailed description of the most recent measures is presented in Annex 3. (orig.)

  11. Orthogonal frequency division multiple access fundamentals and applications

    CERN Document Server

    Jiang, Tao; Zhang, Yan

    2010-01-01

    Supported by the expert-level advice of pioneering researchers, Orthogonal Frequency Division Multiple Access Fundamentals and Applications provides a comprehensive and accessible introduction to the foundations and applications of one of the most promising access technologies for current and future wireless networks. It includes authoritative coverage of the history, fundamental principles, key techniques, and critical design issues of OFDM systems. Covering various techniques of effective resource management for OFDM/OFDMA-based wireless communication systems, this cutting-edge reference:Add

  12. How Accurate are Government Forecast of Economic Fundamentals?

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); Ph.H.B.F. Franses (Philip Hans); M.J. McAleer (Michael)

    2009-01-01

    textabstractA government’s ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an

  13. Opportunities for Fundamental University-Based Research in Energy and Resource Recovery

    Science.gov (United States)

    Zoback, M. D.; Hitzman, M.; Tester, J. W.

    2012-12-01

    In this talk we present, from a university perspective, a few examples of fundamental research needs related to improved energy and resource recovery. One example of such a research need is related to the fact that it is not widely recognized that meeting domestic and worldwide energy needs with renewables such as wind and solar will be materials intensive. If widely deployed, the elements required by renewable technologies will be needed in significant quantities and shortage of these "energy critical elements" could significantly inhibit the adoption of otherwise game changing energy technologies. It is imperative to better understand the geology, metallurgy, and mining engineering of critical mineral deposits if we are to sustainably develop these new technologies. Unfortunately, there is currently no consensus among federal and state agencies, the national and international mining industry, the public, and the U.S. academic community regarding the importance of economic geology in the context of securing sufficient energy critical elements to undertake large-scale renewable energy development. Another option for transitioning away from our current hydrocarbon-based energy system to non-carbon based sources, is geothermal energy - from both conventional hydrothermal resources and enhanced or engineered geothermal systems (EGS). Although geothermal energy is currently used for both electric and non-electric applications worldwide from conventional hydrothermal resources and in ground source heat pumps, most of the emphasis in the US has been generating electricity. To this end, there is a need for research, development and demonstration in five important areas - estimating the magnitude and distribution of recoverable geothermal resources, establishing requirements for extracting and utilizing energy from EGS reservoirs the including drilling, reservoir design and stimulation, exploring end use options for district heating, electricity generation and co

  14. Narrowband direction of arrival estimation for antenna arrays

    CERN Document Server

    Foutz, Jeffrey

    2008-01-01

    This book provides an introduction to narrowband array signal processing, classical and subspace-based direction of arrival (DOA) estimation with an extensive discussion on adaptive direction of arrival algorithms. The book begins with a presentation of the basic theory, equations, and data models of narrowband arrays. It then discusses basic beamforming methods and describes how they relate to DOA estimation. Several of the most common classical and subspace-based direction of arrival methods are discussed. The book concludes with an introduction to subspace tracking and shows how subspace tr

  15. RAM-efficient external memory sorting

    DEFF Research Database (Denmark)

    Arge, Lars; Thorup, Mikkel

    2013-01-01

    In recent years a large number of problems have been considered in external memory models of computation, where the complexity measure is the number of blocks of data that are moved between slow external memory and fast internal memory (also called I/Os). In practice, however, internal memory time...... often dominates the total running time once I/O-efficiency has been obtained. In this paper we study algorithms for fundamental problems that are simultaneously I/O-efficient and internal memory efficient in the RAM model of computation....

  16. Communication: Fitting potential energy surfaces with fundamental invariant neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shao, Kejie; Chen, Jun; Zhao, Zhiqiang; Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn [State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People’s Republic of China and University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China. (China)

    2016-08-21

    A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energy surfaces for OH{sub 3} and CH{sub 4} were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.

  17. A projected preconditioned conjugate gradient algorithm for computing many extreme eigenpairs of a Hermitian matrix

    International Nuclear Information System (INIS)

    Vecharynski, Eugene; Yang, Chao; Pask, John E.

    2015-01-01

    We present an iterative algorithm for computing an invariant subspace associated with the algebraically smallest eigenvalues of a large sparse or structured Hermitian matrix A. We are interested in the case in which the dimension of the invariant subspace is large (e.g., over several hundreds or thousands) even though it may still be small relative to the dimension of A. These problems arise from, for example, density functional theory (DFT) based electronic structure calculations for complex materials. The key feature of our algorithm is that it performs fewer Rayleigh–Ritz calculations compared to existing algorithms such as the locally optimal block preconditioned conjugate gradient or the Davidson algorithm. It is a block algorithm, and hence can take advantage of efficient BLAS3 operations and be implemented with multiple levels of concurrency. We discuss a number of practical issues that must be addressed in order to implement the algorithm efficiently on a high performance computer

  18. Journal Afrika Statistika ISSN 0852-0305 An efficient locally ...

    African Journals Online (AJOL)

    matrix with rank r (rsubspace of RK spanned by the columns of ...... (1) Simple algebra calculus allow to write the symmetric matrix Γn(ν) as ... Statement (1) follows then from an application of the ergodic theorem combined ...

  19. Arguing against fundamentality

    Science.gov (United States)

    McKenzie, Kerry

    This paper aims to open up discussion on the relationship between fundamentality and naturalism, and in particular on the question of whether fundamentality may be denied on naturalistic grounds. A historico-inductive argument for an anti-fundamentalist conclusion, prominent within the contemporary metaphysical literature, is examined; finding it wanting, an alternative 'internal' strategy is proposed. By means of an example from the history of modern physics - namely S-matrix theory - it is demonstrated that (1) this strategy can generate similar (though not identical) anti-fundamentalist conclusions on more defensible naturalistic grounds, and (2) that fundamentality questions can be empirical questions. Some implications and limitations of the proposed approach are discussed.

  20. Efficiency bounds for nonequilibrium heat engines

    International Nuclear Information System (INIS)

    Mehta, Pankaj; Polkovnikov, Anatoli

    2013-01-01

    We analyze the efficiency of thermal engines (either quantum or classical) working with a single heat reservoir like an atmosphere. The engine first gets an energy intake, which can be done in an arbitrary nonequilibrium way e.g. combustion of fuel. Then the engine performs the work and returns to the initial state. We distinguish two general classes of engines where the working body first equilibrates within itself and then performs the work (ergodic engine) or when it performs the work before equilibrating (non-ergodic engine). We show that in both cases the second law of thermodynamics limits their efficiency. For ergodic engines we find a rigorous upper bound for the efficiency, which is strictly smaller than the equivalent Carnot efficiency. I.e. the Carnot efficiency can be never achieved in single reservoir heat engines. For non-ergodic engines the efficiency can be higher and can exceed the equilibrium Carnot bound. By extending the fundamental thermodynamic relation to nonequilibrium processes, we find a rigorous thermodynamic bound for the efficiency of both ergodic and non-ergodic engines and show that it is given by the relative entropy of the nonequilibrium and initial equilibrium distributions. These results suggest a new general strategy for designing more efficient engines. We illustrate our ideas by using simple examples. -- Highlights: ► Derived efficiency bounds for heat engines working with a single reservoir. ► Analyzed both ergodic and non-ergodic engines. ► Showed that non-ergodic engines can be more efficient. ► Extended fundamental thermodynamic relation to arbitrary nonequilibrium processes