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...
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....
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...
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...
Unsupervised spike sorting based on discriminative subspace learning.
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.
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
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...
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...
Supervised orthogonal discriminant subspace projects learning for face recognition.
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.
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.
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.
Subspace-based interference removal methods for a multichannel biomagnetic sensor array
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.
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...
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.
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.
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.
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.
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
Subspace-based analysis of the ERT inverse problem
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.
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....
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
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
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....
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....
The influence of different PAST-based subspace trackers on DaPT parameter estimation
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.
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...
Geometric mean for subspace selection.
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.
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.
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.
Estimation of direction of arrival of a moving target using subspace based approaches
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.
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....
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,
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....
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.
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
Subspace based adaptive denoising of surface EMG from neurological injury patients
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.
N-screen aware multicriteria hybrid recommender system using weight based subspace clustering.
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.
A new subspace based approach to iterative learning control
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,
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.
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...
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
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.
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.
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.
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.
Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon
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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.
Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF
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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.
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
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.
A unified classifier for robust face recognition based on combining multiple subspace algorithms
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.
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.
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 ...
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).
Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval.
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.
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.
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)
Imaging of heart acoustic based on the sub-space methods using a microphone array.
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.
Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation
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.
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.
Visual tracking based on the sparse representation of the PCA subspace
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.
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.
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)
Consistency Analysis of Nearest Subspace Classifier
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 ...
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
Towards automatic music transcription: note extraction based on independent subspace analysis
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.
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.
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
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.
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
High resolution through-the-wall radar image based on beamspace eigenstructure subspace methods
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.
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...
Subspace learning from image gradient orientations
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
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.
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.
Shape analysis with subspace symmetries
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).
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.
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
Random matrix improved subspace clustering
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.
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.
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.
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
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.
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
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.
Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin
2015-10-01
The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.
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
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.
Seismic noise attenuation using an online subspace tracking algorithm
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
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.
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
Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution
Satish Gajawada; Durga Toshniwal
2012-01-01
Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...
An alternative subspace approach to EEG dipole source localization
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.
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
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.
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).
Random matrix improved subspace clustering
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
BUILT-UP AREA DETECTION BASED ON SUBSPACE PROJECTIONS USING POLARIMETRIC SAR DATA
Directory of Open Access Journals (Sweden)
R. Bordbari
2017-09-01
Full Text Available The task of detecting and identifying objects remotely has long been an area of intense interest and active research. Active sensing of objects with radio waves is a whole new domain of target detection which is made available by radar remote sensors. Land cover/use information extraction is one of the most important applications of radar remote sensing, especially in urban areas. In this paper, we take a new look at the built-up area extraction problem in polarimetric SAR (PolSAR data and assume canonical scattering mechanisms as our signal sources which combination of them with appropriate weight fractions formed a scattering vector of each pixel. The set of the scattering mechanisms is divided into two groups: the scattering mechanism of built-up area, and non-objected scattering mechanisms. Then, we describe a technique which simultaneously annihilates the effect of non-objected scattering mechanisms, and detects the presence of a scattering mechanism of interest. The experimental results on several quad-polarimetric datasets show the significant agreement with expected results, while saving computational complexity.
Seismic noise attenuation using an online subspace tracking algorithm
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.
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
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
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.
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
Adiabatic evolution of decoherence-free subspaces and its shortcuts
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.
Subspace Arrangement Codes and Cryptosystems
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
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.
Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization
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.
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.
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.
Krajcik, Joe
2015-01-01
Project-based science is an exciting way to teach science that aligns with the "Next Generation Science Standards" ("NGSS"). By focusing on core ideas along with practices and crosscutting concepts, classrooms become learning environments where teachers and students engage in science by designing and carrying out…
A General Algorithm for Reusing Krylov Subspace Information. I. Unsteady Navier-Stokes
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.
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 ...
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...
Source localization using recursively applied and projected (RAP) MUSIC
Energy Technology Data Exchange (ETDEWEB)
Mosher, J.C. [Los Alamos National Lab., NM (United States); Leahy, R.M. [Univ. of Southern California, Los Angeles, CA (United States). Signal and Image Processing Inst.
1998-03-01
A new method for source localization is described that is based on a modification of the well known multiple signal classification (MUSIC) algorithm. In classical MUSIC, the array manifold vector is projected onto an estimate of the signal subspace, but errors in the estimate can make location of multiple sources difficult. Recursively applied and projected (RAP) MUSIC uses each successively located source to form an intermediate array gain matrix, and projects both the array manifold and the signal subspace estimate into its orthogonal complement. The MUSIC projection is then performed in this reduced subspace. Using the metric of principal angles, the authors describe a general form of the RAP-MUSIC algorithm for the case of diversely polarized sources. Through a uniform linear array simulation, the authors demonstrate the improved Monte Carlo performance of RAP-MUSIC relative to MUSIC and two other sequential subspace methods, S and IES-MUSIC.
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.
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.
Code subspaces for LLM geometries
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.
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....
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
Improved Stochastic Subspace System Identification for Structural Health Monitoring
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.
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.
Two-level method for unsteady Navier-Stokes equations based on a new projection
International Nuclear Information System (INIS)
Hou Yanren; Li Kaitai
2004-12-01
A two-level algorithm for the two dimensional unsteady Navier-Stokes equations based on a new projection is proposed and investigated. The approximate solution is solved as a sum of a large eddy component and a small eddy component, which are in the sense of the new projection, constructed in this paper. These two terms advance in time explicitly. Actually, the new algorithm proposed here can be regarded as a sort of postprocessing algorithm for the standard Galerkin method (SGM). The large eddy part is solved by SGM in the usual L 2 -based large eddy subspace while the small eddy part (the correction part) is obtained in its complement subspace in the sense of the new projection. The stability analysis indicates the improvement of the stability comparing with SGM of the same scale, and the L 2 -error estimate shows that the scheme can improve the accuracy of SGM approximation for half order. We also propose a numerical implementation based on Lagrange multiplier for this two-level algorithm. (author)
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.
Active Subspaces of Airfoil Shape Parameterizations
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.
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.
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
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.
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.
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...
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.
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:
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...
Subarray Processing for Projection-based RFI Mitigation in Radio Astronomical Interferometers
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.
Conjunctive patches subspace learning with side information for collaborative image retrieval.
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.
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.
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)
View subspaces for indexing and retrieval of 3D models
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.
A subspace approach to high-resolution spectroscopic imaging.
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.
Face recognition based on two-dimensional discriminant sparse preserving projection
Zhang, Dawei; Zhu, Shanan
2018-04-01
In this paper, a supervised dimensionality reduction algorithm named two-dimensional discriminant sparse preserving projection (2DDSPP) is proposed for face recognition. In order to accurately model manifold structure of data, 2DDSPP constructs within-class affinity graph and between-class affinity graph by the constrained least squares (LS) and l1 norm minimization problem, respectively. Based on directly operating on image matrix, 2DDSPP integrates graph embedding (GE) with Fisher criterion. The obtained projection subspace preserves within-class neighborhood geometry structure of samples, while keeping away samples from different classes. The experimental results on the PIE and AR face databases show that 2DDSPP can achieve better recognition performance.
Structured Kernel Subspace Learning for Autonomous Robot Navigation.
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.
Subspace methods for identification of human ankle joint stiffness.
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.
Enhancing Low-Rank Subspace Clustering by Manifold Regularization.
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.
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.
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.
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.
Indoor Subspacing to Implement Indoorgml for Indoor Navigation
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.
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 ...
Parand, K.; Nikarya, M.
2017-11-01
In this paper a novel method will be introduced to solve a nonlinear partial differential equation (PDE). In the proposed method, we use the spectral collocation method based on Bessel functions of the first kind and the Jacobian free Newton-generalized minimum residual (JFNGMRes) method with adaptive preconditioner. In this work a nonlinear PDE has been converted to a nonlinear system of algebraic equations using the collocation method based on Bessel functions without any linearization, discretization or getting the help of any other methods. Finally, by using JFNGMRes, the solution of the nonlinear algebraic system is achieved. To illustrate the reliability and efficiency of the proposed method, we solve some examples of the famous Fisher equation. We compare our results with other methods.
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.
Projection-based curve clustering
International Nuclear Information System (INIS)
Auder, Benjamin; Fischer, Aurelie
2012-01-01
This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat a l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU time-consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, the CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centres found by the clustering method based on projections, compared with the 'true' ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem. (authors)
A block Krylov subspace time-exact solution method for linear ordinary differential equation systems
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
Managing projects a team-based approach
Brown, Karen A
2010-01-01
Students today are likely to be assigned to project teams or to be project managers almost immediately in their first job. Managing Projects: A Team-Based Approach was written for a wide range of stakeholders, including project managers, project team members, support personnel, functional mangers who provide resources for projects, project customers (and customer representatives), project sponsors, project subcontractors, and anyone who plays a role in the project delivery process. The need for project management is on the rise as product life cycles compress, demand for IT systems increases, and business takes on an increasingly global character. This book adds to the project management knowledge base in a way that fills an unmet need—it shows how teams can apply many of the standard project management tools, as well as several tools that are relatively new to the field. Managing Projects: A Team-Based Approach offers the academic rigor found in most textbooks along with the practical attributes often foun...
Orthogonality Measurement for Homogenous Projects-Bases
Ivan, Ion; Sandu, Andrei; Popa, Marius
2009-01-01
The homogenous projects-base concept is defined. Next, the necessary steps to create a homogenous projects-base are presented. A metric system is built, which then will be used for analyzing projects. The indicators which are meaningful for analyzing a homogenous projects-base are selected. The given hypothesis is experimentally verified. The…
Solving Conic Systems via Projection and Rescaling
Pena, Javier; Soheili, Negar
2015-01-01
We propose a simple projection and rescaling algorithm to solve the feasibility problem \\[ \\text{ find } x \\in L \\cap \\Omega, \\] where $L$ and $\\Omega$ are respectively a linear subspace and the interior of a symmetric cone in a finite-dimensional vector space $V$. This projection and rescaling algorithm is inspired by previous work on rescaled versions of the perceptron algorithm and by Chubanov's projection-based method for linear feasibility problems. As in these predecessors, each main it...
Central subspace dimensionality reduction using covariance operators.
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.
Subspace methods for pattern recognition in intelligent environment
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.
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
Sparse subspace clustering for data with missing entries and high-rank matrix completion.
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.
Web Based Project Management System
Aadamsoo, Anne-Mai
2010-01-01
To increase an efficiency of a product, nowadays many web development companies are using different project management systems. A company may run a number of projects at a time, and requires input from a number of individuals, or teams for a multi level development plan, whereby a good project management system is needed. Project management systems represent a rapidly growing technology in IT industry. As the number of users, who utilize project management applications continues to grow, w...
Linear Subspace Ranking Hashing for Cross-Modal Retrieval.
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.
Robust adaptive subspace detection in impulsive noise
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.
Robust adaptive subspace detection in impulsive noise
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.
Adapting Project Management Practices to Research-Based Projects
Bahr, P.; Baker, T.; Corbin, B.; Keith, L.; Loerch, L.; Mullenax, C.; Myers, R.; Rhodes, B.; Skytland, N.
2007-01-01
From dealing with the inherent uncertainties in outcomes of scientific research to the lack of applicability of current NASA Procedural Requirements guidance documentation, research-based projects present challenges that require unique application of classical project management techniques. If additionally challenged by the creation of a new program transitioning from basic to applied research in a technical environment often unfamiliar with the cost and schedule constraints addressed by project management practices, such projects can find themselves struggling throughout their life cycles. Finally, supplying deliverables to a prime vehicle customer, also in the formative stage, adds further complexity to the development and management of research-based projects. The Biomedical Research and Countermeasures Projects Branch at NASA Johnson Space Center encompasses several diverse applied research-based or research-enabling projects within the newly-formed Human Research Program. This presentation will provide a brief overview of the organizational structure and environment in which these projects operate and how the projects coordinate to address and manage technical requirements. We will identify several of the challenges (cost, technical, schedule, and personnel) encountered by projects across the Branch, present case reports of actions taken and techniques implemented to deal with these challenges, and then close the session with an open forum discussion of remaining challenges and potential mitigations.
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)
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)
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.
Projection methods for line radiative transfer in spherical media.
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).
Web-based Project Reporting System
US Agency for International Development — Web-PRS is a web-based system that captures financial information and project status information that is sortable by geographical location, pillar, project type and...
Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization
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
Optimal projection of observations in a Bayesian setting
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.
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
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.
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...
Detecting anomalies in crowded scenes via locality-constrained affine subspace coding
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.
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.
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.
Enhanced oil recovery projects data base
Energy Technology Data Exchange (ETDEWEB)
Pautz, J.F.; Sellers, C.A.; Nautiyal, C.; Allison, E.
1992-04-01
A comprehensive enhanced oil recovery (EOR) project data base is maintained and updated at the Bartlesville Project Office of the Department of Energy. This data base provides an information resource that is used to analyze the advancement and application of EOR technology. The data base has extensive information on 1,388 EOR projects in 569 different oil fields from 1949 until the present, and over 90% of that information is contained in tables and graphs of this report. The projects are presented by EOR process, and an index by location is provided.
Base-age invariance and inventory projections
C. J. Cieszewski; R. L. Bailey; B. E. Borders; G. H. Brister; B. D. Shiver
2000-01-01
One of the most important functions of forest inventory is to facilitate management decisions towards forest sustainability based on inventory projections into the future. Therefore, most forest inventories are used for predicting future states of the forests, in modern forestry the most common methods used in inventory projections are based on implicit functions...
Project-Based Learning in Scottish Prisons
Sams, Kirsten
2014-01-01
The article describes the development of a project-based approach to learning in seven Scottish prisons. It argues that the project-based approach is ideally suited to prison education due to its flexibility and ability to enrich the relatively narrow prison curriculum and create meaningful links with wider society, reducing the isolation of…
Reverse time migration by Krylov subspace reduced order modeling
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.
A Variational Approach to Video Registration with Subspace Constraints.
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.
Organizational Diagnosis in Project-Based Companies
Directory of Open Access Journals (Sweden)
Behrouz Zarei
2014-05-01
Full Text Available The purpose of this article is to develop a new method for corporate diagnosis (CD. To this end, a method is developed for the diagnosis process of project-based companies. The article presents a case study in a large company where data have been collected through focus groups. Project delay, high project cost, and low profitability are examples of project deficiency in project-based companies. Such issues have made managers pay special attention to find effective solutions to improve them. Prominent factors are inappropriate strategy, structure, system, human resource management, and PMBOK(Project Management Body of Knowledge processes. Thus, CD and analysis is an important task in improvement of corporate performance. The CD model that is developed in this article could be used for project-based companies. The proposed method can be used for CD in any project-based company. This article provides an emphatic application of CD as a prerequisite for restructuring in project-based companies.
Application of Earthquake Subspace Detectors at Kilauea and Mauna Loa Volcanoes, Hawai`i
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.
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.
Independence and totalness of subspaces in phase space methods
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.
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.
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.)
Process-based software project management
Goodman, F Alan
2006-01-01
Not connecting software project management (SPM) to actual, real-world development processes can lead to a complete divorcing of SPM to software engineering that can undermine any successful software project. By explaining how a layered process architectural model improves operational efficiency, Process-Based Software Project Management outlines a new method that is more effective than the traditional method when dealing with SPM. With a clear and easy-to-read approach, the book discusses the benefits of an integrated project management-process management connection. The described tight coup
Gene selection for microarray data classification via subspace learning and manifold regularization.
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.
Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection
Al, Mert; Wan, Shibiao; Kung, Sun-Yuan
2017-01-01
With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life. Nevertheless, it has also opened new venues for breaching users' privacy. Hence it is highly required to develop techniques that enable data owners to privatize their data while keeping it useful for intended applications. Existing methods, however, do not offer enough flexibility for controlling the utility-privacy trade-off and may incur unfavorable results when...
Discriminative Projection Selection Based Face Image Hashing
Karabat, Cagatay; Erdogan, Hakan
Face image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussian mixture model at the quantization step. Our simulation results on three different databases demonstrate that the proposed method has superior performance in comparison to previously proposed random projection based methods.
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
Visualizing Practices in Project-based Design
DEFF Research Database (Denmark)
Whyte, Jennifer; Tryggestad, Kjell; Comi, Alice
2016-01-01
Project-based design involves a variety of visual representations, which are evolved to make decisions and accomplish project objectives. Yet, such mediated and distributed ways of working are difficult to capture through ethnographies that examine situated design. A novel approach is developed t...... representations enabled participants in project-based design to develop and share understanding. The complexity of projects and their distributed and mediated nature makes this approach timely and important in addressing new research questions and practical challenges.......Project-based design involves a variety of visual representations, which are evolved to make decisions and accomplish project objectives. Yet, such mediated and distributed ways of working are difficult to capture through ethnographies that examine situated design. A novel approach is developed...... of situated design. This allows the researcher to be nimble, tracing connections across complex engineering projects; reconstructing practices through their visual representations; and observing their effects. Second, it articulates how, in these empirical cases, interaction with a cascade of visual...
Recursive subspace identification for in flight modal analysis of airplanes
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.
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...
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...
Subspace identification of distributed clusters of homogeneous systems
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
Projective synchronization based on suitable separation
International Nuclear Information System (INIS)
Li Guohui; Xiong Chuan; Sun Xiaonan
2007-01-01
A new approach for constructing a projective-synchronized chaotic slave system is proposed in this paper. This method is based on suitable separation by decomposing the system as the linear part and the nonlinear one. From matrix measure theory, some simple but efficient criteria are derived for projective synchronization of chaotic system. Numerical simulations for the Lorenz system show that this control method works very well
Integrated Data Base: Status and waste projections
International Nuclear Information System (INIS)
Klein, J.A.
1990-01-01
The Integrated Data Base (IDB) is the official US Department of Energy (DOE) data base for spent fuel and radioactive waste inventories and projections. DOE low-level waste (LLW) is just one of the many waste types that are documented with the IDB. Summary-level tables and figures are presented illustrating historical and projected volume changes of DOE LLW. This information is readily available through the annual IDB publication. Other presentation formats are also available to the DOE community through a request to the IDB Program. 4 refs., 6 figs., 5 tabs
Dominant Taylor Spectrum and Invariant Subspaces
Czech Academy of Sciences Publication Activity Database
Ambrozie, Calin-Grigore; Müller, Vladimír
2009-01-01
Roč. 61, č. 1 (2009), s. 101-111 ISSN 0379-4024 R&D Projects: GA ČR(CZ) GA201/06/0128 Institutional research plan: CEZ:AV0Z10190503 Keywords : Taylor spectrum * Scott-Brown technique * dominant spectrum Subject RIV: BA - General Mathematics Impact factor: 0.580, year: 2009
Lights, Camera, Project-Based Learning!
Cox, Dannon G.; Meaney, Karen S.
2018-01-01
A physical education instructor incorporates a teaching method known as project-based learning (PBL) in his physical education curriculum. Utilizing video-production equipment to imitate the production of a televisions show, sixth-grade students attending a charter school invited college students to share their stories about physical activity and…
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
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
Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery
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
Index Formulae for Subspaces of Kreĭn Spaces
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
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.
Positive projections of symmetric matrices and Jordan algebras
DEFF Research Database (Denmark)
Fuglede, Bent; Jensen, Søren Tolver
2013-01-01
An elementary proof is given that the projection from the space of all symmetric p×p matrices onto a linear subspace is positive if and only if the subspace is a Jordan algebra. This solves a problem in a statistical model.......An elementary proof is given that the projection from the space of all symmetric p×p matrices onto a linear subspace is positive if and only if the subspace is a Jordan algebra. This solves a problem in a statistical model....
Comparison Study of Subspace Identification Methods Applied to Flexible Structures
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.
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.
The Nordic Chernobyl data base project
International Nuclear Information System (INIS)
Lippert, J.
1991-01-01
Chernobyl Data Base has been established with the purpose of collecting the large amount of data resulting from measurements made in several laboratories in the period following the Chernobyl accident. All information is stored in the C data base handling system developed especially for this kind of environmental data. The C base data handling system allows for easy input from a variety of sources and easy output to other computer programs for further data treatment. This paper describes the structures, codes and methods used and the present status of the project. (au)
Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling.
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.
Agent-Based Simulations for Project Management
White, J. Chris; Sholtes, Robert M.
2011-01-01
Currently, the most common approach used in project planning tools is the Critical Path Method (CPM). While this method was a great improvement over the basic Gantt chart technique being used at the time, it now suffers from three primary flaws: (1) task duration is an input, (2) productivity impacts are not considered , and (3) management corrective actions are not included. Today, computers have exceptional computational power to handle complex simulations of task e)(eculion and project management activities (e.g ., dynamically changing the number of resources assigned to a task when it is behind schedule). Through research under a Department of Defense contract, the author and the ViaSim team have developed a project simulation tool that enables more realistic cost and schedule estimates by using a resource-based model that literally turns the current duration-based CPM approach "on its head." The approach represents a fundamental paradigm shift in estimating projects, managing schedules, and reducing risk through innovative predictive techniques.
Knowledge Governance in Project-Based Organizations
DEFF Research Database (Denmark)
Pemsel, Sofia; Wiewiora, Anna; Müller, Ralf
This paper conceptualizes knowledge governance (KG) in project-based organizations (PBOs) and its methodological approaches for empirical investigation. Three key contributions towards a multi-faceted view of KG and an understanding of KG in PBOs are advanced. These contributions include a defini......This paper conceptualizes knowledge governance (KG) in project-based organizations (PBOs) and its methodological approaches for empirical investigation. Three key contributions towards a multi-faceted view of KG and an understanding of KG in PBOs are advanced. These contributions include...... a definition of KG in PBOs, a conceptual framework to investigate KG and a methodological framework for empirical inquiry into KG in PBO settings. Our definition highlights the contingent nature of KG processes in relation to their organizational context. The conceptual framework addresses macro- and micro...
Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling.
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.
Satellite based Ocean Forecasting, the SOFT project
Stemmann, L.; Tintoré, J.; Moneris, S.
2003-04-01
The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.
Project based learning for reactor engineering education
International Nuclear Information System (INIS)
Narabayashi, Tadashi; Tsuji, Masashi; Shimazu, Yoichiro
2009-01-01
Trial in education of nuclear engineering in Hokkaido University has proved to be quite attractive for students. It is an education system called Project Based Learning (PBL), which is not based on education by lecture only but based mostly on practice of students in the classroom. The system was adopted four years ago. In the actual class, we separated the student into several groups of the size about 6 students. In the beginning of each class room time, a brief explanations of the related theory or technical bases. Then the students discuss in their own group how to precede their design calculations and do the required calculation and evaluation. The target reactor type of each group was selected by the group members for themselves at the beginning of the semester as the first step of the project. The reactor types range from a small in house type to that for a nuclear ship. At the end of the semester, each group presents the final design. The presentation experience gives students a kind of fresh sensation. Nowadays the evaluation results of the subject by the students rank in the highest in the faculty of engineering. Based on the considerations above, we designed the framework of our PBL for reactor engineering. In this paper, we will present some lessons learned in this PBL education system from the educational points of view. The PBL education program is supported by IAE/METI in Japan for Nuclear Engineering Education. (author)
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
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
[Orthogonal Vector Projection Algorithm for Spectral Unmixing].
Song, Mei-ping; Xu, Xing-wei; Chang, Chein-I; An, Ju-bai; Yao, Li
2015-12-01
Spectrum unmixing is an important part of hyperspectral technologies, which is essential for material quantity analysis in hyperspectral imagery. Most linear unmixing algorithms require computations of matrix multiplication and matrix inversion or matrix determination. These are difficult for programming, especially hard for realization on hardware. At the same time, the computation costs of the algorithms increase significantly as the number of endmembers grows. Here, based on the traditional algorithm Orthogonal Subspace Projection, a new method called. Orthogonal Vector Projection is prompted using orthogonal principle. It simplifies this process by avoiding matrix multiplication and inversion. It firstly computes the final orthogonal vector via Gram-Schmidt process for each endmember spectrum. And then, these orthogonal vectors are used as projection vector for the pixel signature. The unconstrained abundance can be obtained directly by projecting the signature to the projection vectors, and computing the ratio of projected vector length and orthogonal vector length. Compared to the Orthogonal Subspace Projection and Least Squares Error algorithms, this method does not need matrix inversion, which is much computation costing and hard to implement on hardware. It just completes the orthogonalization process by repeated vector operations, easy for application on both parallel computation and hardware. The reasonability of the algorithm is proved by its relationship with Orthogonal Sub-space Projection and Least Squares Error algorithms. And its computational complexity is also compared with the other two algorithms', which is the lowest one. At last, the experimental results on synthetic image and real image are also provided, giving another evidence for effectiveness of the method.
On the selection of user-defined parameters in data-driven stochastic subspace identification
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.
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.
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.
Subspace Analysis of Indoor UWB Channels
Directory of Open Access Journals (Sweden)
Rachid Saadane
2005-03-01
Full Text Available This work aims at characterizing the second-order statistics of indoor ultra-wideband (UWB channels using channel sounding techniques. We present measurement results for different scenarios conducted in a laboratory setting at Institut EurÃƒÂ©com. These are based on an eigendecomposition of the channel autocovariance matrix, which allows for the analysis of the growth in the number of significant degrees of freedom of the channel process as a function of the signaling bandwidth as well as the statistical correlation between different propagation paths. We show empirical eigenvalue distributions as a function of the signal bandwidth for both line-of-sight and non-line-of-sight situations. Furthermore, we give examples where paths from different propagation clusters (possibly arising from reflection or diffraction show strong statistical dependence.
Orthogonally Based Digital Content Management Applicable to Projects-bases
Directory of Open Access Journals (Sweden)
Daniel MILODIN
2009-01-01
Full Text Available There is defined the concept of digital content. The requirements of an efficient management of the digital content are established. There are listed the quality characteristics of digital content. Orthogonality indicators of digital content are built up. They are meant to measure the image, the sound as well as the text orthogonality as well. Projects-base concept is introduced. There is presented the model of structuring the content in order to maximize orthogonality via a convergent iterative process. The model is instantiated for the digital content of a projects-base. It is introduced the application used to test the model. The paper ends with conclusions.
Process-based project proposal risk management
Directory of Open Access Journals (Sweden)
Alok Kumar
2016-12-01
Full Text Available We all are aware of the organizational omnipresence. Projects within the organizations are ubiquitous too. Projects achieve their goals successfully if they are planned, scheduled, controlled and implemented well. The project lifecycle of initiating, planning, scheduling, controlling and implementing are very well-planned by project managers and the organizations. Successful projects have well-developed risk management plans to deal with situations impacting projects. Like any other organisation, a university does try to access funds for different purposes too. For such organisations, running a project is not the issue, rather getting a project proposal approved to fund a project is the key. Project proposal processing is done by the nodal office in every organisation. Usually, these nodal offices help in administration and submission of a project proposal for accessing funds. Seldom are these nodal project offices within the organizations facilitate a project proposal approval by proactively reaching out to the project managers. And as project managers prepare project proposals, little or no attention is made to prepare a project proposal risk plan so as to maximise project acquisition. Risk plans are submitted while preparing proposals but these risk plans cater to a requirement to address actual projects upon approval. Hence, a risk management plan for project proposal is either missing or very little effort is made to treat the risks inherent in project acquisition. This paper is an integral attempt to highlight the importance of risk treatment for project proposal stage as an extremely important step to preparing the risk management plan made for projects corresponding to their lifecycle phases. Several tools and techniques have been proposed in the paper to help and guide either the project owner (proposer or the main organisational unit responsible for project management. Development of tools and techniques to further enhance project
Application of the Theory of Constraints in Project Based Structures
Martynas Sarapinas; Vytautas Pranas Sūdžius
2011-01-01
The article deals with the application of the Theory of Constraints (TOC) in project management. This article involves a short introduction to TOC as a project management method and deep analysis of project management specialties using the TOC: TOC based project planning, timetable management, tasks synchronization, project control and “relay runner work ethic”. Moreover, the article describes traditional and TOC based project management theories in their comparison, and emphasize the main be...
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
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....
Directory of Open Access Journals (Sweden)
Halyna V. Lutsenko
2015-02-01
Full Text Available The peculiarities of modern world experience of implementation of project-based learning in engineering education have been considered. The potential role and place of projects in learning activity have been analyzed. The methodology of organization of project-based activity of engineering students when studying the project management methodology and computer systems of project management has been proposed. The requirements to documentation and actual results of students' projects have been described in detail. The requirements to computer-aided systems of project management developed by using Microsoft Project in the scope of diary scheduling and resources planning have been formulated.
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.
Beamforming using subspace estimation from a diagonally averaged sample covariance.
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.
Alligator Rivers Analogue project. Geochemical Data Bases
International Nuclear Information System (INIS)
Bennett, D.G.; Read, D.
1992-01-01
The Koongarra uranium deposit in the Northern Territory of Australia is being studied to evaluate the processes involved in the geochemical alteration of the ore body and the formation of the uranium dispersion fan. A broad range of research is being undertaken into the geochemistry and hydrology of the site with the aim of understanding the transport of radionuclides through the system. During the project a range of geochemical and hydrogeochemical models have been developed to account for measured data from the site and with which to predict site evolution. The majority of these models are based on the premise of thermodynamic chemical equilibrium and employ fundamental thermodynamic data to characterise the chemistry of the system. From the differences which exist between the thermodynamic data bases (Appendices I and II) it is possible to gain a view of the level of uncertainty associated with thermodynamic data in each set of calculations. This report gives a brief introduction to the geochemical processes underlying the models, and details the equations used to quantify the more common of these processes (e.g. aqueous speciation and mineral solubility). A description is given of the computer codes (EQ3/6, PHREEQE, MINTEQ) most commonly used during the project for geochemical modelling. Their key features are highlighted and comparisons made. It is concluded that the degree of uncertainty in geochemical modelling studies arising as a result of using one code rather than another is relatively insignificant when compared to that related to differences in the underlying data bases. 73 refs., 3 figs
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…
Scaffolding Project-Based Learning with the Project Management Body of Knowledge (PMBOK[R])
van Rooij, Shahron Williams
2009-01-01
This paper reports the results of a study of the extent to which processes and procedures from the discipline of project management can scaffold online project-based learning in a graduate-level instructional technology course, by facilitating intra-team interaction, enhancing project outcomes and promoting a positive project team experience. With…
PORTFOLIO COMMUNICATION BASE OF PROJECT ORIENTED ORGANIZATION
Directory of Open Access Journals (Sweden)
Тетяна Віталіївна РОМАНІВ
2015-06-01
Full Text Available It is proposed a tool for overcoming communication barriers that arise between all stakeholders in the implementation of complex projects where the company understands the limits and properties of own resources as well as the construction of communication noise to a minimum, taking into account the plans, performance indicators and received projections in fact the project.
Kanter, David E.
2010-01-01
Project-based science curricula can improve students' usable or meaningful understanding of the science content underlying a project. However, such curricula designed around "performances" wherein students design or make something do not always do this. We researched ways to design performance project-based science curricula (pPBSc) to better…
Jaime, Arturo; Blanco, José Miguel; Domínguez, César; Sánchez, Ana; Heras, Jónathan; Usandizaga, Imanol
2016-01-01
Different learning methods such as project-based learning, spiral learning and peer assessment have been implemented in science disciplines with different outcomes. This paper presents a proposal for a project management course in the context of a computer science degree. Our proposal combines three well-known methods: project-based learning,…
Arantes do Amaral, Joao Alberto; Gonçalves, Paulo; Hess, Aurélio
2015-01-01
This article describes the project-based learning environment created to support project management graduate courses. The paper will focus on the learning context and procedures followed for 13 years, in 47 project-based learning MBA courses, involving approximately 1,400 students and 34 community partners.
Project-based knowledge in organizing open innovation
Comacchio, Anna; Pizzi, Claudio
2014-01-01
Enriching understanding of the current theoretical debate on project-based open innovation, ‘Project-based Knowledge in Organizing Open Innovation’ draws on innovation management literature and knowledge-based perspectives to investigate the relationship between knowledge development at project level and the strategic organization of open innovation. Addressing the still open issue of how the firm level should be complemented by studies at the project level of analysis, this book provides theoretical and empirical arguments on the advantages of a more fine-grained level of analysis to understand how firms organize their innovation processes across boundaries. The book also addresses the emerging interest in the management literature on project-based organizations, and on the relevance of project forms of organizing in a knowledge-based economy. Through field research in different industrial settings , this book provides empirical evidence on how firms design open innovation project-by-project and it will ...
24 CFR 990.275 - Project-based management (PBM).
2010-04-01
... 24 Housing and Urban Development 4 2010-04-01 2010-04-01 false Project-based management (PBM). 990... URBAN DEVELOPMENT THE PUBLIC HOUSING OPERATING FUND PROGRAM Asset Management § 990.275 Project-based... of rental housing at the project level. Under PBM, these property management services are arranged...
MATHEMATICAL APPARATUS FOR KNOWLEDGE BASE PROJECT MANAGEMENT OF OCCUPATIONAL SAFETY
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Валентина Николаевна ПУРИЧ
2015-05-01
Full Text Available The occupational safety project (OSP management is aimed onto a rational choice implementation. With respect to the subjectivity of management goals the project selection is considered as a minimum formalization level information process, The proposed project selection model relies upon the enterprise’s occupational and industrial safety assessment using fuzzy logic and linguistic variables based on occupational safety knowledge base.
Projection computation based on pixel in simultaneous algebraic reconstruction technique
International Nuclear Information System (INIS)
Wang Xu; Chen Zhiqiang; Xiong Hua; Zhang Li
2005-01-01
SART is an important arithmetic of image reconstruction, in which the projection computation takes over half of the reconstruction time. An efficient way to compute projection coefficient matrix together with memory optimization is presented in this paper. Different from normal method, projection lines are located based on every pixel, and the following projection coefficient computation can make use of the results. Correlation of projection lines and pixels can be used to optimize the computation. (authors)
Application of the Theory of Constraints in Project Based Structures
Directory of Open Access Journals (Sweden)
Martynas Sarapinas
2011-04-01
Full Text Available The article deals with the application of the Theory of Constraints (TOC in project management. This article involves a short introduction to TOC as a project management method and deep analysis of project management specialties using the TOC: TOC based project planning, timetable management, tasks synchronization, project control and “relay runner work ethic”. Moreover, the article describes traditional and TOC based project management theories in their comparison, and emphasize the main benefits we received as the results of the study. Article in Lithuanian
Multi-Role Project (MRP): A New Project-Based Learning Method for STEM
Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric
2016-01-01
This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…
Arantes do Amaral, Joao Alberto
2017-01-01
In this case study we discuss the dynamics that drive a free-of-charge project-based learning extension course. We discuss the lessons learned in the course, "Laboratory of Social Projects." The course aimed to teach project management skills to the participants. It was conducted from August to November of 2015, at Federal University of…
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)
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.
Knowledge Utilization in Projects – a Practice-based
DEFF Research Database (Denmark)
Thuesen, Christian
Drawing upon Practice-based theorizing in general and Actor Network Theory and theories of Communities of Practices in particular the paper develops an analytical strategy for understanding “life” in projects. The analytical strategy is applied on empirical material from an 18-month ethnographic...... study of a construction project. The project is interpreted as constellation of networked practices, which always is in the making. Participation in this project is a learning process where existing practices are reproduced and developed. This understanding of “life” in the project, frames a concluding...... analysis and discussion of the utilization of knowledge in the project....
Software-based annunciator replacement: a tale of two projects
Energy Technology Data Exchange (ETDEWEB)
Simmons, G.T., E-mail: simmongt@westinghouse.com [Westinghouse Electric Company LLC, Cranberry Township, PA (United States)
2015-07-01
Annunciator upgrade projects are often included as parts of operating plant life extension projects as the systems are old and replacement parts are difficult to source. This paper contains case studies of the software-based annunciator replacement projects at the Westinghouse SNUPPS training simulator in Pennsylvania and the Axpo Beznau nuclear power plant in Switzerland. Software-based annunciator systems can offer a number of feature enhancements including improved readability and operator awareness, easy configuration, alarm suppression features, and alarm management at operator workstations. This paper provides an overview of each project and discusses advantages, challenges, and lessons learned from both annunciator-replacement projects. (author)
Software-based annunciator replacement: a tale of two projects
International Nuclear Information System (INIS)
Simmons, G.T.
2015-01-01
Annunciator upgrade projects are often included as parts of operating plant life extension projects as the systems are old and replacement parts are difficult to source. This paper contains case studies of the software-based annunciator replacement projects at the Westinghouse SNUPPS training simulator in Pennsylvania and the Axpo Beznau nuclear power plant in Switzerland. Software-based annunciator systems can offer a number of feature enhancements including improved readability and operator awareness, easy configuration, alarm suppression features, and alarm management at operator workstations. This paper provides an overview of each project and discusses advantages, challenges, and lessons learned from both annunciator-replacement projects. (author)
MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING
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....
Ambidextrous Leadership and Sustainability-Based Project Performance: The Role of Project Culture
Directory of Open Access Journals (Sweden)
Junwei Zheng
2017-12-01
Full Text Available Most studies in the project management field emphasized the effects of leaders or managers, but these effects have rarely been examined at the project level. To cover this research gap, this study applies organizational ambidexterity theory to examine the effects among ambidextrous leadership, ambidextrous culture and sustainability-based project performance. Using a valid sample of 217 project leaders and members from Chinese construction projects, the method of multiple linear regression was adopted to assess the direct relationship among ambidextrous leadership, ambidextrous culture and project performance. Moreover, the bootstrapping technique through structural equation modeling, has been used to analyze the mediating effect of ambidextrous culture. Additionally, the sample data was divided into different groups according to the median value of the variables to conduct the ANOVA and to assess the within-group differences. The results indicated a positive and direct relationship that ambidextrous leadership has on project performance and ambidextrous culture. In addition, there was also a mediating impact of ambidextrous leadership on project performance via ambidextrous culture. Thus, ambidextrous leadership combined with transformational leadership as well as transactional leadership likely has a stronger positive impact on project performance through fostering the adaptive culture and consistent culture. Our findings contribute to an in-depth understanding of the role of the leader and culture for project outcomes. The project-based organization in construction projects could train project leaders’ ambidextrous leadership behavior to facilitate the formation of an ambidextrous culture and to increase project performance. Moreover, this study enriches the existing literature on leadership and project management by highlighting the important path of ambidextrous leadership and ambidextrous culture on the performance at the project level
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.
Project-based organizing and strategic management
DEFF Research Database (Denmark)
very different industries such as management consulting, engineering or entertainment. Contributors analyze PBOs as firms, units or networks of firms set up to complete a specific assignment, as well as address the evolution from traditional operations-driven project management, to the strategic role...
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.
Orthogonal Projection-Based Harmonic Signal Removal for Operational Modal Analysis
DEFF Research Database (Denmark)
Gres, Szymon; Andersen, Palle; Hoen, C.
2019-01-01
A presence of a high amplitude periodic signals in the output responses from operating structures often pose a challenge for output-only system identification and, in case of health monitoring, damage detection/localization methods. This paper introduces a pre-processing approach that removes...... the harmonic part from the output signals directly in the time domain. The new method uses orthogonal projections of the harmonic realization of the signal onto the raw time series within the stochastic subspace framework. Proposed algorithm is tested on two experimental examples. First, an aluminum plate...... excited with both random white and periodic excitations. Second, a full-scale industrial case of a ferry excited by a random environmental load with harmonic interference from a rotating machinery on-board. In both cases the proposed method removes the harmonics from the structural responses while leaving...
A Survey of Technologies Supporting Virtual Project Based Learning
DEFF Research Database (Denmark)
Dirckinck-Holmfeld, Lone
2002-01-01
This paper describes a survey of technologies and to what extent they support virtual project based learning. The paper argues that a survey of learning technologies should be related to concrete learning tasks and processes. Problem oriented project pedagogy (POPP) is discussed, and a framework...... for evaluation is proposed where negotiation of meaning, coordination and resource management are identified as the key concepts in virtual project based learning. Three e-learning systems are selected for the survey, Virtual-U, Lotus Learningspace and Lotus Quickplace, as each system offers different strategies...... for e-learning. The paper concludes that virtual project based learning may benefit from facilities of all these systems....
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.
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
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.
Optimal Design of Large Dimensional Adaptive Subspace Detectors
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.
Optimal Design of Large Dimensional Adaptive Subspace Detectors
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.
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.
Teacher and Student Intrinsic Motivation in Project-Based Learning
Lam, Shui-fong; Cheng, Rebecca Wing-yi; Ma, William Y. K.
2009-01-01
In this study we examined the relationship between teacher and student intrinsic motivation in project-based learning. The participants were 126 Hong Kong secondary school teachers and their 631 students who completed evaluation questionnaires after a semester-long project-based learning program. Both teachers and students were asked to indicate…
Organisational Memories in Project-Based Companies: An Autopoietic View
Koskinen, Kaj U.
2010-01-01
Purpose: The purpose of this paper is to describe project-based companies' knowledge production and memory development with the help of autopoietic epistemology. Design/methodology/approach: The discussion first defines the concept of a project-based company. Then the discussion deals with the two epistemological assumptions, namely cognitivist…
Systemwide Implementation of Project-Based Learning: The Philadelphia Approach
Schwalm, Jason; Tylek, Karen Smuck
2012-01-01
Citywide implementation of project-based learning highlights the benefits--and the challenges--of promoting exemplary practices across an entire out-of-school time (OST) network. In summer 2009, the City of Philadelphia and its intermediary, the Public Health Management Corporation (PHMC), introduced project-based learning to a network of more…
Project-Based Learning Not Just for STEM Anymore
Duke, Nell K.; Halvorsen, Anne-Lise; Strachan, Stephanie L.
2016-01-01
The popularity of project-based learning has been driven in part by a growing number of STEM schools and programs. But STEM subjects are not the only fertile ground for project-based learning (PBL). Social studies and literacy content, too, can be adapted into PBL units to benefit teaching and learning, the authors argue. They review key studies…
Distributed Research Project Scheduling Based on Multi-Agent Methods
Directory of Open Access Journals (Sweden)
Constanta Nicoleta Bodea
2011-01-01
Full Text Available Different project planning and scheduling approaches have been developed. The Operational Research (OR provides two major planning techniques: CPM (Critical Path Method and PERT (Program Evaluation and Review Technique. Due to projects complexity and difficulty to use classical methods, new approaches were developed. Artificial Intelligence (AI initially promoted the automatic planner concept, but model-based planning and scheduling methods emerged later on. The paper adresses the project scheduling optimization problem, when projects are seen as Complex Adaptive Systems (CAS. Taken into consideration two different approaches for project scheduling optimization: TCPSP (Time- Constrained Project Scheduling and RCPSP (Resource-Constrained Project Scheduling, the paper focuses on a multiagent implementation in MATLAB for TCSP. Using the research project as a case study, the paper includes a comparison between two multi-agent methods: Genetic Algorithm (GA and Ant Colony Algorithm (ACO.
Something of value Governance of Projects in the Project-Based Organisation
DEFF Research Database (Denmark)
Riis, Eva
2013-01-01
The paper presents a conceptual framework for studying Governance of Projects and issues of value in the project-based organisation. Attempts at exploring and conceptualising the issues in the current literature are reviewed. Issues pertinent to gaining value by Governance of Projects have mainly...... for a better understanding of Governance of Projects and value contribution. The findings presented here form the conceptual foundation for subsequent empirical studies of value by Governance of Projects....... been explored from the perspective of economic theory. Models found in the literature are not typically concerned with value for the implementing organisation as a whole; their scope is limited to the time just after project completion. No valid, empirically-based models of value creation by Governance...
Risk Assessment of Engineering Project Financing Based on PPP Model
Directory of Open Access Journals (Sweden)
Ma Qiuli
2017-01-01
Full Text Available At present, the project financing channel is single, and the urban facilities are in short supply, and the risk assessment and prevention mechanism of financing should be further improved to reduce the risk of project financing. In view of this, the fuzzy comprehensive evaluation model of project financing risk which combined the method of fuzzy comprehensive evaluation and analytic hierarchy process is established. The scientificalness and effectiveness of the model are verified by the example of the world port project in Luohe city, and it provides basis and reference for engineering project financing based on PPP mode.
Dealing with project complexity by matrix-based propagation modelling for project risk analysis
Fang , Chao; Marle , Franck
2012-01-01
International audience; Engineering projects are facing a growing complexity and are thus exposed to numerous and interdependent risks. In this paper, we present a quantitative method for modelling propagation behaviour in the project risk network. The construction of the network requires the involvement of the project manager and related experts using the Design Structure Matrix (DSM) method. A matrix-based risk propagation model is introduced to calculate risk propagation and thus to re-eva...
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.
Project-Based Learning and Design-Focused Projects to Motivate Secondary Mathematics Students
Remijan, Kelly W.
2017-01-01
This article illustrates how mathematics teachers can develop design-focused projects, related to project-based learning, to motivate secondary mathematics students. With first-hand experience as a secondary mathematics teacher, I provide a series of steps related to the engineering design process, which are helpful to teachers in developing…
Optimal projection of observations in a Bayesian setting
Giraldi, Loic; Le Maî tre, Olivier P.; Hoteit, Ibrahim; Knio, Omar
2018-01-01
, 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
Cao, Xu; Zhang, Bin; Liu, Fei; Wang, Xin; Bai, Jing
2011-12-01
Limited-projection fluorescence molecular tomography (FMT) can greatly reduce the acquisition time, which is suitable for resolving fast biology processes in vivo but suffers from severe ill-posedness because of the reconstruction using only limited projections. To overcome the severe ill-posedness, we report a reconstruction method based on the projected restarted conjugate gradient normal residual. The reconstruction results of two phantom experiments demonstrate that the proposed method is feasible for limited-projection FMT. © 2011 Optical Society of America
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.
Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods
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.
Managing a big ground-based astronomy project: the Thirty Meter Telescope (TMT) project
Sanders, Gary H.
2008-07-01
TMT is a big science project and its scale is greater than previous ground-based optical/infrared telescope projects. This paper will describe the ideal "linear" project and how the TMT project departs from that ideal. The paper will describe the needed adaptations to successfully manage real world complexities. The progression from science requirements to a reference design, the development of a product-oriented Work Breakdown Structure (WBS) and an organization that parallels the WBS, the implementation of system engineering, requirements definition and the progression through Conceptual Design to Preliminary Design will be summarized. The development of a detailed cost estimate structured by the WBS, and the methodology of risk analysis to estimate contingency fund requirements will be summarized. Designing the project schedule defines the construction plan and, together with the cost model, provides the basis for executing the project guided by an earned value performance measurement system.
Technology Base Research Project for electrochemical energy storage
Kinoshita, K.
1985-06-01
The DOE Electrochemical Energy Storage Program is divided into two projects: (1) the exploratory technology development and testing (ETD) project and (2) the technology base research (TBR) project. The role of the TBR Project is to perform supporting research for the advanced battery systems under development by the ETD Project, and to evaluate new systems with potentially superior performance, durability and/or cost characteristics. The specific goal of the TBR Project is to identify the most promising electrochemical technologies and transfer them to industry and/or the ETD Project for further development and scale-up. This report summarizes the research, financial, and management activities relevant to the TBR Project in CY 1984. General problem areas addressed by the project include identification of new electrochemical couples for advanced batteries, determination of technical feasibility of the new couples, improvements in battery components and materials, establishment of engineering principles applicable to electrochemical energy storage and conversion, and the assessment of fuel-cell technology for transportation applications. Major emphasis is given to applied research which will lead to superior performance and lower life-cycle costs. The TBR Project is divided into three major project elements: exploratory research, applied science research, and air systems research.
Fast image matching algorithm based on projection characteristics
Zhou, Lijuan; Yue, Xiaobo; Zhou, Lijun
2011-06-01
Based on analyzing the traditional template matching algorithm, this paper identified the key factors restricting the speed of matching and put forward a brand new fast matching algorithm based on projection. Projecting the grayscale image, this algorithm converts the two-dimensional information of the image into one-dimensional one, and then matches and identifies through one-dimensional correlation, meanwhile, because of normalization has been done, when the image brightness or signal amplitude increasing in proportion, it could also perform correct matching. Experimental results show that the projection characteristics based image registration method proposed in this article could greatly improve the matching speed, which ensuring the matching accuracy as well.
Change Project-Based Learning in Teacher Education in Botswana ...
African Journals Online (AJOL)
Environmental education (EE) and education for sustainable development (ESD) pedagogies are intricate, and to enhance learning, teacher education has to be innovative in teaching approach. This article investigates how the change project approach enhances project-based learning in practice. The investigation is ...
A Project-based Learning approach for teaching Robotics to ...
African Journals Online (AJOL)
In this research we used a project-based learning approach to teach robotics basics to undergraduate business computing students. The course coverage includes basic electronics, robot construction and programming using arduino. Students developed and tested a robot prototype. The project was evaluated using a ...
Quantity versus Quality in Project Based Learning Practices
A. Keegan (Anne); J.R. Turner (Rodney)
2000-01-01
textabstractIn the midst of the turbulence wrought by the global economy, it has become common to see projects as an essential medium for achieving change. However, project based learning practices - as a subset of organizational learning practices- have not kept pace with this development. To
Learning Physics through Project-Based Learning Game Techniques
Baran, Medine; Maskan, Abdulkadir; Yasar, Seyma
2018-01-01
The aim of the present study, in which Project and game techniques are used together, is to examine the impact of project-based learning games on students' physics achievement. Participants of the study consist of 34 9th grade students (N = 34). The data were collected using achievement tests and a questionnaire. Throughout the applications, the…
The Project-Based Learning Approach in Environmental Education
Genc, Murat
2015-01-01
The purpose of this study is to investigate the effect of project-based learning on students' attitudes toward the environment. In the study that was performed with 39 students who take the "Environmental Education" course, attitude changes toward the environment were investigated in students who developed projects on environmental…
Soft Assembling Project-Based Learning and Leadership in Japan
Knight, Kevin; Murphey, Tim
2017-01-01
In this article, we initially focus on how the conceptualization of leadership by Knight (2013a) in his leadership seminars became the basis for choosing a project-based learning (PBL) approach. We then consider how soft assembling can enhance the leadership project activities of student teams and group-work in general classes. Soft assembling…
Project-Based Learning in Electronic Technology: A Case Study
Li, Li
2015-01-01
A case study of project-based learning (PBL) implemented in Tianjin University of Technology and Education is presented. This multidiscipline project is innovated to meet the novel requirements of industry while keeping its traditional effectiveness in driving students to apply knowledge to practice and problem-solving. The implementation of PBL…
Assessment of Project-Based Learning in a MECHATRONICS Context
Doppelt, Yaron
2005-01-01
Project-based learning (PBL) that has authenticity in the pupils' world enables the teaching of science and technology to pupils from a variety of backgrounds. PBL has the potential to enable pupils to research, plan, design, and reflect on the creation of technological projects (Doppelt, 2000). Engineering education, which is common in Israel,…
The Minnesota Articulation Project and Its Proficiency-Based Assessments.
Chalhoub-Deville, Micheline
1997-01-01
Reports on the Minnesota Articulation Project, providing an overview of the projects' three principal working groups: political action, curriculum, and assessment. The article then outlines the theoretical underpinnings of the proficiency-based assessment instruments developed in French, German, and Spanish and describes in detail the content and…
Effective Teaching Methods--Project-based Learning in Physics
Holubova, Renata
2008-01-01
The paper presents results of the research of new effective teaching methods in physics and science. It is found out that it is necessary to educate pre-service teachers in approaches stressing the importance of the own activity of students, in competences how to create an interdisciplinary project. Project-based physics teaching and learning…
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.
Project organized Problem-based learning in Distance Education
DEFF Research Database (Denmark)
Jensen, Lars Peter; Helbo, Jan; Knudsen, Morten
2002-01-01
Project organized problem based learning is a successful concept for on-campus engineering education at Aalborg University. Recently this "Aalborg concept" has been used in networked distance education as well. This paper describes the experiences from two years of Internet-mediated project work...... in a new Master of Information Technology education. The main conclusions are, that the project work is a strong learning motivator, enhancing peer collaboration, for off-campus students as well. However, the concept cannot be directly transferred to off-campus learning. In this paper, the main problems...... experienced with group organized project work in distance education are described, and some possible solutions are listed....
Project-Organized Problem-Based Learning in Distance Education
DEFF Research Database (Denmark)
Jensen, Lars Peter; Helbo, Jan; Knudsen, Morten
2003-01-01
Project organized problem based learning is a successful concept for on-campus engineering education at Aalborg University. Recently this "Aalborg concept" has been used in networked distance education as well. This paper describes the experiences from two years of Internet-mediated project work...... in a new Master of Information Technology education. The main conclusions are, that the project work is a strong learning motivator, enhancing peer collaboration, for off-campus students as well. However, the concept cannot be directly transferred to off-campus learning. In this paper, the main problems...... experienced with group organized project work in distance education are described, and some possible solutions are listed....
Carroll County Competency Based Teacher Certification Project. Librarian's Report.
Coker, Homer; Coker, Joan G.
Reference materials, slides, cassettes, books, pamphlets, materials from other states, and articles used in the Carroll County, Georgia, Competency Based Teacher Certification Project, 1973-74, are listed. Brief annotations are included for both the reference materials and articles. (MJM)
DEVELOPMENT MODEL OF PATISSERIE PROJECT-BASED LEARNING
Ana Ana; Lutfhiyah Nurlaela
2013-01-01
The study aims to find a model of patisserie project-based learning with production approach that can improve effectiveness of patisserie learning. Delphi Technique, Cohen's Kappa and percentages of agreements were used to assess model of patisserie project based learning. Data collection techniques employed in the study were questionnaire, check list worksheet, observation, and interview sheets. Subjects were 13 lectures of expertise food and nutrition and 91 students of Food and Nutrition ...
Project transition to risk based corrective action
International Nuclear Information System (INIS)
Judge, J.M.; Cormier, S.L.
1996-01-01
Many states have adopted or are considering the adoption of the America Society for Testing and Materials (ASTM) Standard E 1739, Standard for Risk Based Corrective Action (RBCA) Applied to Petroleum Release Sites. This standard is being adopted to regulate leaking underground storage tank (LUST) sites. The case studies of two LUST sites in Michigan will be presented to demonstrate the decision making process and limiting factors involved in transitioning sites to the RBCA program. Both of these case studies had been previously investigated and one was actively remediated. The first case study involves a private petroleum facility where soil and ground water have been impacted. Remediation involved a ground water pump and treat system. Subsequent monitoring during system operation indicated that analytical data were still above the Tier 1 RBSLs but below the Tier 2 SSTLs. The closure strategy that was developed was based on the compounds of concern that were below the SSTLs. A deed restriction was also developed for the site as an institutional control. The second LUST site exhibited BTEX concentrations in soil and ground water above the Tier 1 RBSLs. Due to the exceedence of the Tier 1 RBSLs, the second site required a Tier 2 assessment to develop SSTLs as remedial objectives and remove hot spots in the soil and treat the ground water to achieve closure. Again, a deed restriction was instituted along with a performance monitoring plan
Process competencies in a problem and project based learning environment
DEFF Research Database (Denmark)
Du, Xiangyun; Kolmos, Anette
2006-01-01
with the expected professional competencies. Based on the educational practice of PBL Aalborg Model, which is characterized by problem-orientation, project-organization and team work, this paper examines the process of developing process competencies through studying engineering in a PBL environment from...... process competencies through doing problem and project based work in teams? 2) How do students perceive their achievement of these process competencies?......Future engineers are not only required to master technological competencies concerning solving problems, producing and innovating technology, they are also expected to have capabilities of cooperation, communication, and project management in diverse social context, which are referred to as process...
Problem-based and project-oriented learning
DEFF Research Database (Denmark)
Blaabjerg, Frede; Teodorescu, Remus; Chen, Zhe
2005-01-01
. Generally, the content of the curriculum should be more expanded without extra study time. This paper presents a teaching approach, which makes it possible very fast for the students to obtain in-depth skills into new research areas, and this method is the problem-oriented and project-based learning....... In this paper the necessary skills for power electronic engineers are outlined that is followed up by a description on how the problem-oriented and project-based learning are implemented. A complete curriculum in power electronics and drives at Aalborg University is presented where different power electronics...... related projects at different study levels also are presented....
Project-Based Learning in Programmable Logic Controller
Seke, F. R.; Sumilat, J. M.; Kembuan, D. R. E.; Kewas, J. C.; Muchtar, H.; Ibrahim, N.
2018-02-01
Project-based learning is a learning method that uses project activities as the core of learning and requires student creativity in completing the project. The aims of this study is to investigate the influence of project-based learning methods on students with a high level of creativity in learning the Programmable Logic Controller (PLC). This study used experimental methods with experimental class and control class consisting of 24 students, with 12 students of high creativity and 12 students of low creativity. The application of project-based learning methods into the PLC courses combined with the level of student creativity enables the students to be directly involved in the work of the PLC project which gives them experience in utilizing PLCs for the benefit of the industry. Therefore, it’s concluded that project-based learning method is one of the superior learning methods to apply on highly creative students to PLC courses. This method can be used as an effort to improve student learning outcomes and student creativity as well as to educate prospective teachers to become reliable educators in theory and practice which will be tasked to create qualified human resources candidates in order to meet future industry needs.
Continued multidisciplinary project-based learning - implementation in health informatics.
Wessel, C; Spreckelsen, C
2009-01-01
Problem- and project-based learning are approved methods to train students, graduates and post-graduates in scientific and other professional skills. The students are trained on realistic scenarios in a broader context. For students specializing in health informatics we introduced continued multidisciplinary project-based learning (CM-PBL) at a department of medical informatics. The training approach addresses both students of medicine and students of computer science. The students are full members of an ongoing research project and develop a project-related application or module, or explore or evaluate a sub-project. Two teachers guide and review the students' work. The training on scientific work follows a workflow with defined milestones. The team acts as peer group. By participating in the research team's work the students are trained on professional skills. A research project on a web-based information system on hospitals built the scenario for the realistic context. The research team consisted of up to 14 active members at a time, who were scientists and students of computer science and medicine. The well communicated educational approach and team policy fostered the participation of the students. Formative assessment and evaluation showed a considerable improvement of the students' skills and a high participant satisfaction. Alternative education approaches such as project-based learning empower students to acquire scientific knowledge and professional skills, especially the ability of life-long learning, multidisciplinary team work and social responsibility.
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.)
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.
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.
Renewable based hydrogen energy projects in remote and island communities
International Nuclear Information System (INIS)
Miles, S.; Gillie, M.
2009-01-01
Task 18 working group of the International Energy Agency's Hydrogen Implementing Agreement has been evaluating and documenting experiences with renewable based hydrogen energy projects in remote and island communities in the United Kingdom, Canada, Norway, Iceland, Gran Canaria, Spain and New Zealand. The objective was to examine the lessons learned from existing projects and provide recommendations regarding the effective development of hydrogen systems. In order to accomplish this task, some of the drivers behind the niche markets where hydrogen systems have already been developed, or are in the development stages, were studied in order to determine how these could be expanded and modified to reach new markets. Renewable based hydrogen energy projects for remote and island communities are currently a key niche market. This paper compared various aspects of these projects and discussed the benefits, objectives and barriers facing the development of a hydrogen-based economy
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.
An accurate projection algorithm for array processor based SPECT systems
International Nuclear Information System (INIS)
King, M.A.; Schwinger, R.B.; Cool, S.L.
1985-01-01
A data re-projection algorithm has been developed for use in single photon emission computed tomography (SPECT) on an array processor based computer system. The algorithm makes use of an accurate representation of pixel activity (uniform square pixel model of intensity distribution), and is rapidly performed due to the efficient handling of an array based algorithm and the Fast Fourier Transform (FFT) on parallel processing hardware. The algorithm consists of using a pixel driven nearest neighbour projection operation to an array of subdivided projection bins. This result is then convolved with the projected uniform square pixel distribution before being compressed to original bin size. This distribution varies with projection angle and is explicitly calculated. The FFT combined with a frequency space multiplication is used instead of a spatial convolution for more rapid execution. The new algorithm was tested against other commonly used projection algorithms by comparing the accuracy of projections of a simulated transverse section of the abdomen against analytically determined projections of that transverse section. The new algorithm was found to yield comparable or better standard error and yet result in easier and more efficient implementation on parallel hardware. Applications of the algorithm include iterative reconstruction and attenuation correction schemes and evaluation of regions of interest in dynamic and gated SPECT
Projection-slice theorem based 2D-3D registration
van der Bom, M. J.; Pluim, J. P. W.; Homan, R.; Timmer, J.; Bartels, L. W.
2007-03-01
In X-ray guided procedures, the surgeon or interventionalist is dependent on his or her knowledge of the patient's specific anatomy and the projection images acquired during the procedure by a rotational X-ray source. Unfortunately, these X-ray projections fail to give information on the patient's anatomy in the dimension along the projection axis. It would be very profitable to provide the surgeon or interventionalist with a 3D insight of the patient's anatomy that is directly linked to the X-ray images acquired during the procedure. In this paper we present a new robust 2D-3D registration method based on the Projection-Slice Theorem. This theorem gives us a relation between the pre-operative 3D data set and the interventional projection images. Registration is performed by minimizing a translation invariant similarity measure that is applied to the Fourier transforms of the images. The method was tested by performing multiple exhaustive searches on phantom data of the Circle of Willis and on a post-mortem human skull. Validation was performed visually by comparing the test projections to the ones that corresponded to the minimal value of the similarity measure. The Projection-Slice Theorem Based method was shown to be very effective and robust, and provides capture ranges up to 62 degrees. Experiments have shown that the method is capable of retrieving similar results when translations are applied to the projection images.
Maleki, Reza A.
2009-01-01
This is the first of two articles in which the author shares experiences gained from the development and delivery of a business/industry project-based capstone course. The course integrates research, proposal development and design experience based on knowledge and skills acquired in earlier coursework. The course also incorporates standards and…
Collaborative Project-Based Learning: An Integrative Science and Technological Education Project
Baser, Derya; Ozden, M. Yasar; Karaarslan, Hasan
2017-01-01
Background: Blending collaborative learning and project-based learning (PBL) based on Wolff (2003) design categories, students interacted in a learning environment where they developed their technology integration practices as well as their technological and collaborative skills. Purpose: The study aims to understand how seventh grade students…
Robust subspace estimation using low-rank optimization theory and applications
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
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....
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....
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.
From cases to projects in problem-based medical education
Directory of Open Access Journals (Sweden)
Diana Stentoft
2014-06-01
Full Text Available Problem-based learning (PBL based on patient cases has become a well-established worldwide educational approach in medical education. Recent studies indicate that case-based PBL when used throughout an entire curriculum may develop into a counter-productive routine for students as well as teachers. Consequently, there is a need to develop PBL approaches further allowing students to work with more ill-defined problems and alternative learning structures. In this paper, we argue that this can be realised by introducing project-PBL into the medical curriculum, as in the medical education at Aalborg University, Denmark. We outline organisations of case- and project- PBL in the medical curriculum and present an explorative study of 116 first and second year students’ experiences working in the two settings of PBL. Results reveal that students generally rate their PBL experiences positively however, project-PBL is rated more positively than case-PBL on all parameters studied. These results invite further consideration of the differences in working with cases and projects. Two central differences are discussed; the nature of the problem as the trigger of learning and students' possibilities for directing their own learning processes. The study demonstrates that introducing project-PBL may contribute significantly in problem-based medical education. However, the need for extensive research into advantages and limitations of the combined use of case- and project-PBL is also emphasised.
THE BASES OF THE MANAGEMENT OF SOCIAL PROJECTS OF BUSINESS
Directory of Open Access Journals (Sweden)
O. B. Zilberstein
2015-01-01
Full Text Available In article on wide material the author opens basic approaches to formation of the theoretical base of management of social projects of business.The purpose of work is generalization of theoretical bases of project management of business which have social character. The logic of a statement of material is under construction as follows. In water part of article authors do the assumption that at the heart of an object of research three basic elements are put: project management, social management and management of social projects. Proceeding from this assumption in work further reasonings are under construction. The main part of article contains three subsections. In the fi rst subsection of article defi nition of the concept «project management» is formulated and methodological approaches to project management (PRINCE2, PERT, the Model Agile, Lean Management, BRM are generalized. In the second subsection the author's understanding of entity of social management is presented and the main techniques of social management are considered, and also the characteristic of methodology of management of social projects is given. In the third subsection the author's concept of entity of management of social projects of business is formulated. In fi nal part of article authors draw a conclusion about the importance of management of social projects of business. This article, fi rst of all, has theoretical and methodological value for formation of eff ective methods and instruments of management of social projects of business, and also for development of the theory and practice of corporate social responsibility.
Projector primary-based optimization for superimposed projection mappings
Ahmed, Bilal; Lee, Jong Hun; Lee, Yong Yi; Lee, Kwan H.
2018-01-01
Recently, many researchers have focused on fully overlapping projections for three-dimensional (3-D) projection mapping systems but reproducing a high-quality appearance using this technology still remains a challenge. On top of existing color compensation-based methods, much effort is still required to faithfully reproduce an appearance that is free from artifacts, colorimetric inconsistencies, and inappropriate illuminance over the 3-D projection surface. According to our observation, this is due to the fact that overlapping projections are treated as an additive-linear mixture of color. However, this is not the case according to our elaborated observations. We propose a method that enables us to use high-quality appearance data that are measured from original objects and regenerate the same appearance by projecting optimized images using multiple projectors, ensuring that the projection-rendered results look visually close to the real object. We prepare our target appearances by photographing original objects. Then, using calibrated projector-camera pairs, we compensate for missing geometric correspondences to make our method robust against noise. The heart of our method is a target appearance-driven adaptive sampling of the projection surface followed by a representation of overlapping projections in terms of the projector-primary response. This gives off projector-primary weights to facilitate blending and the system is applied with constraints. These samples are used to populate a light transport-based system. Then, the system is solved minimizing the error to get the projection images in a noise-free manner by utilizing intersample overlaps. We ensure that we make the best utilization of available hardware resources to recreate projection mapped appearances that look as close to the original object as possible. Our experimental results show compelling results in terms of visual similarity and colorimetric error.
Directory of Open Access Journals (Sweden)
Guangdong Wu
2013-09-01
Full Text Available Purpose: Within project-based supply chain inter-organizational cooperative innovation, the achievement of project value-adding reflects by factors such as project-based organizational effect level, the relationship between project cooperative innovation objectives etc. The purpose is to provide a reliable reference for the contractor reasonably allocate the effect level and resources between the knowledge input and innovation stage and realize the knowledge collaboration for project-based supply chain. Design/methodology/approach: Based on the assumption of equal cooperation between project-based organizations, from the view of maximizing project value-adding and the relationship of effect cost between knowledge input and innovation stage in consideration, the knowledge collaborative incentive model for project-based supply chain inter-organizational cooperative innovation was established, and solved through the first-order and second-order approach, then the digital simulation and example analysis were presented. Findings: The results show that, the project management enterprise resorted to adjust project knowledge collaboration incentive intensity and implemented knowledge input-innovation coordinative incentive strategy, not only could achieve project value-adding maximization, but also could realize net earnings Pareto improvement between project management enterprise and contractor. Research limitations/implications: To simplify the knowledge flow among project-based organizations, the knowledge flow in the model hypothesis is presented as knowledge input and knowledge innovation stage, thus it may affect the final analysis results. Originality/value: In construction project practice, knowledge is become more and more important to achieve project value-adding. The research can provide a theoretical guideline for the project-based organizations, such as the contractor, the owner, especially how to utilize their core knowledge.
Meeting the challenges of developing LED-based projection displays
Geißler, Enrico
2006-04-01
The main challenge in developing a LED-based projection system is to meet the brightness requirements of the market. Therefore a balanced combination of optical, electrical and thermal parameters must be reached to achieve these performance and cost targets. This paper describes the system design methodology for a digital micromirror display (DMD) based optical engine using LEDs as the light source, starting at the basic physical and geometrical parameters of the DMD and other optical elements through characterization of the LEDs to optimizing the system performance by determining optimal driving conditions. LEDs have a luminous flux density which is just at the threshold of acceptance in projection systems and thus only a fully optimized optical system with a matched set of LEDs can be used. This work resulted in two projection engines, one for a compact pocket projector and the other for a rear projection television, both of which are currently in commercialization.
Data base on nuclear power plant dose reduction research projects
Energy Technology Data Exchange (ETDEWEB)
Khan, T.A.; Dionne, B.J.; Baum, J.W.
1985-12-01
This report contains project information on the research and development activities of the nuclear power industry in the area of dose reduction. It is based on a data base of information set up at the ALARA Center of Brookhaven National Laboratory. One purpose of this report is to draw attention to work in progress and to enable researchers and subscribers to obtain further information from the investigators and project managers. Information is provided on 180 projects, divided according to whether they are oriented to Engineering Research or to Health Physics Technology. The report contains indices on main category, project manager, principal investigator, sponsoring organization, contracting organization, and subject. This is an initial report. It is intended that periodic updates be issued whenever sufficient material has been accumulated.
Data base on nuclear power plant dose reduction research projects
International Nuclear Information System (INIS)
Khan, T.A.; Dionne, B.J.; Baum, J.W.
1985-12-01
This report contains project information on the research and development activities of the nuclear power industry in the area of dose reduction. It is based on a data base of information set up at the ALARA Center of Brookhaven National Laboratory. One purpose of this report is to draw attention to work in progress and to enable researchers and subscribers to obtain further information from the investigators and project managers. Information is provided on 180 projects, divided according to whether they are oriented to Engineering Research or to Health Physics Technology. The report contains indices on main category, project manager, principal investigator, sponsoring organization, contracting organization, and subject. This is an initial report. It is intended that periodic updates be issued whenever sufficient material has been accumulated
PACS project management utilizing web-based tools
Patel, Sunil; Levin, Brad; Gac, Robert J., Jr.; Harding, Douglas, Jr.; Chacko, Anna K.; Radvany, Martin; Romlein, John R.
2000-05-01
As Picture Archiving and Communications Systems (PACS) implementations become more widespread, the management of deploying large, multi-facility PACS will become a more frequent occurrence. The tools and usability of the World Wide Web to disseminate project management information obviates time, distance, participant availability, and data format constraints, allowing for the effective collection and dissemination of PACS planning, implementation information, for a potentially limitless number of concurrent PACS sites. This paper will speak to tools, such as (1) a topic specific discussion board, (2) a 'restricted' Intranet, within a 'project' Intranet. We will also discuss project specific methods currently in use in a leading edge, regional PACS implementation concerning the sharing of project schedules, physical drawings, images of implementations, site-specific data, point of contacts lists, project milestones, and a general project overview. The individual benefits realized for the end user from each tool will also be covered. These details will be presented, balanced with a spotlight on communication as a critical component of any project management undertaking. Using today's technology, the web arguably provides the most cost and resource effective vehicle to facilitate the broad based, interactive sharing of project information.
Navigating Evidence-Based Practice Projects: The Faculty Role.
Moch, Susan D; Quinn-Lee, Lisa; Gallegos, Cara; Sortedahl, Charlotte K
: An innovative way to facilitate evidence-based practice (EBP) learning and to get evidence into practice is through academic-clinical agency projects involving faculty, undergraduate students, and agency staff. The central role of the faculty is key to successful academic-clinical agency partnerships. Faculty navigate the often difficult process of focusing students and engaging busy staff through initiating, maintaining, and evaluating projects. Students learn valuable EBP skills, staff become engaged in EBP, and the projects are rated highly by agency administrators.
Performance-based procurement for low disturbance brdige construction projects
Sebastian, R.; Claeson-Jonsson, C.; Di Giulio, R.
2013-01-01
Purpose – This paper aims to introduce a method of performance-based procurement, based on the most economically advantageous tender (MEAT), for low-disturbance bridge construction projects in urban environment. Design/methodology/approach – The first part of this paper reviews the key performance
Project Based Learning in Multi-Grade Class
Ciftci, Sabahattin; Baykan, Ayse Aysun
2013-01-01
The purpose of this study is to evaluate project based learning in multi-grade classes. This study, based on a student-centered learning approach, aims to analyze students' and parents' interpretations. The study was done in a primary village school belonging to the Centre of Batman, already adapting multi-grade classes in their education system,…
Implementing Project Based Learning Approach to Graphic Design Course
Riyanti, Menul Teguh; Erwin, Tuti Nuriah; Suriani, S. H.
2017-01-01
The purpose of this study was to develop a learning model based Commercial Graphic Design Drafting project-based learning approach, was chosen as a strategy in the learning product development research. University students as the target audience of this model are the students of the fifth semester Visual Communications Design Studies Program…
Interdisciplinary project-based learning: technology for improving student cognition
Natalia Stozhko; Boris Bortnik; Ludmila Mironova; Albina Tchernysheva; Ekaterina Podshivalova
2015-01-01
The article studies a way of enhancing student cognition by using interdisciplinary project-based learning (IPBL) in a higher education institution. IPBL is a creative pedagogic approach allowing students of one area of specialisation to develop projects for students with different academic profiles. The application of this approach in the Ural State University of Economics resulted in a computer-assisted learning system (CALS) designed by IT students. The CALS was used in an analytical chemi...
Project-Based Laboratory Experiences in Mechanical Engineering
Directory of Open Access Journals (Sweden)
Narendra Sharma
2011-12-01
Full Text Available In this paper we describe project-based laboratories in Mechanical Engineering designed to provide semester-long team experiences which mimic the real life industrial processes of design, development, testing and optimization. The labs are focused on courses at the sophomore level and thus require special attention to constraints of student backgrounds and experience. This paper describes laboratory projects in Dynamics and Fluid Mechanics.
Grant Project Information via a Shared Data Base
Directory of Open Access Journals (Sweden)
Justine Roberts
1973-09-01
Full Text Available A quarterly keyword index to campus grant projects is provided by the Health Science Library at the University of California, San Francisco, using a data base created and maintained by the campus' Contracts & Grants Office. The index is printed in KWOC format, using the chief investigator's name as the key to a section of project summaries. A third section is also included, listing the summaries under the name of the sponsoring department.
An Agent Based Approach for Project Management in Construction
Sencer, Safiye; Turgay, Tahsin
2013-01-01
Project management has an important role in terms of time, cost and flexibility. An agentbased architecture provides additional robustness, scalability, flexibility that is particularly appropriate for problems with a dynamic and distributed nature. Integrated agent based project management covers design and construction planning. It is combined with plan execution, tolerating both the design and plan, which may be changed as necessary. In this reason, the decision making process requires tha...
Evaluation of project based learning sufficiency of teacher candidates
Directory of Open Access Journals (Sweden)
Vasfi Tugun
2012-01-01
Full Text Available The aim of that research, it is the project based learning process suffuciency of teacher candidates who developedmultimedia by working in online and blended groups. Importance of research Being able to guide to studies that is going tobe done about assessment of multimedia projection for project based educational application to teachers and teachercandidates and It has been thought as an advisor source about being arranged new educational environment for the futureto teacher and teacher candidates for project based educational application and multimedia projection. Research is anexperimental study and has been shaped according to pre-test and last-test research model with the two groups. This groupsare online group and blended group. Discussion of research In the result of the study, in the process of project basedlearning, it is determined that the success level in multimedia development of teacher candidates who work in blendedlearning model is higher than the success level of teacher candidates who work in online learning model.
The Effectiveness of Project Based Learning in Trigonometry
Gerhana, M. T. C.; Mardiyana, M.; Pramudya, I.
2017-09-01
This research aimed to explore the effectiveness of Project-Based Learning (PjBL) with scientific approach viewed from interpersonal intelligence toward students’ achievement learning in mathematics. This research employed quasi experimental research. The subjects of this research were grade X MIPA students in Sleman Yogyakarta. The result of the research showed that project-based learning model is more effective to generate students’ mathematics learning achievement that classical model with scientific approach. This is because in PjBL model students are more able to think actively and creatively. Students are faced with a pleasant atmosphere to solve a problem in everyday life. The use of project-based learning model is expected to be the choice of teachers to improve mathematics education.
Controlled cooling of an electronic system based on projected conditions
David, Milnes P.; Iyengar, Madhusudan K.; Schmidt, Roger R.
2015-08-18
Energy efficient control of a cooling system cooling an electronic system is provided based, in part, on projected conditions. The control includes automatically determining an adjusted control setting(s) for an adjustable cooling component(s) of the cooling system. The automatically determining is based, at least in part, on projected power consumed by the electronic system at a future time and projected temperature at the future time of a heat sink to which heat extracted is rejected. The automatically determining operates to reduce power consumption of the cooling system and/or the electronic system while ensuring that at least one targeted temperature associated with the cooling system or the electronic system is within a desired range. The automatically determining may be based, at least in part, on an experimentally obtained model(s) relating the targeted temperature and power consumption of the adjustable cooling component(s) of the cooling system.
Directory of Open Access Journals (Sweden)
Maurizio Bevilacqua
2014-09-01
Full Text Available Purpose: This work investigates the influence of project managers’ personality on the success of a project in a Multinational Corporation. The methodology proposed for analyzing the project managers’ personality is based on the Myers-Briggs Type Indicator.Design/methodology/approach: Forty projects carried out in 2012 by multinational corporation, concerning new product development (NPD, have been analyzed, comparing the profile of project managers with results obtained in terms of traditional performance indexes (time delay and over-budget of projects and performance indexes usually used in “Lean Production” sector (waste time and type of “wastes”. A detailed analysis of the most important “wastes” during the project development is carried out using the Value Stream Mapping (VSM technique.Findings and Originality/value: Relying on the Myers–Briggs personality instrument, results show that extroverted managers (as opposed to introverted managers carry out projects that show lower delay and lower waste time. Introverted managers often make “Over-processing” and “Defect” types of waste. Moreover, lower delay and over-budget have been shown by perceiving managers.Research limitations: Regarding the limitations of this work it is necessary to highlight that we collected data from project managers in a retrospective way. While we believe that several aspects of our data collection effort helped enhance the accuracy of the results, future research could conduct real-time case study research to get more detailed insights into the proposed relationships and avoid retrospective bias. Moreover we focused on a single respondent, the project manager. This helped us ensure that their interpretations played an important role in product development. But, we cannot examined the opinion of team members that could be different from project managers opinion regarding some questions.Originality/value: This research provides insight useful
Using Web-Based Peer Benchmarking to Manage the Client-Based Project
Raska, David; Keller, Eileen Weisenbach; Shaw, Doris
2013-01-01
The complexities of integrating client-based projects into marketing courses provide challenges for the instructor but produce richness of context and active learning for the student. This paper explains the integration of Web-based peer benchmarking as a means of improving student performance on client-based projects within a single semester in…
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...
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)
A frequency domain subspace algorithm for mixed causal, anti-causal LTI systems
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
A web-based repository of surgical simulator projects.
Leskovský, Peter; Harders, Matthias; Székely, Gábor
2006-01-01
The use of computer-based surgical simulators for training of prospective surgeons has been a topic of research for more than a decade. As a result, a large number of academic projects have been carried out, and a growing number of commercial products are available on the market. Keeping track of all these endeavors for established groups as well as for newly started projects can be quite arduous. Gathering information on existing methods, already traveled research paths, and problems encountered is a time consuming task. To alleviate this situation, we have established a modifiable online repository of existing projects. It contains detailed information about a large number of simulator projects gathered from web pages, papers and personal communication. The database is modifiable (with password protected sections) and also allows for a simple statistical analysis of the collected data. For further information, the surgical repository web page can be found at www.virtualsurgery.vision.ee.ethz.ch.
Application of Technology in Project-Based Distance Learning
Directory of Open Access Journals (Sweden)
Ali Mehrabian
2008-06-01
Full Text Available Present technology and the accessibility of internet have made distance learning easier, more efficient, and more convenient for students. This technology allows instructors and students to communicate asynchronously, at times and locations of their own choosing, by exchanging printed or electronic information. The use of project-based approach is being recognized in the literature as a potential component of courses in the faculties of engineering, science, and technology. Instructors may have to restructure their course differently to accommodate and facilitate the effectiveness of distance learning. A project-based engineering course, traditionally taught in a classroom settings using live mode at the College of Engineering and Computer Sciences at the University of Central Florida (UCF has been transformed to a distance course taught using distance modes. In this case, pedagogical transitions and adjustments are required, in particular for obtaining an optimal balance between the course material and the project work. Project collaboration in groups requires communication, which is possible with extensive utilization of new information and communication technology, such as virtual meetings. This paper discusses the course transition from live to distance modes and touches on some issues as they relate to the effectiveness of this methodology and the lessons learned from its application within different context. More specifically, this discussion includes the benefit of implementing project-based work in the domain of the distance learning courses.
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
Organizational Enablers for Project Governance and Governmentality in Project-based Organizations
DEFF Research Database (Denmark)
Pemsel, Sofia; Müller, Ralf; Shao, Jingting
for governmentality belong mainly to the cultural-cognitive pillar. Collectively these OEs provide for an ambidexterity of flexibility and stability, which allows organizations to align their internal characteristics with their organizational context. Managerial and theoretical implications of the results......This study identifies organizational enablers (OEs) for governance and governmentality in the realm of projects in project-based organizations (PBOs). We use a multiple case study design with six firms in Sweden and China to identify OEs. Institutional theory serves as theoretical perspective...
Organizational Enablers for Project Governance and Governmentality in Project-based Organizations
DEFF Research Database (Denmark)
Müller, Ralf; Pemsel, Sofia; Shao, Jingting
2015-01-01
for governmentality belong mainly to the cultural–cognitive pillar. Collectively these OEs provide for an ambidexterity of flexibility and stability, which allows organizations to align their internal characteristics with their organizational context. Managerial and theoretical implications of the results......This study identifies organizational enablers (OEs) for governance and governmentality in the realm of projects in project-based organizations (PBOs). We use a multiple case study design with six firms in Sweden and China to identify OEs. Institutional theory serves as theoretical perspective...
Simulation-based valuation of project finance investments. Crucial aspects of power plant projects
Energy Technology Data Exchange (ETDEWEB)
Pietz, Matthaeus
2010-12-15
The liberalization of electricity markets transformed a regulated and stable market to a market with former unknown price volatility. This results in a high uncertainty which is mainly due to the, from an economic point of view, lack of storability of the commodity electricity. Thus investments in power plants are highly risky. This dissertation analyzes crucial aspects within the valuation of a power plant financed via project finance, a popular financing method for projects with high capital requirements. Starting with the development of a valuation model based on stochastic modelling of the future cash flows the focus of the analysis is on the impact of model complexity and electricity prices. (orig.)
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
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
Expert Systems Based Clinical Assessment and Tutorial Project.
Papa, Frank; Shores, Jay
This project at the Texas College of Osteopathic Medicine (Fort Worth) evaluated the use of an artificial-intelligence-derived measure, "Knowledge-Based Inference Tool" (KBIT), as the basis for assessing medical students' diagnostic capabilities and designing instruction to improve diagnostic skills. The instrument was designed to…
A Project-Based Biologically-Inspired Robotics Module
Crowder, R. M.; Zauner, K.-P.
2013-01-01
The design of any robotic system requires input from engineers from a variety of technical fields. This paper describes a project-based module, "Biologically-Inspired Robotics," that is offered to Electronics and Computer Science students at the University of Southampton, U.K. The overall objective of the module is for student groups to…
Conflict from Teamwork in Project-Based Collaborative Learning
Chan, Lim Ha; Chen, Ching-Huei
2010-01-01
This study investigated the conflict occurring during teamwork among college seniors in project-based collaborative learning in a capstone course. It found that conflict emerged with poor communication, task management, and work allocation; unequal treatments among classmates; egocentricity; a clash of values; and lack of responsibility and…
Adaptive Knowledge Management of Project-Based Learning
Tilchin, Oleg; Kittany, Mohamed
2016-01-01
The goal of an approach to Adaptive Knowledge Management (AKM) of project-based learning (PBL) is to intensify subject study through guiding, inducing, and facilitating development knowledge, accountability skills, and collaborative skills of students. Knowledge development is attained by knowledge acquisition, knowledge sharing, and knowledge…
Project-Based Management Development: "The Volvo Story."
Branch, John; Smith, Bryan
1992-01-01
A modular group project-based approach to management development was implemented by Volvo Concessionaires (United Kingdom) in partnership with training consultants. Ingredients of its success included top-level commitment, investment in diagnosis and tailoring, and use of company-specific case studies. (SK)
ICT in Problem- and Project-based Learning
DEFF Research Database (Denmark)
Andreasen, Lars Birch; Lerche Nielsen, Jørgen
2012-01-01
The paper discusses how teaching and learning practices at universities can implement new information technologies, inspired by the traditions of problem- and project-based learning. The changing roles in the teacher-student relationship, and students’ development of information literacy are disc...
Perceptions of Teacher Candidates Regarding Project-Based Learning
Baysura, Ozge Deniz; Altun, Sertel; Yucel-Toy, Banu
2016-01-01
Problem Statement: Project-based learning (PBL) is a learning and teaching approach that makes students search for new knowledge and skills, helps them overcome real-life questions, and makes them design their own studies and performances. Research in Turkey reveals that teachers are not well-informed about PBL, can not guide students in this…
Implementation of a Project-Based Telecommunications Engineering Design Course
Aliakbarian, Hadi; Soh, Ping Jack; Farsi, Saeed; Xu, Hantao; Van Lil, Emmanuel H. E. M. J. C.; Nauwelaers, Bart K. J. C.; Vandenbosch, Guy A. E.; Schreurs, Dominique M. M.-P.
2014-01-01
This paper describes and discusses the implementation of a project-based graduate design course in telecommunications engineering. This course, which requires a combination of technical and soft skills for its completion, enables guided independent learning (GIL) and application of technical knowledge acquired from classroom learning. Its main…
Health. Nevada Competency-Based Adult High School Diploma Project.
Nevada Univ., Las Vegas. Coll. of Education.
This document is one of ten curriculum guides developed by the Nevada Competency-Based Adult High School Diploma (CBAHSD) Project. This curriculum guide on health is divided into ten topics. The topics included are Nutrition, Reproduction, Menstruation, Contraception, Alcohol Abuse, Tobacco, Immunization, Disease, Accident Prevention, and…
Creativity Assessment in the Context of Maker-Based Projects
Lille, Benjamin; Romero, Margarida
2017-01-01
Creativity is a key competence in 21st century education. Among the active learning pedagogies which aims to develop creativity, learning by making is an emerging approach in which the students are engaged in the co-creation of a shared artefact. In this study, we aim to analyse the creativity competency through a maker-based projects.…
Introducing Project-Based Learning to Design Enterprises for Creativity
DEFF Research Database (Denmark)
Wang, Feng; Zhou, Chunfang; Chen, Hongbing
2013-01-01
This paper aims to emphasize the necessity of introducing Project-Based Learning (PBL) to design enterprises in order to foster designers creativity and facilitate innovation of design enterprises. According to the literature review, creativity can be viewed as the first stage of innovation; PBL...
Investigating Science Interest in a Game-Based Learning Project
Annetta, Leonard; Vallett, David; Fusarelli, Bonnie; Lamb, Richard; Cheng, Meng-Tzu; Holmes, Shawn; Folta, Elizabeth; Thurmond, Brandi
2014-01-01
The purpose of this study was to examine the effect Serious Educational Games (SEGs) had on student interest in science in a federally funded game-based learning project. It can be argued that today's students are more likely to engage in video games than they are to interact in live, face-to-face learning environments. With a keen eye on…
Advanced prototyping tools for project- and problem-based learning
DEFF Research Database (Denmark)
Teodorescu, Remus; Bech, Michael Møller; Holm, Allan J.
2002-01-01
A new approach in prototyping for project- and problem-based learning is achieved by using the new Total Development Environment concept introduced by dSPACE that allows a full visual block-oriented programming of dynamic real-time systems to be achieved using the Matlab/Simulink environment...
Enhancing College Students' Life Skills through Project Based Learning
Wurdinger, Scott; Qureshi, Mariam
2015-01-01
This study examined whether life skills could be developed in a Project Based Learning (PBL) course. The participants were students enrolled in a graduate level PBL course. The same 35-question survey was given to students at the beginning and end of the course, and students were asked to rank their life skills using a Likert scale. Additionally,…
Project-Based Learning: A Review of the Literature
Kokotsaki, Dimitra; Menzies, Victoria; Wiggins, Andy
2016-01-01
Project-based learning (PBL) is an active student-centred form of instruction which is characterised by students' autonomy, constructive investigations, goal-setting, collaboration, communication and reflection within real-world practices. It has been explored in various contexts and in different phases of schooling, from primary to higher…
Facilitating Problem Framing in Project-Based Learning
Svihla, Vanessa; Reeve, Richard
2016-01-01
While problem solving is a relatively well understood process, problem framing is less well understood, particularly with regard to supporting students to learn as they frame problems. Project-based learning classrooms are an ideal setting to investigate how teachers facilitate this process. Using participant observation, this study investigated…
Project-Based Learning in Geotechnics: Cooperative versus Collaborative Teamwork
Pinho-Lopes, Margarida; Macedo, Joaquim
2016-01-01
Since 2007/2008 project-based learning models have been used to deliver two fundamental courses on Geotechnics in University of Aveiro, Portugal. These models have evolved and have encompassed either cooperative or collaborative teamwork. Using data collected in five editions of each course (Soil Mechanics I and Soil Mechanics II), the different…
Teacher Perceptions of Project Based Learning in the Secondary Classroom
MacMath, Sheryl; Sivia, Awneet; Britton, Vandy
2017-01-01
This study examines teacher perceptions of their experiences with Project Based Learning (PBL) at a secondary school in Western Canada. This PBL initiative included English language arts, mathematics, science, and digital literacy courses and all the grade nines at this large secondary school. This article reports on two teacher focus group…
Incremental projection approach of regularization for inverse problems
Energy Technology Data Exchange (ETDEWEB)
Souopgui, Innocent, E-mail: innocent.souopgui@usm.edu [The University of Southern Mississippi, Department of Marine Science (United States); Ngodock, Hans E., E-mail: hans.ngodock@nrlssc.navy.mil [Naval Research Laboratory (United States); Vidard, Arthur, E-mail: arthur.vidard@imag.fr; Le Dimet, François-Xavier, E-mail: ledimet@imag.fr [Laboratoire Jean Kuntzmann (France)
2016-10-15
This paper presents an alternative approach to the regularized least squares solution of ill-posed inverse problems. Instead of solving a minimization problem with an objective function composed of a data term and a regularization term, the regularization information is used to define a projection onto a convex subspace of regularized candidate solutions. The objective function is modified to include the projection of each iterate in the place of the regularization. Numerical experiments based on the problem of motion estimation for geophysical fluid images, show the improvement of the proposed method compared with regularization methods. For the presented test case, the incremental projection method uses 7 times less computation time than the regularization method, to reach the same error target. Moreover, at convergence, the incremental projection is two order of magnitude more accurate than the regularization method.
Lin, Chien-Liang
2018-01-01
This study sought to develop a self-report instrument to be used in the assessment of the project competences of college students engaged in online project-based learning. Three scales of the KIPSSE instrument developed for this study, namely, the knowledge integration, project skills, and self-efficacy scales, were based on related theories and…
Scoping study into community-based renewable energy projects
Energy Technology Data Exchange (ETDEWEB)
NONE
2000-07-01
This scoping study has been carried out by the Centre for Sustainable Energy (CSE), a charity which promotes energy efficiency and renewable energy. CSE have used their involvement in the development of the Energy Club (the first energy service company for householders in the UK) and the Bristol Environment and Energy Trust (a cross-sector organisation initiating environmental projects) as the basis of the study. This study is the first phase of a long term project to set up two small-scale renewable energy schemes to demonstrate the benefits of a community based approach. Specific objectives of the study were: to identify, quantify and cost, renewable energy resources for interested community organisations; to evaluate two routes for developing community based projects - Environment Trusts and Energy Clubs'; to organise a seminar with the objective of bringing together community interest groups with experts in renewable energy; to identify two communities with viable renewable projects for the next phase - full feasibility studies/pilot projects. (author)
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.
Construction project investment control model based on instant information
Institute of Scientific and Technical Information of China (English)
WANG Xue-tong
2006-01-01
Change of construction conditions always influences project investment by causing the loss of construction work time and extending the duration. To resolve such problem as difficult dynamic control in work construction plan, this article presents a concept of instant optimization by ways of adjustment operation time of each working procedure to minimize investment change. Based on this concept, its mathematical model is established and a strict mathematical justification is performed. An instant optimization model takes advantage of instant information in the construction process to duly complete adjustment of construction; thus we maximize cost efficiency of project investment.
Anazifa, R. D; Djukri, D
2017-01-01
The study aims at finding (1) the effect of project-based learning and problem-based learning on student's creativity and critical thinking and (2) the difference effect of project-based learning and problem-based learning on student's creativity and critical thinking. This study is quasi experiment using non-equivalent control-group design. Research population of this study was all classes in eleventh grade of mathematics and natural science program of SMA N 1 Temanggung. The participants we...
A Turn-Projected State-Based Conflict Resolution Algorithm
Butler, Ricky W.; Lewis, Timothy A.
2013-01-01
State-based conflict detection and resolution (CD&R) algorithms detect conflicts and resolve them on the basis on current state information without the use of additional intent information from aircraft flight plans. Therefore, the prediction of the trajectory of aircraft is based solely upon the position and velocity vectors of the traffic aircraft. Most CD&R algorithms project the traffic state using only the current state vectors. However, the past state vectors can be used to make a better prediction of the future trajectory of the traffic aircraft. This paper explores the idea of using past state vectors to detect traffic turns and resolve conflicts caused by these turns using a non-linear projection of the traffic state. A new algorithm based on this idea is presented and validated using a fast-time simulator developed for this study.
Project-Based Organizations and the New Spirit of Capitalism
DEFF Research Database (Denmark)
Jagd, Søren
Luc Boltanski and Eve Chiapello’s analysis of recent changes in capitalism – Le nouvel esprit du capitalisme - is an important contribution to the sociological analysis of capitalism. The aim of this paper is to present their main argument. It is argued that a particular important aspect...... of their analysis is the ideal-typical model of the new spirit of capitalism, the ideological underpinning of project-based organizations in ‘The Project World’. It is argued that the ideal-typical model of the Project World is the most elaborated description of the ‘cultural code’ of the emergent form...... of capitalist organizations. Last, it is discussed how we may obtain empirical evidence to test the hypothesis of a major change in capitalist organizational forms as well as in the recent spirit of capitalism....
Development of Entrepreneurship Competences from Project Based Learning Approaches
Directory of Open Access Journals (Sweden)
Lidia SÁNCHEZ GONZÁLEZ
2017-02-01
Full Text Available A key issue for any educational institution is to train individuals in such a way that they participate efficiently in their context. The current context where learning is carried out is digital era. With this landscape, and taking into account the current socioeconomic situation, it is necessary to increase students’ employability. This requires that what is taught in educational institutions fits with companies’ requirements. In this sense one of most common companies’ requirement is that students have knowledge about how to develop a project from scratch. In order to address this issue, the present paper poses the application of a project based learning methodology. This methodology is applied in an experiment that comprises two academic years. During each year, the students should develop complete projects working as teams and obtain final products. In this way, it was possible to develop competences that can be interesting for companies and also to increment students’ motivation.
Project-based Collaborative learning in distance education
DEFF Research Database (Denmark)
Knudsen, Morten; Bajard, Christine; Helbo, Jan
2004-01-01
) programme indicates, however, that adjustments are required in transforming the on-campus model to distance education. The main problem is that while project work is an excellent regulator of the learning process for on-campus students, this does not seem to be the case for off-campus students. Consequently......This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII......, didactic adjustments have been made based on feedback, in particular from evaluation questionnaires. This process has been very constructive in approaching the goal: a successful model for project organized learning in distance education....
Workshop : ACPSEM/ARPS competency based standards project
International Nuclear Information System (INIS)
Collins, L.
1996-01-01
The ACPSEM together with the Australian Radiation Protection Society has been working for nearly two years now on a competency based standards project for the professions of medical and health physicists. Competencies are being used increasingly in industry and the professions as a means of determining skill levels. For example, all the medical radiation technology streams have a CBS system in the final stages of development, and our engineering colleagues have completed theirs. Last year there was a draft document sent to all members asking for feedback. Following a vote of funding by both bodies, a project offect (Dr David Waggett) has been appointed, and has produced a very much improved set of competency standards covering all significant subspecialties in our profession. This workshop will detail the work done so far, and preview the draft document. A healthy discussion will be encouraged, as the project steering group will shortly be arranging the next steps in process. (author)
Sustainable Mining Land Use for Lignite Based Energy Projects
Dudek, Michal; Krysa, Zbigniew
2017-12-01
This research aims to discuss complex lignite based energy projects economic viability and its impact on sustainable land use with respect to project risk and uncertainty, economics, optimisation (e.g. Lerchs and Grossmann) and importance of lignite as fuel that may be expressed in situ as deposit of energy. Sensitivity analysis and simulation consist of estimated variable land acquisition costs, geostatistics, 3D deposit block modelling, electricity price considered as project product price, power station efficiency and power station lignite processing unit cost, CO2 allowance costs, mining unit cost and also lignite availability treated as lignite reserves kriging estimation error. Investigated parameters have nonlinear influence on results so that economically viable amount of lignite in optimal pit varies having also nonlinear impact on land area required for mining operation.
Project-based learning in geomatics at Aalborg University
DEFF Research Database (Denmark)
Höhle, Joachim
2005-01-01
The paper deals with the education in Geomatics at Aalborg University (AAU), Denmark. Since its foundation in 1974 AAU has used Project-Based Learning (PBL) as its educational model. In each of the 10 semesters a project has to be carried out by a group of students. The paper presents the ideas...... behind PBL and the use of this approach in Geomatics. Some examples of project work in the field of photogrammetry and remote sensing are given. Teachers and researchers at AAU recently published a book on the Aalborg PBL model, in which progress, diversity and challenges of the approach are documented....... Some of the findings in this investigation are presented. The on-campus education in Geomatics uses the Internet, and the paper informs about its application on campus. PBL is also practised at AAU's distance education programmes, which combine e-learning and weekend seminars on the campus. Experiences...
PROJECT-BASED LEARNING IN ENGLISH FOR MEDICINE
Directory of Open Access Journals (Sweden)
Zorica Antić
2012-06-01
Full Text Available Project-based learning facilitates hands-on learning in student-driven investigations, resulting in high-quality, challenging activities. Students participate actively in projects that revolve around their interests, questions or needs. PBL also develops the 21st century skills including critical thinking, collaboration and communication. The essence of PBL is problem-solving, a key critical thinking skill. Since problem-solving is an integral part of medicine, projects represent a significant method of instruction in English for Medical Purposes. Depending on their individual interests and abilities, each student contributes to the whole group work and the final outcome. The approach also requires students to work in teams and to communicate their findings. Using real-life problems to motivate students, challenging them to think deeply about meaningful content, and enabling them to work collaboratively are practices that yield benefits for all students and their future careers.
In-service teachers' perceptions of project-based learning.
Habók, Anita; Nagy, Judit
2016-01-01
The study analyses teachers' perceptions of methods, teacher roles, success and evaluation in PBL and traditional classroom instruction. The analysis is based on empirical data collected in primary schools and vocational secondary schools. An analysis of 109 questionnaires revealed numerous differences based on degree of experience and type of school. In general, project-based methods were preferred among teachers, who mostly perceived themselves as facilitators and considered motivation and transmission of values central to their work. Teachers appeared not to capitalize on the use of ICT tools or emotions. Students actively participated in the evaluation process via oral evaluation.
Sustainable assessment of learning experiences based on projects
Directory of Open Access Journals (Sweden)
Ignacio TRAVERSO RIBÓN
2016-05-01
Full Text Available In a project-based learning experience, the detailed monitoring of the activities in which team members participate can be useful to evaluate their work. Using learning-oriented assessment procedures, supervisors can assess the teamwork abilities with a formative purpose. Evaluation strategies such as self-assessment, peer assessment and co-assessment are often used to make evaluation formative and sustainable. Conducting an assessment strategy is not easy for team members, since they need before to have a reasonable understanding of the evaluation process and criteria. This paper describes a learning-oriented evaluation methodology and an open data framework that can be applied to collaborative project settings. An evaluation rubric and a series of indicators that provide evidences about the developed skills have been elaborated and applied in a small-scale project-based course. Projects were managed and developed with the help of an open source software forge that contains a ticketing tool for planning and tracking of tasks, a version control repository to save the software outcomes, and using a wiki to host text deliverables. The experience provides evidences in favor of using the assessment method and open data framework to make teamwork evaluation more sustainable.
Condition based maintenance pilot projects at Pickering ND
International Nuclear Information System (INIS)
Zemdegs, R.T.
1995-01-01
Ontario Hydro has recognized that the approaches to maintenance have undergone significant changes to the past decades. The traditional break down maintenance approach has been replaced by preventative maintenance and more recently, by condition based maintenance. The nuclear plants of Ontario Hydro have evaluated on a number of alternative programs to improve their maintenance effectiveness and to reduce costs, including Reliability Centred Maintenance (RCM), call-up review, component-based PM programs, analysis of failure history and so on. Pickering ND (nuclear division) and Ontario Hydro's Nuclear Technologies Services Division, have embarked on a Condition Based Maintenance pilot project to address the above issues as a breakthrough solution for smarter maintenance. The Condition Based Maintenance pilot project will demonstrate an end-to-end process utilizing a Reliability Centred Maintenance structured approach to re-engineer and redefine the existing maintenance programs. The project emphasizes on-condition maintenance where justified, and utilizes an information management tool to provide the required records keeping and analysis infrastructure. This paper briefly describes the planned maintenance model at Pickering ND used to guide the CBM pilot, and an overview of the methodology used to develop on-condition equipment indicators as part of a re-engineered maintenance plan
PROJECT BASED LEARNING BERMUATAN ETNOMATEMATIKA DALAM PEMBELAJAR MATEMATIKA
Directory of Open Access Journals (Sweden)
I Wayan Eka Mahendra
2017-03-01
Full Text Available This study aims to determine differences simultaneously in motivation and mathematics learning outcomes between students taking project based learningmodel charged ethnomathematics and students who followed the conventional learning modelon the class VIII SMP Negeri 3 Abiansemalyear 2016/2017. It was a quasi experiment with a sample of 71 student obtain by using simple random sampling. The data were analyzed by one-way multivariate analysis (Manova.The results of this study indicate that there are differences in simultaneously in learning motivation and learning outcomes between students taking mathematics model project based learning charged ethnomathematics and students who followed the conventional learning model on the class VIII SMP Negeri 3 Abiansemal year 2016/2017. Besed on the research findings, junior high school teachers are suggested to improve their student learning outcome for mathematics. Teachers also need to use a learning models accurately and correctly.
Learning based in projects to promote interdisciplinarity in Secondary School
Directory of Open Access Journals (Sweden)
Daniela Boff
2016-02-01
Full Text Available The project-based learning is an active learning strategy that helps break the paradigm of traditional teaching methods. The student is involved in the learning proposal that includes the PiBL, on which one is not passive and becomes the main actor in one's own teaching learning process. Within this learning strategy, the teacher becomes a mediator between theory and practice thus each different subject interact with one another in order to develop a topic that is mutual to all areas because the learning environment is naturally interdisciplinary. The idea of this kind of learning strategy was applied during a workshop that took place with primary and secondary schoolteachers in order to help them approach the strategy in the classroom, contributing with experiences and ideas towards the interdisciplinary based project.
A Conceptualization of Knowledge Governance in Project-based Organizations
DEFF Research Database (Denmark)
Pemsel, Sofia; Wiewiorac, Anna; Müller, Ralf
2014-01-01
This paper conceptualizes and defines knowledge governance (KG) in project-based organizations (PBOs). Two key contributions towards a multi-faceted view of KG and an understanding of KG in PBOs are advanced, as distinguished from knowledge management and organizational learning concepts. The con......This paper conceptualizes and defines knowledge governance (KG) in project-based organizations (PBOs). Two key contributions towards a multi-faceted view of KG and an understanding of KG in PBOs are advanced, as distinguished from knowledge management and organizational learning concepts....... The conceptual framework addresses macro- and micro-level elements of KG and their interaction. Our definition of KG in PBOs highlights the contingent nature of KG processes in relation to their organizational context. These contributions provide a novel platform for understanding KG in PBOs....
Krylov Subspace Methods for Saddle Point Problems with Indefinite Preconditioning
Czech Academy of Sciences Publication Activity Database
Rozložník, Miroslav; Simoncini, V.
2002-01-01
Roč. 24, č. 2 (2002), s. 368-391 ISSN 0895-4798 R&D Projects: GA ČR GA101/00/1035; GA ČR GA201/00/0080 Institutional research plan: AV0Z1030915 Keywords : saddle point problems * preconditioning * indefinite linear systems * finite precision arithmetic * conjugate gradients Subject RIV: BA - General Mathematics Impact factor: 0.753, year: 2002
Nonseparable closed vector subspaces of separable topological vector spaces
Czech Academy of Sciences Publication Activity Database
Kąkol, Jerzy; Leiderman, A. G.; Morris, S. A.
2017-01-01
Roč. 182, č. 1 (2017), s. 39-47 ISSN 0026-9255 R&D Projects: GA ČR GF16-34860L Institutional support: RVO:67985840 Keywords : locally convex topological vector space * separable topological space Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.716, year: 2016 https://link.springer.com/article/10.1007%2Fs00605-016-0876-2
Project management for complex ground-based instruments: MEGARA plan
García-Vargas, María. Luisa; Pérez-Calpena, Ana; Gil de Paz, Armando; Gallego, Jesús; Carrasco, Esperanza; Cedazo, Raquel; Iglesias, Jorge
2014-08-01
The project management of complex instruments for ground-based large telescopes is a challenge itself. A good management is a clue for project success in terms of performance, schedule and budget. Being on time has become a strict requirement for two reasons: to assure the arrival at the telescope due to the pressure on demanding new instrumentation for this first world-class telescopes and to not fall in over-costs. The budget and cash-flow is not always the expected one and has to be properly handled from different administrative departments at the funding centers worldwide distributed. The complexity of the organizations, the technological and scientific return to the Consortium partners and the participation in the project of all kind of professional centers working in astronomical instrumentation: universities, research centers, small and large private companies, workshops and providers, etc. make the project management strategy, and the tools and procedures tuned to the project needs, crucial for success. MEGARA (Multi-Espectrógrafo en GTC de Alta Resolución para Astronomía) is a facility instrument of the 10.4m GTC (La Palma, Spain) working at optical wavelengths that provides both Integral-Field Unit (IFU) and Multi-Object Spectrograph (MOS) capabilities at resolutions in the range R=6,000-20,000. The project is an initiative led by Universidad Complutense de Madrid (Spain) in collaboration with INAOE (Mexico), IAA-CSIC (Spain) and Universidad Politécnica de Madrid (Spain). MEGARA is being developed under contract with GRANTECAN.
Incentive Model Based on Cooperative Relationship in Sustainable Construction Projects
Directory of Open Access Journals (Sweden)
Guangdong Wu
2017-07-01
Full Text Available Considering the cooperative relationship between owners and contractors in sustainable construction projects, as well as the synergistic effects created by cooperative behaviors, a cooperative incentive model was developed using game theory. The model was formulated and analyzed under both non-moral hazard and moral hazard situations. Then, a numerical simulation and example were proposed to verify the conclusions derived from the model. The results showed that the synergistic effect increases the input intensity of one party’s resource transfer into the increase of marginal utility of the other party, thus the owner and contractor are willing to enhance their levels of effort. One party’s optimal benefit allocation coefficient is positively affected by its own output efficiency, and negatively affected by the other party’s output efficiency. The effort level and expected benefits of the owner and contractor can be improved by enhancing the cooperative relationship between the two parties, as well as enhancing the net benefits of a sustainable construction project. The synergistic effect cannot lower the negative effect of moral hazard behaviors during the implementation of sustainable construction projects. Conversely, the higher levels of the cooperative relationship, the wider the gaps amongst the optimal values under both non-moral hazard and moral hazard situations for the levels of effort, expected benefits and net project benefits. Since few studies to date have emphasized the effects of cooperative relationship on sustainable construction projects, this study constructed a game-based incentive model to bridge the gaps. This study contributes significant theoretical and practical insights into the management of cooperation amongst stakeholders, and into the enhancement of the overall benefits of sustainable construction projects.
Project-based faculty development for e-learning.
Vyas, Rashmi; Faith, Minnie; Selvakumar, Dhayakani; Pulimood, Anna; Lee, Mary
2016-12-01
The Christian Medical College, Vellore, in collaboration with Tufts University, Boston, conducted an advanced workshop in e-learning for medical faculty members in India. E-learning can enhance educational reforms for today's computer-literate generation, and keep faculty members up to speed in a rapidly changing world. The purpose of this paper is to report on the design and evaluation of a project-based faculty member development programme focused on developing faculty members as educators and as peer trainers who can use e-learning for educational reforms. During a 2-day workshop, 29 participants in groups of two or three developed 13 e-learning projects for implementation in their institutions. Evaluation of the workshop was through written feedback from the participants at the end of the workshop and by telephone interview with one participant from each project group at the end of one year. Content analysis of qualitative data was perfomed. The participants reported that they were motivated to implement e-learning projects and recognised the need for and usefulness of e-learning. The majority of projects (10 out of 13) that were implemented 'to some extent' or 'to a great extent' faced challenges with a lack of resources and administrative support, but faculty members were able to overcome them. E-learning can enhance educational reforms for today's computer-literate generation IMPLICATIONS: Designing feasible e-learning projects in small groups and obtaining hands-on experience with e-learning tools enhance the effectiveness of subsequent implementation. To successfully incorporate e-learning when designing educational reforms, faculty member training, continuing support and infrastructure facilities are essential. © 2016 John Wiley & Sons Ltd.
Project B-610 instrument data base. Revision 6
International Nuclear Information System (INIS)
Silvan, G.R.
1994-01-01
The following technical information document contains the published data base listing for the instrumentation to be connected into the new MICON distributed control computer. Project B-610 PFP Instrument Upgrade (PFP HVAC Control Room Upgrade) will install the MICON system in PFP and provide for a new control room at the present site of the new SWP change room EDT-150750 1/11/93
Didactics, Technology, and Organisation of Project Based Distance Education
DEFF Research Database (Denmark)
Knudsen, Morten Haack; Borch, Ole M.; Helbo, Jan
2005-01-01
The didactics, technology, and organization of an ICT supported distance engineering Master education are described. A systematic monitoring and evaluation of the basis year has given useful experience, subsequently used for adjustments and improvements. A successful on-campus project organized...... as asynchronous, which is possible with extensive utilization of new information and communication technology. Virtual meetings are conducted with text, sound and video based communication. Also the organization requires technology. A new learning management system, specifically designed to the didactic form...
Understanding Project Based Production through Socio-technical Modularity
DEFF Research Database (Denmark)
Thuesen, Christian Langhoff
This paper develops an approach for understanding Project Based Production. This form of production is characterized by unique deliverables, high complexity, high value, high risk, profound uncertainty and many stakeholders and is increasingly important the postmodern society. Common to the pract......This paper develops an approach for understanding Project Based Production. This form of production is characterized by unique deliverables, high complexity, high value, high risk, profound uncertainty and many stakeholders and is increasingly important the postmodern society. Common...... to the practices of PBP and other production practices is the goal of balancing the dilemma between creativity and productivity. In response to industrialized production, the concept of modularity gained popularity for addressing this dilemma by exploring product, process and organization structures. However...... with the starting point in system theory and a strong bias towards industrial production, the predominant understanding of modularity faces difficulty in explaining practices of Project Based Production in both social – technical and dynamic – stable aspects. Illustrated by a case the paper addresses this gap...
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.
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
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)
GPU-based Branchless Distance-Driven Projection and Backprojection.
Liu, Rui; Fu, Lin; De Man, Bruno; Yu, Hengyong
2017-12-01
Projection and backprojection operations are essential in a variety of image reconstruction and physical correction algorithms in CT. The distance-driven (DD) projection and backprojection are widely used for their highly sequential memory access pattern and low arithmetic cost. However, a typical DD implementation has an inner loop that adjusts the calculation depending on the relative position between voxel and detector cell boundaries. The irregularity of the branch behavior makes it inefficient to be implemented on massively parallel computing devices such as graphics processing units (GPUs). Such irregular branch behaviors can be eliminated by factorizing the DD operation as three branchless steps: integration, linear interpolation, and differentiation, all of which are highly amenable to massive vectorization. In this paper, we implement and evaluate a highly parallel branchless DD algorithm for 3D cone beam CT. The algorithm utilizes the texture memory and hardware interpolation on GPUs to achieve fast computational speed. The developed branchless DD algorithm achieved 137-fold speedup for forward projection and 188-fold speedup for backprojection relative to a single-thread CPU implementation. Compared with a state-of-the-art 32-thread CPU implementation, the proposed branchless DD achieved 8-fold acceleration for forward projection and 10-fold acceleration for backprojection. GPU based branchless DD method was evaluated by iterative reconstruction algorithms with both simulation and real datasets. It obtained visually identical images as the CPU reference algorithm.
Ceramic Technology Project data base: September 1992 summary report
Energy Technology Data Exchange (ETDEWEB)
Keyes, B.L.P.
1993-06-01
Data presented in this report represent an intense effort to improve processing methods, testing methods, and general mechanical properties (rupture modulus, tensile, creep, stress-rupture, dynamic and cyclic fatigue, fracture toughness) of candidate ceramics for use in advanced heat engines. This work was performed by many facilities and represents only a small part of the data generated by the Ceramic Technology Project (CTP) since 1986. Materials discussed include GTE PY6, GN-10, NT-154, NT-164, SN-260, SN-251, SN-252, AY6, silicon nitride combined with rare-earth oxides, Y-TZP, ZTA, NC-433, NT-230, Hexoloy SA, MgO-PSZ-to-MgO-PSZ joints, MgO-PSZ-to-cast iron, and a few whisker/fiber-reinforced ceramics. Information in this report was taken from the project`s semiannual and bimonthly progress reports and from final reports summarizing the results of individual studies. Test results are presented in tabular form and in graphs. All data, including test rig descriptions and material characterizations, are stored in the CTP data base and are available to all project participants on request. The objective of this report is to make available the test results from these studies but not to draw conclusions from those data.
Project-Based Collaborative Learning in Distance Education
DEFF Research Database (Denmark)
Knudsen, Morten; Bajard, C.; Helbo, Jan
2003-01-01
This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII)......, didactic adjustments have been made based on feedback, in particular from evaluation questionnaires. This process has been very constructive in approaching the goal: a successful model for project organized learning in distance education.......) programme indicates, however, that adjustments are required in transforming the on-campus model to distance education. The main problem is that while project work is an excellent regulator of the learning process for on-campus students, this does not seem to be the case for off-campus students. Consequently......This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII...
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.
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
Krylov Subspace Methods for Complex Non-Hermitian Linear Systems. Thesis
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.
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)
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.
Management aspects of Gemini's base facility operations project
Arriagada, Gustavo; Nitta, Atsuko; Adamson, A. J.; Nunez, Arturo; Serio, Andrew; Cordova, Martin
2016-08-01
Gemini's Base Facilities Operations (BFO) Project provided the capabilities to perform routine nighttime operations without anyone on the summit. The expected benefits were to achieve money savings and to become an enabler of the future development of remote operations. The project was executed using a tailored version of Prince2 project management methodology. It was schedule driven and managing it demanded flexibility and creativity to produce what was needed, taking into consideration all the constraints present at the time: Time available to implement BFO at Gemini North (GN), two years. The project had to be done in a matrix resources environment. There were only three resources assigned exclusively to BFO. The implementation of new capabilities had to be done without disrupting operations. And we needed to succeed, introducing the new operational model that implied Telescope and instrumentation Operators (Science Operations Specialists - SOS) relying on technology to assess summit conditions. To meet schedule we created a large number of concurrent smaller projects called Work Packages (WP). To be reassured that we would successfully implement BFO, we initially spent a good portion of time and effort, collecting and learning about user's needs. This was done through close interaction with SOSs, Observers, Engineers and Technicians. Once we had a clear understanding of the requirements, we took the approach of implementing the "bare minimum" necessary technology that would meet them and that would be maintainable in the long term. Another key element was the introduction of the "gradual descent" concept. In this, we increasingly provided tools to the SOSs and Observers to prevent them from going outside the control room during nighttime operations, giving them the opportunity of familiarizing themselves with the new tools over a time span of several months. Also, by using these tools at an early stage, Engineers and Technicians had more time for debugging
A Web-Based Monitoring System for Multidisciplinary Design Projects
Rogers, James L.; Salas, Andrea O.; Weston, Robert P.
1998-01-01
In today's competitive environment, both industry and government agencies are under pressure to reduce the time and cost of multidisciplinary design projects. New tools have been introduced to assist in this process by facilitating the integration of and communication among diverse disciplinary codes. One such tool, a framework for multidisciplinary computational environments, is defined as a hardware and software architecture that enables integration, execution, and communication among diverse disciplinary processes. An examination of current frameworks reveals weaknesses in various areas, such as sequencing, displaying, monitoring, and controlling the design process. The objective of this research is to explore how Web technology, integrated with an existing framework, can improve these areas of weakness. This paper describes a Web-based system that optimizes and controls the execution sequence of design processes; and monitors the project status and results. The three-stage evolution of the system with increasingly complex problems demonstrates the feasibility of this approach.
Projection model for flame chemiluminescence tomography based on lens imaging
Wan, Minggang; Zhuang, Jihui
2018-04-01
For flame chemiluminescence tomography (FCT) based on lens imaging, the projection model is essential because it formulates the mathematical relation between the flame projections captured by cameras and the chemiluminescence field, and, through this relation, the field is reconstructed. This work proposed the blurry-spot (BS) model, which takes more universal assumptions and has higher accuracy than the widely applied line-of-sight model. By combining the geometrical camera model and the thin-lens equation, the BS model takes into account perspective effect of the camera lens; by combining ray-tracing technique and Monte Carlo simulation, it also considers inhomogeneous distribution of captured radiance on the image plane. Performance of these two models in FCT was numerically compared, and results showed that using the BS model could lead to better reconstruction quality in wider application ranges.
Rapid matching of stereo vision based on fringe projection profilometry
Zhang, Ruihua; Xiao, Yi; Cao, Jian; Guo, Hongwei
2016-09-01
As the most important core part of stereo vision, there are still many problems to solve in stereo matching technology. For smooth surfaces on which feature points are not easy to extract, this paper adds a projector into stereo vision measurement system based on fringe projection techniques, according to the corresponding point phases which extracted from the left and right camera images are the same, to realize rapid matching of stereo vision. And the mathematical model of measurement system is established and the three-dimensional (3D) surface of the measured object is reconstructed. This measurement method can not only broaden application fields of optical 3D measurement technology, and enrich knowledge achievements in the field of optical 3D measurement, but also provide potential possibility for the commercialized measurement system in practical projects, which has very important scientific research significance and economic value.
Problem based Learning versus Design Thinking in Team based Project work
DEFF Research Database (Denmark)
Denise J. Stokholm, Marianne
2014-01-01
project based learning issues, which has caused a need to describe and compare the two models; in specific the understandings, approaches and organization of learning in project work. The PBL model viewing the process as 3 separate project stages including; problem analysis, problem solving and project......All educations at Aalborg University has since 1974 been rooted in Problem Based Learning (PBL). In 1999 a new education in Industrial design was set up, introducing Design Based Learning (DBL). Even though the two approaches have a lot in common they also hold different understandings of core...... report, with focus on problem solving through analysis. Design Based Learning viewing the process as series of integrated design spaces including; alignment, research, mission, vision, concept, product and process report, with focus on innovative ideation though integration. There is a need of renewing...
International Nuclear Information System (INIS)
Zhu Ning; Jiang Yong; Kato, Seizo
2005-01-01
This study uses ultrasound in combination with tomography to obtain three-dimensional temperature measurements using projection data obtained from limited projection angle. The main feature of the new computerized tomography (CT) reconstruction algorithm is to employ extrapolation scheme to make up for the incomplete projection data, it is based on the conventional filtered back projection (FBP) method while on top of that taking into account the correlation between the projection data and Fourier transform-based extrapolation. Computer simulation is conducted to verify the above algorithm. An experimental 3D temperature distribution measurement is also carried out to validate the proposed algorithm. The simulation and experimental results demonstrate that the extrapolated FBP CT algorithm is highly effective in dealing with projection data from limited projection angle
Oerlemans, L.A.G.; Pretorius, M.W.
2014-01-01
As organisations in more and more industries look for flexible ways of production in the wake of rapidly changing market environments, project-based organising is becoming an increasingly important mode of organisation (Eisenhardt & Tabrizi, 1995). Whereas project-based organisation was
Problem and Project Based Learning in Hybrid Spaces
DEFF Research Database (Denmark)
Ryberg, Thomas; Davidsen, Jacob; Hodgson, Vivien
2016-01-01
There is a need within networked learning to understand and conceptualise the interplay between digital and physical spaces or what we could term hybrid spaces. Therefore, we discuss a recent study of students from two different programmes who are engaged in long-term, group-based problem...... and project based learning. Based on interviews, workshops and observations of students’ actual group practices in open, shared and flexible spaces in Aalborg University (AAU), we identify and discuss how students incorporate networked and digital technologies into their group work and into the study places...... they create for themselves. We describe how in one of the programmes ‘nomadic’ groups of students used different technologies and spaces for ‘placemaking’. We then show how their experience and approach to collaborative work differs to that of the more static or ‘artisan’ groups of students in the other...
Problems and Projects Based Approach For Analog Electronic Circuits' Course
Directory of Open Access Journals (Sweden)
Vahé Nerguizian
2009-04-01
Full Text Available New educational methods and approaches are recently introduced and implemented at several North American and European universities using Problems and Projects Based Approach (PPBA. The PPBA employs a teaching technique based mostly on competences/skills rather than only on knowledge. This method has been implemented and proven by several pedagogical instructors and authors at several educational institutions. This approach is used at different disciplines such as medicine, biology, engineering and many others. It has the advantage to improve the student's skills and the knowledge retention rate, and reflects the 21st century industrial/company needs and demands. Before implementing this approach to a course, a good resources preparation and planning is needed upfront by the responsible or instructor of the course to achieve the course and students related objectives. This paper presents the preparation, the generated documentation and the implementation of a pilot project utilizing PPBA education for a second year undergraduate electronic course over a complete semester, and for two different class groups (morning and evening groups. The outcome of this project (achieved goals, observed difficulties and lessons learned is presented based on different tools such as students 'in class' communication and feedback, different course evaluation forms and the professor/instructor feedback. Resources, challenges, difficulties and recommendations are also assessed and presented. The impact, the effect and the results (during and at the end of the academic fall session of the PPBA on students and instructor are discussed, validated, managed and communicated to help other instructor in taking appropriate approach decisions with respect to this new educational approach compared to the classical one.
Software for project-based learning of robot motion planning
Moll, Mark; Bordeaux, Janice; Kavraki, Lydia E.
2013-12-01
Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can be explained in a simplified two-dimensional setting, but this masks many of the subtleties and complexities of the underlying problem. We have developed software for project-based learning of motion planning that enables deep learning. The projects that we have developed allow advanced undergraduate students and graduate students to reflect on the performance of existing textbook algorithms and their own variations on such algorithms. Formative assessment has been conducted at three institutions. The core of the software used for this teaching module is also used within the Robot Operating System, a widely adopted platform by the robotics research community. This allows for transfer of knowledge and skills to robotics research projects involving a large variety robot hardware platforms.
Knowledge Based Product Configuration - a documentatio tool for configuration projects
DEFF Research Database (Denmark)
Hvam, Lars; Malis, Martin
2003-01-01
. A lot of knowledge isput into these systems and many domain experts are involved. This calls for an effective documentation system in order to structure this knowledge in a way that fits to the systems. Standard configuration systems do not support this kind of documentation. The chapter deals...... with the development of a Lotus Notes application that serves as a knowledge based documentation tool for configuration projects. A prototype has been developed and tested empirically in an industrial case-company. It has proved to be a succes....
Sourcing Team Behavior in Project-Based MNE's
DEFF Research Database (Denmark)
Hansen, Anders Peder Lysholm
2014-01-01
across the three cases was characterized by conflict between departments represented in the category teams. This resulted in unfortunate sourcing team behaviour and unaligned performance management, which in turn had a number of adverse effects. Further research on how to create a holistic and balanced......This paper presents and discusses a multiple case study of three cross-functional category teams responsible for sourcing critical components within multi-national, project-based enterprises. The study focused on behaviour and management of the sourcing teams and found that the sourcing process...... team perspective in the sourcing teams is suggested....
Knowledge Governance Strategies in Project-based Organizations
DEFF Research Database (Denmark)
Pemsel, Sofia; Müller, Ralf; Söderlund, Jonas
2016-01-01
Knowledge governance (KG) aims at strategically influencing knowledge processes by implementing governance mechanisms. Little is known about whether, how, or why such strategies differ among firms. We utilize a large-scale empirical study of 20 organizations to develop a typology of KG strategies...... in project-based organizations; we then explore how these strategies emerge and affect organizational knowledge processes. Six strategies are identified: Protector, Deliverer, Polisher, Explorer, Supporter, and Analyzer. This paper posits a multi-level categorization model to facilitate comparisons among KG...... strategies. We uncover three main drivers of organizations' chosen knowledge governance strategies-namely, attitudes about humans, knowledge, and knowledge control....
A time projection chamber with GEM-based readout
Energy Technology Data Exchange (ETDEWEB)
Attié, David [CEA Saclay, IRFU, F-91191 Gif-sur-Yvette (France); Behnke, Ties [Deutsches Elektronen-Synchrotron DESY, A Research Centre of the Helmholtz Association, Notkestrasse 85, 22607 Hamburg (Hamburg site) (Germany); Bellerive, Alain [Carleton University, Department of Physics, 1125 Colonel By Drive, Ottawa, ON, Canada K1S 5B6 (Canada); Bezshyyko, Oleg [Taras Shevchenko National University of Kyiv, 64/13, Volodymyrska Street, City of Kyiv 01601 (Ukraine); Bhattacharya, Deb Sankar [CEA Saclay, IRFU, F-91191 Gif-sur-Yvette (France); now at Saha Institute of Nuclear Physics, 1/AF, Sector 1, Bidhan Nagar, Kolkata 700064 (India); Bhattacharya, Purba [Saha Institute of Nuclear Physics, 1/AF, Sector 1, Bidhan Nagar, Kolkata 700064 (India); now at National Institute of Science Education and Research (NISER) Bhubaneswar, P.O. Jatni, Khurda 752050, Odisha (India); Bhattacharya, Sudeb [Saha Institute of Nuclear Physics, 1/AF, Sector 1, Bidhan Nagar, Kolkata 700064 (India); Caiazza, Stefano [Deutsches Elektronen-Synchrotron DESY, A Research Centre of the Helmholtz Association, Notkestrasse 85, 22607 Hamburg (Hamburg site) (Germany); now at Johannes Gutenberg Universität Mainz, Institut für Physik, 55099 Mainz (Germany); Colas, Paul [CEA Saclay, IRFU, F-91191 Gif-sur-Yvette (France); Lentdecker, Gilles De [Inter University ULB-VUB, Av. Fr. Roosevelt 50, B1050 Bruxelles (Belgium); Dehmelt, Klaus [Deutsches Elektronen-Synchrotron DESY, A Research Centre of the Helmholtz Association, Notkestrasse 85, 22607 Hamburg (Hamburg site) (Germany); now at State University of New York at Stony Brook, Department of Physics and Astronomy, Stony Brook, NY 11794-3800 (United States); Desch, Klaus [Universität Bonn, Physikalisches Institut, Nußallee 12, 53115 Bonn (Germany); and others
2017-06-01
For the International Large Detector concept at the planned International Linear Collider, the use of time projection chambers (TPC) with micro-pattern gas detector readout as the main tracking detector is investigated. In this paper, results from a prototype TPC, placed in a 1 T solenoidal field and read out with three independent Gas Electron Multiplier (GEM) based readout modules, are reported. The TPC was exposed to a 6 GeV electron beam at the DESY II synchrotron. The efficiency for reconstructing hits, the measurement of the drift velocity, the space point resolution and the control of field inhomogeneities are presented.
A school and inquiry based project with Nordic student teachers
DEFF Research Database (Denmark)
Stougaard, Birgitte
Álka is a framework involving teacher education institutions in the Nordic counties. This study describes the design of a module aiming at the establishment of a stronger nexus between research (theory) and teaching of science (practice) and to explore an issue relevant for the student teachers...... future professional life. The theme that was used for the research based project was: Nordic Children’s ideas about living things in the sea. Oral presentation at The 10th Nordic Research Symposium on Science Education (NFSUN), Linköbing, Sweden, June 2011....
Modern Policyholder Preferences and Scenario-Based Projections
DEFF Research Database (Denmark)
Jensen, Ninna Reitzel
This PhD thesis covers aspects of policyholder preferences and life insurance projections based on economic scenarios. Both topics are relevant to policyholders as well as to the life insurance and pension industry—and equally important, the topics give rise to a variety of interesting mathematical...... is important for designing competitive life insurance and savings products and for providing sound advice to policyholders. Preferences come in many shapes and forms. In this thesis, we focus on separation of risk and time preferences and preferences for smooth investment. The latter is modeled with something...
New neutron detector based on micromegas technology for ADS projects
International Nuclear Information System (INIS)
Andriamonje, Samuel; Andriamonje, Gregory; Aune, Stephan; Ban, Gilles; Breaud, Stephane; Blandin, Christophe; Ferrer, Esther; Geslot, Benoit; Giganon, Arnaud; Giomataris, Ioannis; Jammes, Christian; Kadi, Yacine; Laborie, Philippe; Lecolley, Jean Francois; Pancin, Julien; Riallot, Marc; Rosa, Roberto; Sarchiapone, Lucia; Steckmeyer, Jean Claude; Tillier, Joel
2006-01-01
A new neutron detector based on Micromegas technology has been developed for the measurement of the simulated neutron spectrum in the ADS project. After the presentation of simulated neutron spectra obtained in the interaction of 140 MeV protons with the spallation target inside the TRIGA core, a full description of the new detector configuration is given. The advantage of this detector compared to conventional neutron flux detectors and the results obtained with the first prototype at the CELINA 14 MeV neutron source facility at CEA-Cadarache are presented. The future developments of operational Piccolo-Micromegas for fast neutron reactors are also described
New neutron detector based on micromegas technology for ADS projects
Energy Technology Data Exchange (ETDEWEB)
Andriamonje, Samuel [CEA-Saclay, DSM/DAPNIA, F-91191 Gif-sur-Yvette (France)]. E-mail: sandriamonje@cea.fr; Andriamonje, Gregory [IXL-Universite Bordeaux 1-BAT. A31-351 cours de la Liberation-F-33405 Talence Cedex (France); Aune, Stephan [CEA-Saclay, DSM/DAPNIA, F-91191 Gif-sur-Yvette (France); Ban, Gilles [CNRS/IN2P3 LPC Caen, 6 Boulevard Marechal Juin, F-14050 Caen Cedex (France); Breaud, Stephane [CEA/DEN/Cadarache, 13108 Saint-Paul Lez Durance (France); Blandin, Christophe [CEA/DEN/Cadarache, 13108 Saint-Paul Lez Durance (France); Ferrer, Esther [CEA-Saclay, DSM/DAPNIA, F-91191 Gif-sur-Yvette (France); Geslot, Benoit [CEA/DEN/Cadarache, 13108 Saint-Paul Lez Durance (France); Giganon, Arnaud [CEA-Saclay, DSM/DAPNIA, F-91191 Gif-sur-Yvette (France); Giomataris, Ioannis [CEA-Saclay, DSM/DAPNIA, F-91191 Gif-sur-Yvette (France); Jammes, Christian [CEA/DEN/Cadarache, 13108 Saint-Paul Lez Durance (France); Kadi, Yacine [CERN CH 1211 Geneva (Switzerland); Laborie, Philippe [CNRS/IN2P3 LPC Caen, 6 Boulevard Marechal Juin, F-14050 Caen Cedex (France); Lecolley, Jean Francois [CNRS/IN2P3 LPC Caen, 6 Boulevard Marechal Juin, F-14050 Caen Cedex (France); Pancin, Julien [CEA-Saclay, DSM/DAPNIA, F-91191 Gif-sur-Yvette (France); Riallot, Marc [CEA-Saclay, DSM/DAPNIA, F-91191 Gif-sur-Yvette (France); Rosa, Roberto [ENEA-Casaccia, Via Anguillarese, 00060 Rome (Italy); Sarchiapone, Lucia [CERN CH 1211 Geneva (Switzerland); Steckmeyer, Jean Claude [CNRS/IN2P3 LPC Caen, 6 Boulevard Marechal Juin, F-14050 Caen Cedex (France); Tillier, Joel [CNRS/IN2P3 LPC Caen, 6 Boulevard Marechal Juin, F-14050 Caen Cedex (France)
2006-06-23
A new neutron detector based on Micromegas technology has been developed for the measurement of the simulated neutron spectrum in the ADS project. After the presentation of simulated neutron spectra obtained in the interaction of 140 MeV protons with the spallation target inside the TRIGA core, a full description of the new detector configuration is given. The advantage of this detector compared to conventional neutron flux detectors and the results obtained with the first prototype at the CELINA 14 MeV neutron source facility at CEA-Cadarache are presented. The future developments of operational Piccolo-Micromegas for fast neutron reactors are also described.
An efficient method for sampling the essential subspace of proteins
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
Data Driven Fault Tolerant Control : A Subspace Approach
Dong, J.
2009-01-01
The main stream research on fault detection and fault tolerant control has been focused on model based methods. As far as a model is concerned, changes therein due to faults have to be extracted from measured data. Generally speaking, existing approaches process measured inputs and outputs either by
Empirical projection-based basis-component decomposition method
Brendel, Bernhard; Roessl, Ewald; Schlomka, Jens-Peter; Proksa, Roland
2009-02-01
Advances in the development of semiconductor based, photon-counting x-ray detectors stimulate research in the domain of energy-resolving pre-clinical and clinical computed tomography (CT). For counting detectors acquiring x-ray attenuation in at least three different energy windows, an extended basis component decomposition can be performed in which in addition to the conventional approach of Alvarez and Macovski a third basis component is introduced, e.g., a gadolinium based CT contrast material. After the decomposition of the measured projection data into the basis component projections, conventional filtered-backprojection reconstruction is performed to obtain the basis-component images. In recent work, this basis component decomposition was obtained by maximizing the likelihood-function of the measurements. This procedure is time consuming and often unstable for excessively noisy data or low intrinsic energy resolution of the detector. Therefore, alternative procedures are of interest. Here, we introduce a generalization of the idea of empirical dual-energy processing published by Stenner et al. to multi-energy, photon-counting CT raw data. Instead of working in the image-domain, we use prior spectral knowledge about the acquisition system (tube spectra, bin sensitivities) to parameterize the line-integrals of the basis component decomposition directly in the projection domain. We compare this empirical approach with the maximum-likelihood (ML) approach considering image noise and image bias (artifacts) and see that only moderate noise increase is to be expected for small bias in the empirical approach. Given the drastic reduction of pre-processing time, the empirical approach is considered a viable alternative to the ML approach.
Students’ Lived Experience of Project-Based Learning
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Sandy Ferianda
2017-07-01
Full Text Available Inspired by personal experiences during the study time in the Graduate Program in English Language Studies (ELS Sanata Dharma University Yogyakarta, this research focused mainly on investigating the ELS students’ lived experience of project-based learning implemented by the ELS lecturers. This study employed hermeneutic phenomenology since it described and interpreted the meanings of ELS students lived experience. The participants of this study were the three ELS students considered to be illuminating from the three different streams batch of 2015. In this study we used one-on-one in depth interview to gain the data. The findings of this study consisted of four prefigured meanings and two emergent meanings namely a authentic learning, b learner autonomy, c cooperative learning, d multiple intelligences, e understanding others, and f personal development. The findings of this study gave implications not only to the ELS students and lecturers, but also to the audience. Lastly, recommendations were also addressed to the ELS students as their habit formation, to the ELS lecturers as their inputs to give more feedbacks to their students, and to the future researchers. Keywords: Lived experience, project-based learning.
Nurse Educator Pathway Project: a competency-based intersectoral curriculum.
Young, Lynne; Frost, Linda J; Bigl, Julie; Clauson, Marion; McRae, Cora; Scarborough, Kathy S; Murphy, Sue; Jillings, Carol; Gillespie, Frank
2010-01-01
In this paper, we begin by providing an overview of the Educator Pathway Project (EPP), an education infrastructure that was developed in response to emerging critical nursing workplace issues, and the related demand for enhanced workplace education. We then describe the EPP competency-based curriculum designed to prepare nurses as preceptors, mentors, and educators to lead learning with diverse learner groups. This competency-based curriculum was developed through a collaboration of nurse leaders across practice, academic, and union sectors and drew from a widely embraced curriculum development model (Iwasiw, Goldenberg, & Andrusyzyn, 2005). The goal of the curriculum was to prepare nurses through a four-level career pathway model that contextualized practice and education theory to various education-related roles and levels of experience within the practice setting. Over 1,100 nurses participated in this innovative intersectoral nursing initiative.
A MUSIC-Based Algorithm for Blind User Identification in Multiuser DS-CDMA
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M. Reza Soleymani
2005-04-01
Full Text Available A blind scheme based on multiple-signal classification (MUSIC algorithm for user identification in a synchronous multiuser code-division multiple-access (CDMA system is suggested. The scheme is blind in the sense that it does not require prior knowledge of the spreading codes. Spreading codes and users' power are acquired by the scheme. Eigenvalue decomposition (EVD is performed on the received signal, and then all the valid possible signature sequences are projected onto the subspaces. However, as a result of this process, some false solutions are also produced and the ambiguity seems unresolvable. Our approach is to apply a transformation derived from the results of the subspace decomposition on the received signal and then to inspect their statistics. It is shown that the second-order statistics of the transformed signal provides a reliable means for removing the false solutions.
Thorium-based Molten Salt Reactor (TMSR) project in China
International Nuclear Information System (INIS)
Dai, Zhimin; Liu, Wei
2013-01-01
Making great efforts in development of nuclear energy is one of the long-term-plan in China's energy strategies. The advantages of Thorium-based nuclear energy are: rich resource in nature, less nuclear waste, low toxicity, nuclear non-proliferation and so on. Furthermore, China is a country with abundant thorium, thus it is necessary to develop the Thorium-based Molten Salt Reactor (TMSR) in China. Shanghai Institute of Applied Physics, Chinese Academy of Sciences (SINAP) had designed and constructed the first China's light-water reactor and developed a zero-power thorium-based molten salt reactor successfully in the early 1970s. The applied research project 'thorium molten salt reactor nuclear power system' by SINAP together with several other institutes had been accepted and granted by China government in 2011. The whole project has been divided into three stages: Firstly, built a 2 MW-zero-power high temperature solid molten salt reactor in 2015 and a 2 MW-zero-power high temperature liquid molten salt reactor in 2017. Secondly, in 2020 built a 10 MW high temperature liquid molten salt reactor. Thirdly, on the base of previous work, a 100 MW high temperature molten salt reactor should be achieving in 2030. After more than one years of efforts, a high quality scientific research team has been formed, which is able to design the molten salt reactor, the molten salt loop and related key equipment, the systems of molten salt preparation, purification and the radioactive gas removal. In the past one year, the initial physical design of high temperature molten salt reactor has been completed; the nuclear chemistry and radiation chemical laboratory has been built, a high temperature salt (HTS) loop and radioactive gas removal experiment device system have been successfully developed and constructed. Further, the preliminary study on reactor used carbon-carbon composite material has been investigated. (author)
Performance Criteria of Spatial Development Projects Based on Interregional Integration
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Elena Viktorovna Kurushina
2018-03-01
Full Text Available The search of efficient ways for the development of regional socio-economic space is a relevant problem. The authors consider the models of spatial organization according to the Spatial Development Strategy of the Russian Federation until 2030. We conduct the comparative analysis of scenarios for the polarized and diversified spatial growth. Many investigations consider the concepts of polarized and endogenous growth. This study proposes a methodology to assess the development of macroregions and to increase the viability of interregional integration projects. To develop this methodology, we formulate scientific principles and indirect criteria of the project performance conforming to the theory of regional integration. In addition to the territorial community and complementarity of the development potentials, regional integration in the country should be based on the principles of security, networking, limited quantity and awareness of the potential project participants. Integration should ensure synergetic effects and take into account cultural and historical closeness, that manifests in the common mentality and existing economic relations among regions. The calculation results regarding the indirect criteria are obtained using the methods of classification and spatial correlation. This study confirms the hypothesis, that the formation of the Western Siberian and Ural macro-regions is appropriate. We have concluded this on the basis of the criteria of economic development, economic integration, the similarity of regional spaces as habitats, and a number of participants for the subjects of the Ural Federal District. The projection of the patterns of international economic integration to the interregional level allows predicting the highest probability for the successful cooperation among the Western Siberian regions with a high level of economic development. The authors’ method has revealed a high synchronization between the economies of
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Ratna Malawati
2016-06-01
Full Text Available This study aims to improve the physics Science Process Skills Students on cognitive and psychomotor aspects by using model based Project Based Learning training.The object of this study is the Project Based Learning model used in the learning process of Computationa Physics.The method used is classroom action research through two learning cycles, each cycle consisting of the stages of planning, implementation, observation and reflection. In the first cycle of treatment with their emphasis given training in the first phase up to third in the model Project Based Learning, while the second cycle is given additional treatment with emphasis discussion is collaboration in achieving the best results for each group of products. The results of data analysis showed increased ability to think Students on cognitive and Science Process Skills in the psychomotor.
Rocket to Creativity: A Field Experience in Problem-Based and Project-Based Learning
Dole, Sharon F.; Bloom, Lisa A.; Doss, Kristy Kowalske
2016-01-01
This article reports the impact of a field experience in problem-based (PBL) and project-based learning (PjBL) on in-service teachers' conceptions of experiential learning. Participants had been enrolled in a hybrid class that included an online component in which they learned about PBL and PjBL, and an experiential component in which they…
Predicting Software Projects Cost Estimation Based on Mining Historical Data
Najadat, Hassan; Alsmadi, Izzat; Shboul, Yazan
2012-01-01
In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration software project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open source projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining techniques are used to classify software projects in terms of their development complexity. Data mining techniqu...
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...
Bayesian-network-based safety risk analysis in construction projects
International Nuclear Information System (INIS)
Zhang, Limao; Wu, Xianguo; Skibniewski, Miroslaw J.; Zhong, Jingbing; Lu, Yujie
2014-01-01
This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion” is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment. - Highlights: • A systemic Bayesian network based approach for safety risk analysis is developed. • An expert confidence indicator for probability fuzzification is proposed. • Safety risk analysis progress is extended to entire life cycle of risk-prone events. • A typical
Directory of Open Access Journals (Sweden)
Farhad Habibi
2018-09-01
Full Text Available Among different factors, correct scheduling is one of the vital elements for project management success. There are several ways to schedule projects including the Critical Path Method (CPM and Program Evaluation and Review Technique (PERT. Due to problems in estimating dura-tions of activities, these methods cannot accurately and completely model actual projects. The use of fuzzy theory is a basic way to improve scheduling and deal with such problems. Fuzzy theory approximates project scheduling models to reality by taking into account uncertainties in decision parameters and expert experience and mental models. This paper provides a step-by-step approach for accurate estimation of time and cost of projects using the Project Evaluation and Review Technique (PERT and expert views as fuzzy numbers. The proposed method included several steps. In the first step, the necessary information for project time and cost is estimated using the Critical Path Method (CPM and the Project Evaluation and Review Technique (PERT. The second step considers the duration and cost of the project activities as the trapezoidal fuzzy numbers, and then, the time and cost of the project are recalculated. The duration and cost of activities are estimated using the questionnaires as well as weighing the expert opinions, averaging and defuzzification based on a step-by-step algorithm. The calculating procedures for evaluating these methods are applied in a real project; and the obtained results are explained.
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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.
Directory of Open Access Journals (Sweden)
Leon AG Oerlemans
2014-02-01
Full Text Available As organisations in more and more industries look for flexible ways of production in the wake of rapidly changing market environments, project-based organising is becoming an increasingly important mode of organisation (Eisenhardt & Tabrizi, 1995. Whereas project-based organisation was traditionally mainly the domain of industries such as film making (Sorenson & Waguespack, 2006, theatre (Goodman & Goodman, 1976, and construction (Gann & Salter, 2000, a project-based mode of operation has recently pervaded many other sectors in the economy, including software development, advertising, biotechnology, consulting, emergency response, fashion, television and complex products and systems (Grabher, 2004; Hobday, 2000. This increasing prevalence is reflected in an exponentially growing body of research (Bakker, 2010, which has made marked progress in areas such as project-based learning (Prencipe & Tell, 2001, project-based innovation (Eisenhardt & Tabrizi, 1995 and project-based careers (Jones, 1996. As a consequence, research on project organisation has moved from being a narrow specialty domain toward being a broad research paradigm, attending to a broad audience in organisation science and beyond (Sydow et al., 2004. In a fairly recent review paper, Bakker (2010 shows that in the period 1988–2008 scholarly attention, as indicated by publications in books and ISI-indexed journals, grew exponentially (see Figure 1. Comparing the number of publications in the period 1988-1998 with the period 1998–2008, he observed an increase of almost 340%.
Buttler, T.
2016-01-01
Project-based organizations collect lessons learned in order to improve the performance of projects. They aim to repeat successes by using positive lessons learned, and to avoid repeating negative experiences by using negative lessons learned. Cooke-Davies (2002) claimed that the ability to learn
Applying Cultural Project Based Learning to Develop Students’ Academic Writing
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Lulus Irawati
2015-06-01
Full Text Available Writing is considered to be the most demanding and difficult skill for many college students, since there are some steps to be followed such as prewriting, drafting, editing, revising and publishing. The interesting topic like culture including lifestyle, costume, and custom is necessary to be offered in Academic Writing class. Accordingly, this article aims to elaborate the application of a cultural project based learning to develop students’ ability in academic writing. This descriptive qualitative research was conducted in Academic Writing class consisting of 20 students of the fourth semester. The students were divided into some groups, each consisting of 4-5 people assigned to make a cultural project within 6 weeks, in the form of essay. Each member of the groups has to create his/ her own essay and then compile the essays to be a mini-journal. Therefore, one group has one mini-journal consisting of 4-5 essays. To check the content of mini-journal, the lecturer also asked the groups to present in front of the class to get some suggestions, feedback, or comments.
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).
Integrating Industry in Project Organized Problem Based Learning for Engineering Educations
DEFF Research Database (Denmark)
Nielsen, Kirsten M.
2006-01-01
This abstract deals with the challenge of establishing engineering student projects in collaboration with industry. Based on empirical results a set of advices for industrial collaboration in project oriented problem based learning are formulated......This abstract deals with the challenge of establishing engineering student projects in collaboration with industry. Based on empirical results a set of advices for industrial collaboration in project oriented problem based learning are formulated...
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
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
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...
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...
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.
Prototype Biology-Based Radiation Risk Module Project
Terrier, Douglas; Clayton, Ronald G.; Patel, Zarana; Hu, Shaowen; Huff, Janice
2015-01-01
Biological effects of space radiation and risk mitigation are strategic knowledge gaps for the Evolvable Mars Campaign. The current epidemiology-based NASA Space Cancer Risk (NSCR) model contains large uncertainties (HAT #6.5a) due to lack of information on the radiobiology of galactic cosmic rays (GCR) and lack of human data. The use of experimental models that most accurately replicate the response of human tissues is critical for precision in risk projections. Our proposed study will compare DNA damage, histological, and cell kinetic parameters after irradiation in normal 2D human cells versus 3D tissue models, and it will use a multi-scale computational model (CHASTE) to investigate various biological processes that may contribute to carcinogenesis, including radiation-induced cellular signaling pathways. This cross-disciplinary work, with biological validation of an evolvable mathematical computational model, will help reduce uncertainties within NSCR and aid risk mitigation for radiation-induced carcinogenesis.
Remote Laboratory Java Server Based on JACOB Project
Directory of Open Access Journals (Sweden)
Pavol Bisták
2011-02-01
Full Text Available Remote laboratories play an important role in the educational process of engineers. This paper deals with the structure of remote laboratories. The principle of the proposed remote laboratory structure is based on the Java server application that communicates with Matlab through the COM technology for the data exchange under the Windows operating system. Java does not support COM directly so the results of the JACOB project are used and modified to cope with this problem. In laboratories for control engineering education a control algorithm usually runs on a PC with Matlab that really controls the real plant. This is the server side described in the paper in details. To demonstrate the possibilities of a remote control a Java client server application is also introduced. It covers communication and offers a user friendly interface for the control of a remote plant and visualization of measured data.
Directory of Open Access Journals (Sweden)
Tinus Pretorius
2014-02-01
Full Text Available Globalisation is challenging almost every aspect of the political, economic, social and technological environment. Organisations, whether public or private, have to adapt their strategies and operations to stay competitive and efficient. Historically, organisations adopted project-based operations as a mode to stay competitive, although the applications tended to be the oneoff type of operations such as construction and system development projects (Edum-Fotwe & McCaffer, 2000. As the world changed from an industrially driven to a more knowledge driven economy and the pace of continuous change became more intense, organisations adopted a project-based mode of operations on a broader scale. The knowledge economy lead to the creation of many service orientated industries. Organisations started facing portfolios of projects where the nature of these projects differed in technological complexity, urgency, customer value and social impact (Gutjahr & Froeschl, 2013. Based on their experience with more technically orientated projects, organisations focused their attention more intensely on new project management methods, tools and processes and not necessarily on the human and organisational interfaces. This paradigm changed however, especially since the 1980s and more and more organisations adopted temporary organisational forms (Bakker, 2010 in order to improve their competitiveness. The contributions in this special edition of the South African Journal of Economic and Management Sciences have a common focus on the importance of the human and organisational interface of project-based operations on project success. The purpose of this concluding article is to analyse the findings and recommendations in these papers and to detect trends and future research opportunities in the field of project-based operations.
Data base on nuclear power plant dose reduction research projects
Energy Technology Data Exchange (ETDEWEB)
Khan, T.A.; Baum, J.W.
1986-10-01
Staff at the ALARA Center of Brookhaven National Laboratory have established a data base of information about current research that is likely to result in lower radiation doses to workers. The data base, concerned primarily with nuclear power generation, is part of a project that the ALARA Center is carrying out for the Nuclear Regulatory Commission. This report describes its current status. A substantial amount of research on reducing occupational exposure is being done in the US and abroad. This research is beginning to have an impact on the collective dose expenditures at nuclear power plants. The collective radiation doses in Europe, Japan, and North America all show downward trends. A large part of the research in the US is either sponsored by the nuclear industry through joint industry organizations such as EPRI and ESEERCO or is done by individual corporations. There is also significant participation by smaller companies. The main emphasis of the research on dose reduction is on engineering approaches aimed at reducing radiation fields or keeping people out of high-exposure areas by using robotics. Effective ALARA programs are also underway at a large number of nuclear plants. Additional attention should be given to non-engineering approaches to dose reduction, which are potentially very useful and cost effective but require quantitative study and analysis based on data from nuclear power plants. 9 refs., 1 fig.
Surfaced-based investigations plan, Volume 4: Yucca Mountain Project
International Nuclear Information System (INIS)
1988-12-01
This document represents a detailed summary of design plans for surface-based investigations to be conducted for site characterization of the Yucca Mountain site. These plans are current as of December 1988. The description of surface-based site characterization activities contained in this document is intended to give all interested parties an understanding of the current plans for site characterization of Yucca Mountain. The maps presented in Volume 4 are products of the Geographic Information System (GIS) being used by the Yucca Mountain Project. The ARC/INFO GIS software, developed by Environmental Systems Research Institute, was used to digitize and process these SBIP maps. The maps were prepared using existing US Geological Survey (USGS) maps as a planimetric base. Roads and other surface features were interpreted from a variety of sources and entered into the GIS. Sources include the USGS maps, 1976 USGS orthophotoquads and aerial photography, 1986 and 1987 aerial photography, surveyed coordinates of field sites, and a combination of various maps, figures, descriptions and approximate coordinates of proposed locations for future activities
Pretorius, M.W.; Oerlemans, L.A.G.
2014-01-01
Globalisation is challenging almost every aspect of the political, economic, social and technological environment. Organisations, whether public or private, have to adapt their strategies and operations to stay competitive and efficient. Historically, organisations adopted project-based operations
Learning from project experiences using a legacy-based approach
Cooper, Lynne P.; Majchrzak, Ann; Faraj, Samer
2005-01-01
As project teams become used more widely, the question of how to capitalize on the knowledge learned in project teams remains an open issue. Using previous research on shared cognition in groups, an approach to promoting post-project learning was developed. This Legacy Review concept was tested on four in tact project teams. The results from those test sessions were used to develop a model of team learning via group cognitive processes. The model and supporting propositions are presented.
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.
Langhout, Regina Day; Rappaport, Julian; Simmons, Doretha
2002-01-01
Culturally relevant, ongoing project-based learning was facilitated in a predominantly African American urban elementary school via a community garden project. The project involved teachers, students, university members, and community members. This article evaluates the project through two classroom-community collaboration models, noting common…
Promoting Collaboration in a Project-Based E-Learning Context
Papanikolaou, Kyparisia; Boubouka, Maria
2011-01-01
In this paper we investigate the value of collaboration scripts for promoting metacognitive knowledge in a project-based e-learning context. In an empirical study, 82 students worked individually and in groups on a project using the e-learning environment MyProject, in which the life cycle of a project is inherent. Students followed a particular…
SOFT project: a new forecasting system based on satellite data
Pascual, Ananda; Orfila, A.; Alvarez, Alberto; Hernandez, E.; Gomis, D.; Barth, Alexander; Tintore, Joaquim
2002-01-01
The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite dat, evolutionary programming and numerical ocean models. To achieve this objective two steps are proved: (1) to obtain an accurate ocean forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build 'intelligent' systems that, learning form the past ocean variability and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid systems while the second set will be used to validate the hybrid and traditional numerical model results.
So many developers, so many projects: toward a motivation-based theory of project selection
van Osch, W.; Adelaar, T.; Pith, M.
2011-01-01
Studies into open source software (OSS) development projects have hitherto focused on the question of why people aremotivated to contribute to these projects, thereby assuming that motivational factors are the same across all types of OSSprojects. In this study we challenge this assumption by
Meyer, Angela Osterman; Mon, Manuel J.; Hibbard, Susan T.
2011-01-01
We present our Lunar Phases Project, an ongoing effort utilizing students' actual observations within a mental model building framework to improve student understanding of the causes and process of the lunar phases. We implement this project with a sample of undergraduate, nonscience major students enrolled in a midsized public university located…
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.
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.
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
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.
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^{+} + N^{0} where N^{+} and N^{0} 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.
Finite detector based projection model for super resolution CT
Energy Technology Data Exchange (ETDEWEB)
Yu, Hengyong; Wang, Ge [Wake Forest Univ. Health Sciences, Winston-Salem, NC (United States). Dept. of Radiology; Virgina Tech, Blacksburg, VA (United States). Biomedical Imaging Div.
2011-07-01
For finite detector and focal spot sizes, here we propose a projection model for super resolution CT. First, for a given X-ray source point, a projection datum is modeled as an area integral over a narrow fan-beam connecting the detector elemental borders and the X-ray source point. Then, the final projection value is expressed as the integral obtained in the first step over the whole focal spot support. An ordered-subset simultaneous algebraic reconstruction technique (OS-SART) is developed using the proposed projection model. In the numerical simulation, our method produces super spatial resolution and suppresses high-frequency artifacts. (orig.)
Xie, Lizhe; Hu, Yining; Chen, Yang; Shi, Luyao
2015-03-01
Projection and back-projection are the most computational consuming parts in Computed Tomography (CT) reconstruction. Parallelization strategies using GPU computing techniques have been introduced. We in this paper present a new parallelization scheme for both projection and back-projection. The proposed method is based on CUDA technology carried out by NVIDIA Corporation. Instead of build complex model, we aimed on optimizing the existing algorithm and make it suitable for CUDA implementation so as to gain fast computation speed. Besides making use of texture fetching operation which helps gain faster interpolation speed, we fixed sampling numbers in the computation of projection, to ensure the synchronization of blocks and threads, thus prevents the latency caused by inconsistent computation complexity. Experiment results have proven the computational efficiency and imaging quality of the proposed method.
Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms
Bas, Gokhan
2008-01-01
This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…
Preparing Hispanic Students for the Real World: Benefits of Problem-Based Service Learning Projects
West, Jean Jaymes; Simmons, Donna
2012-01-01
Student learning is enriched by problem-based service learning (PBSL) projects. For Hispanic students, the learning that takes place in PBSL projects may be even more significant, although the research published in academic journals about client-based projects for Hispanic students is limited. This article begins to advance an understanding of how…
Dimensions of Problem Based Learning--Dialogue and Online Collaboration in Projects
Andreasen,, Lars Birch; Nielsen, Jørgen Lerche
2013-01-01
The article contributes to the discussions on problem based learning and project work, building on and reflecting the experiences of the authors. Four perspectives are emphasized as central to a contemporary approach to problem- and project-based learning: the exploration of problems, projects as a method, online collaboration, and the dialogic…
Space-time least-squares Petrov-Galerkin projection in nonlinear model reduction.
Energy Technology Data Exchange (ETDEWEB)
Choi, Youngsoo [Sandia National Laboratories (SNL-CA), Livermore, CA (United States). Extreme-scale Data Science and Analytics Dept.; Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Carlberg, Kevin Thomas [Sandia National Laboratories (SNL-CA), Livermore, CA (United States). Extreme-scale Data Science and Analytics Dept.
2017-09-01
Our work proposes a space-time least-squares Petrov-Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model-reduction methods that first apply Petrov-Galerkin projection in the spatial dimension and subsequently apply time integration to numerically resolve the resulting low-dimensional dynamical system, the proposed method applies projection in space and time simultaneously. To accomplish this, the method first introduces a low-dimensional space-time trial subspace, which can be obtained by computing tensor decompositions of state-snapshot data. The method then computes discrete-optimal approximations in this space-time trial subspace by minimizing the residual arising after time discretization over all space and time in a weighted ℓ^{2}-norm. This norm can be de ned to enable complexity reduction (i.e., hyper-reduction) in time, which leads to space-time collocation and space-time GNAT variants of the ST-LSPG method. Advantages of the approach relative to typical spatial-projection-based nonlinear model reduction methods such as Galerkin projection and least-squares Petrov-Galerkin projection include: (1) a reduction of both the spatial and temporal dimensions of the dynamical system, (2) the removal of spurious temporal modes (e.g., unstable growth) from the state space, and (3) error bounds that exhibit slower growth in time. Numerical examples performed on model problems in fluid dynamics demonstrate the ability of the method to generate orders-of-magnitude computational savings relative to spatial-projection-based reduced-order models without sacrificing accuracy.
DEFF Research Database (Denmark)
Rasmussen, Troels A.; Merritt, Timothy R.
2017-01-01
CNC cutting machines have become essential tools for designers and architects enabling rapid prototyping, model-building and production of high quality components. Designers often cut from new materials, discarding the irregularly shaped remains. We introduce ProjecTables, a visual augmented...... reality system for interactive packing of model parts onto sheet materials. ProjecTables enables designers to (re)use scrap materials for CNC cutting that would have been previously thrown away, at the same time supporting aesthetic choices related to wood grain, avoiding surface blemishes, and other...... relevant material properties. We conducted evaluations of ProjecTables with design students from Aarhus School of Architecture, demonstrating that participants could quickly and easily place and orient model parts reducing material waste. Contextual interviews and ideation sessions led to a deeper...
Project Robust Scheduling Based on the Scattered Buffer Technology
Directory of Open Access Journals (Sweden)
Nansheng Pang
2018-04-01
Full Text Available The research object in this paper is the sub network formed by the predecessor’s affect on the solution activity. This paper is to study three types of influencing factors from the predecessors that lead to the delay of starting time of the solution activity on the longest path, and to analyze the influence degree on the delay of the solution activity’s starting time from different types of factors. On this basis, through the comprehensive analysis of various factors that influence the solution activity, this paper proposes a metric that is used to evaluate the solution robustness of the project scheduling, and this metric is taken as the optimization goal. This paper also adopts the iterative process to design a scattered buffer heuristics algorithm based on the robust scheduling of the time buffer. At the same time, the resource flow network is introduced in this algorithm, using the tabu search algorithm to solve baseline scheduling. For the generation of resource flow network in the baseline scheduling, this algorithm designs a resource allocation algorithm with the maximum use of the precedence relations. Finally, the algorithm proposed in this paper and some other algorithms in previous literature are taken into the simulation experiment; under the comparative analysis, the experimental results show that the algorithm proposed in this paper is reasonable and feasible.
Collaborative Tracking of Image Features Based on Projective Invariance
Jiang, Jinwei
-mode sensors for improving the flexibility and robustness of the system. From the experimental results during three field tests for the LASOIS system, we observed that most of the errors in the image processing algorithm are caused by the incorrect feature tracking. This dissertation addresses the feature tracking problem in image sequences acquired from cameras. Despite many alternatives to feature tracking problem, iterative least squares solution solving the optical flow equation has been the most popular approach used by many in the field. This dissertation attempts to leverage the former efforts to enhance feature tracking methods by introducing a view geometric constraint to the tracking problem, which provides collaboration among features. In contrast to alternative geometry based methods, the proposed approach provides an online solution to optical flow estimation in a collaborative fashion by exploiting Horn and Schunck flow estimation regularized by view geometric constraints. Proposed collaborative tracker estimates the motion of a feature based on the geometry of the scene and how the other features are moving. Alternative to this approach, a new closed form solution to tracking that combines the image appearance with the view geometry is also introduced. We particularly use invariants in the projective coordinates and conjecture that the traditional appearance solution can be significantly improved using view geometry. The geometric constraint is introduced by defining a new optical flow equation which exploits the scene geometry from a set drawn from tracked features. At the end of each tracking loop the quality of the tracked features is judged using both appearance similarity and geometric consistency. Our experiments demonstrate robust tracking performance even when the features are occluded or they undergo appearance changes due to projective deformation of the template. The proposed collaborative tracking method is also tested in the visual navigation
Project-based fieldwork: perspectives of graduate entry students and project sponsors.
Fortune, Tracy; McKinstry, Carol
2012-08-01
This article builds on an earlier viewpoint regarding the need for project-focussed fieldwork. It presents the findings of an evaluative study into the value of project placements undertaken by final year graduate entry master's students as part of a capstone subject. The authors argue that provision of project placements enable impending graduates to develop and implement macro level strategies to develop prevention, resource and service development skills often required of contemporary occupational therapy practitioners. A qualitative approach is adopted. Student cohorts from 2005 and 2006 completed open-ended, written questionnaires, and agency project sponsors were interviewed to obtain their perspectives of the project placement experience. Despite some concern that project placements might be undertaken at the expense of 'clinical' placements these findings reveal that projects managed by students were perceived by services to add great value enabling them to advance important priorities. Students and sponsors highlighted a range of positive learning outcomes, including the ability to work collaboratively with supervisors and develop advanced communication skills and political acumen. The success of such placements depends on supportive supervision from academic staff. CONCLUSIONS AND SIGNIFICANCE OF THE STUDY: The findings promote project placements as a highly authentic aspect of work integrated learning enabling learners to draw together a range of attributes that support the ability to manage complex issues that have occupational relevance at a macro level. In addition, such experiences help learners to develop agency and political acumen both increasingly important capabilities for the contemporary workplace. © 2012 The Authors Australian Occupational Therapy Journal © 2012 Occupational Therapy Australia.
Projection-slice theorem based 2D-3D registration
Bom, van der M.J.; Pluim, J.P.W.; Homan, R.; Timmer, J.; Bartels, L.W.; Pluim, J.P.W.; Reinhardt, J.M.
2007-01-01
In X-ray guided procedures, the surgeon or interventionalist is dependent on his or her knowledge of the patient's specific anatomy and the projection images acquired during the procedure by a rotational X-ray source. Unfortunately, these X-ray projections fail to give information on the patient's
Linear-programming-based heuristics for project capacity planning
Gademann, A.J.R.M.; Schutten, J.M.J.
2005-01-01
Many multi-project organizations are capacity driven, which means that their operations are constrained by various scarce resources. An important planning aspect in a capacity driven multi-project organization is capacity planning. By capacity planning, we mean the problem of matching demand for
TRIM timber projections: an evaluation based on forest inventory measurements.
John R. Mills
1989-01-01
Two consecutive timberland inventories collected from permanent plots in the natural pine type in North Carolina were used to evaluate the timber resource inventory model (TRIM). This study compares model predictions with field measurements and examines the effect of inventory data aggregation on the accuracy of projections. Projections were repeated for two geographic...
Sizing ERP implementation projects: an activity-based approach
Janssens, G.; Kusters, R.J.; Heemstra, F.J.
2008-01-01
ERP implementation projects affect large parts of an implementing organization and lead to changes in the way an organization performs its tasks. The costs needed for the effort to implement these systems are hard to estimate. Research indicates that the size of an ERP project can be a useful
Performance of RGB laser-based projection for video walls
Hickl, Peter
2018-02-01
The laser phosphor concept is currently the common approach for most applications to introduce laser as a projection light source. However, this concept bears quite some disadvantages for rear-projection video walls. Therefore, Barco has developed a RGB laser design for use in the control room market with tailor-made performance.
Metrics-based control in outsourced software development projects
Ponisio, Laura; van Eck, Pascal
2012-01-01
Measurements have been recognised as vital instruments to improve control in outsourced software development projects. However, project managers are still struggling with the design and implementation of effective measurement programs. One reason for this is that although there is a large body of
Exact angular momentum projection based on cranked HFB solution
Energy Technology Data Exchange (ETDEWEB)
Enami, Kenichi; Tanabe, Kosai; Yosinaga, Naotaka [Saitama Univ., Urawa (Japan). Dept. of Physics
1998-03-01
Exact angular momentum projection of cranked HFB solutions is carried out. It is reconfirmed from this calculation that cranked HFB solutions reproduce the intrinsic structure of deformed nucleus. The result also indicates that the energy correction from projection is important for further investigation of nuclear structure. (author)
Project Scheduling Based on Risk of Gas Transmission Pipe
Silvianita; Nurbaity, A.; Mulyadi, Y.; Suntoyo; Chamelia, D. M.
2018-03-01
The planning of a project has a time limit on which must be completed before or right at a predetermined time. Thus, in a project planning, it is necessary to have scheduling management that is useful for completing a project to achieve maximum results by considering the constraints that will exists. Scheduling management is undertaken to deal with uncertainties and negative impacts of time and cost in project completion. This paper explains about scheduling management in gas transmission pipeline project Gresik-Semarang to find out which scheduling plan is most effectively used in accordance with its risk value. Scheduling management in this paper is assissted by Microsoft Project software to find the critical path of existing project scheduling planning data. Critical path is the longest scheduling path with the fastest completion time. The result is found a critical path on project scheduling with completion time is 152 days. Furthermore, the calculation of risk is done by using House of Risk (HOR) method and it is found that the critical path has a share of 40.98 percent of all causes of the occurence of risk events that will be experienced.
An Internet-Based Accounting Information Systems Project
Miller, Louise
2012-01-01
This paper describes a student project assignment used in an accounting information systems course. We are now truly immersed in the internet age, and while many required accounting information systems courses and textbooks introduce database design, accounting software development, cloud computing, and internet security, projects involving the…
Virtual fringe projection system with nonparallel illumination based on iteration
International Nuclear Information System (INIS)
Zhou, Duo; Wang, Zhangying; Gao, Nan; Zhang, Zonghua; Jiang, Xiangqian
2017-01-01
Fringe projection profilometry has been widely applied in many fields. To set up an ideal measuring system, a virtual fringe projection technique has been studied to assist in the design of hardware configurations. However, existing virtual fringe projection systems use parallel illumination and have a fixed optical framework. This paper presents a virtual fringe projection system with nonparallel illumination. Using an iterative method to calculate intersection points between rays and reference planes or object surfaces, the proposed system can simulate projected fringe patterns and captured images. A new explicit calibration method has been presented to validate the precision of the system. Simulated results indicate that the proposed iterative method outperforms previous systems. Our virtual system can be applied to error analysis, algorithm optimization, and help operators to find ideal system parameter settings for actual measurements. (paper)
Lin, Chien-Liang
2018-02-01
This study sought to develop a self-report instrument to be used in the assessment of the project competences of college students engaged in online project-based learning. Three scales of the KIPSSE instrument developed for this study, namely, the knowledge integration, project skills, and self-efficacy scales, were based on related theories and the analysis results of three project advisor interviews. Those items of knowledge integration and project skill scales focused on the integration of different disciplines and technological skills separately. Two samples of data were collected from information technology-related courses taught with an online project-based learning strategy over different semesters at a college in southern Taiwan. The validity and reliability of the KIPSSE instrument were confirmed through item analysis and confirmatory factor analysis using structural equation modeling of two samples of students' online response sets separately. The Cronbach's alpha reliability coefficient for the entire instrument was 0.931; for each scale, the alpha ranged from 0.832 to 0.907. There was also a significant correlation ( r = 0.55, p < 0.01) between the KIPSSE instrument results and the students' product evaluation scores. The findings of this study confirmed the validity and reliability of the KIPSSE instrument. The confirmation process and related implications are also discussed.
Global Change Data Base Pilot (Diskette) Project for Africa. Dase base documentation version 1.1
1992-06-01
The data base for Africa contains vegetation data, climate data, and other thematic data for a recent three and a half year period. The documentation contains separate chapters of material edited about separate data sets of the overall Global Change Data Base, Pilot (diskette) Project for Africa. Descriptions of data formats, authors, and contributors are accompanied by documentation of the development/use of the data. These materials have been suggested and contributed directly by the authors of the individual data sets. To avoid confusion, some information contained in the documentation that is not directly relevant to the data base has been either omitted, crossed out, or blanked out.
Subspace Dimensionality: A Tool for Automated QC in Seismic Array Processing
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
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.
Projection preconditioning for Lanczos-type methods
Energy Technology Data Exchange (ETDEWEB)
Bielawski, S.S.; Mulyarchik, S.G.; Popov, A.V. [Belarusian State Univ., Minsk (Belarus)
1996-12-31
We show how auxiliary subspaces and related projectors may be used for preconditioning nonsymmetric system of linear equations. It is shown that preconditioned in such a way (or projected) system is better conditioned than original system (at least if the coefficient matrix of the system to be solved is symmetrizable). Two approaches for solving projected system are outlined. The first one implies straightforward computation of the projected matrix and consequent using some direct or iterative method. The second approach is the projection preconditioning of conjugate gradient-type solver. The latter approach is developed here in context with biconjugate gradient iteration and some related Lanczos-type algorithms. Some possible particular choices of auxiliary subspaces are discussed. It is shown that one of them is equivalent to using colorings. Some results of numerical experiments are reported.
A Large Scale Problem Based Learning inter-European Student Satellite Construction Project
DEFF Research Database (Denmark)
Nielsen, Jens Frederik Dalsgaard; Alminde, Lars; Bisgaard, Morten
2006-01-01
that electronic communication technology was vital within the project. Additionally the SSETI EXPRESS project implied the following problems it didn’t fit to a standard semester - 18 months for the satellite project compared to 5/6 months for a “normal” semester project. difficulties in integrating the tasks......A LARGE SCALE PROBLEM BASED LEARNING INTER-EUROPEAN STUDENT SATELLITE CONSTRUCTION PROJECT This paper describes the pedagogical outcome of a large scale PBL experiment. ESA (European Space Agency) Education Office launched January 2004 an ambitious project: Let students from all over Europe build....... The satellite was successfully launched on October 27th 2005 (http://www.express.space.aau.dk). The project was a student driven project with student project responsibility adding at lot of international experiences and project management skills to the outcome of more traditional one semester, single group...
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
A Function-Based Framework for Stream Assessment & Restoration Projects
This report lays out a framework for approaching stream assessment and restoration projects that focuses on understanding the suite of stream functions at a site in the context of what is happening in the watershed.
Disturbance observer-based adaptive sliding mode hybrid projective ...
Indian Academy of Sciences (India)
Ayub Khan
2018-04-21
Apr 21, 2018 ... systems, the effect of external disturbances, energy fluc- tuation and other such ...... When we apply the above proposed strategy to achieve hybrid projective ..... ory and intelligent control (Springer, 2016) pp. 681–. 697.
Camera calibration based on the back projection process
Gu, Feifei; Zhao, Hong; Ma, Yueyang; Bu, Penghui
2015-12-01
Camera calibration plays a crucial role in 3D measurement tasks of machine vision. In typical calibration processes, camera parameters are iteratively optimized in the forward imaging process (FIP). However, the results can only guarantee the minimum of 2D projection errors on the image plane, but not the minimum of 3D reconstruction errors. In this paper, we propose a universal method for camera calibration, which uses the back projection process (BPP). In our method, a forward projection model is used to obtain initial intrinsic and extrinsic parameters with a popular planar checkerboard pattern. Then, the extracted image points are projected back into 3D space and compared with the ideal point coordinates. Finally, the estimation of the camera parameters is refined by a non-linear function minimization process. The proposed method can obtain a more accurate calibration result, which is more physically useful. Simulation and practical data are given to demonstrate the accuracy of the proposed method.
Work-based Project Overcomes Basic Skills Stigma.
Wallis, Emma
2002-01-01
A project to provide steel workers in North Wales with guidance about learning opportunities and to promote lifelong learning in the workplace focused on the development of basic information technology skills. (JOW)
1997-01-01
The purpose of this Phase 2 Study is to compare national motor vehicle accident projections : made from the Crash Avoidance Research Data base (CARDfile) with national motor : vehicle accident projections made from other data bases. For the most part...
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.
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.
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.
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....
Verschuure, G.A.
2014-01-01
Fifteen years have passed since the start of the national project New Dutch Waterline, presented as an illustrative example of a renewed approach of which spatial design was cross linked to heritage, for it described in the Nota Belvedere (Feddes, 1999). From 1815 until 1940 the New Dutch Waterline
A new major SETI project based on Project SERENDIP data and 100,000 personal computers
Sullivan, Woodruff T., III; Werthimer, Dan; Bowyer, Stuart; Cobb, Jeff; Gedye, David; Anderson, David
1997-01-01
We are now developing an innovative SETI project involving massively parallel computation on desktop computers scattered around the world. The public will be uniquely involved in a real scientific project. Individuals will download a screensaver program that will not only provide the usual attractive graphics when their computer is idle, but will also perform sophisticated analysis of SETI data using the host computer. The data are tapped off Project SERENDIP IV's receiver and SETI survey operating on the 305-m-diameter Arecibo radio telescope. We make a continuous tape-recording of a 2-MHz bandwidth signal centered on the 21-cm H I line. The data on these tapes are then preliminarily screened and parceled out by a server that supplies small chunks of data over the Internet to clients possessing the screen-saver software. After the client computer has automatically analyzed a complete chunk of data a report on the best candidate signals is sent back to the server, whereupon a new chunk of data is sent out. If 50,000-100,000 customers can be achieved, the computing power will be equivalent to a substantial fraction of atypical supercomputer, and the project will cover a volume of parameter space comparable to that of SERENDIP IV.