Maximizing the Spread of Influence via Generalized Degree Discount.

PubMed

Wang, Xiaojie; Zhang, Xue; Zhao, Chengli; Yi, Dongyun

2016-01-01

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods.

Maximizing the Spread of Influence via Generalized Degree Discount

PubMed Central

Wang, Xiaojie; Zhang, Xue; Zhao, Chengli; Yi, Dongyun

2016-01-01

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods. PMID:27732681

Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations

PubMed Central

Law, Jodi Woan-Fei; Ab Mutalib, Nurul-Syakima; Chan, Kok-Gan; Lee, Learn-Han

2015-01-01

The incidence of foodborne diseases has increased over the years and resulted in major public health problem globally. Foodborne pathogens can be found in various foods and it is important to detect foodborne pathogens to provide safe food supply and to prevent foodborne diseases. The conventional methods used to detect foodborne pathogen are time consuming and laborious. Hence, a variety of methods have been developed for rapid detection of foodborne pathogens as it is required in many food analyses. Rapid detection methods can be categorized into nucleic acid-based, biosensor-based and immunological-based methods. This review emphasizes on the principles and application of recent rapid methods for the detection of foodborne bacterial pathogens. Detection methods included are simple polymerase chain reaction (PCR), multiplex PCR, real-time PCR, nucleic acid sequence-based amplification (NASBA), loop-mediated isothermal amplification (LAMP) and oligonucleotide DNA microarray which classified as nucleic acid-based methods; optical, electrochemical and mass-based biosensors which classified as biosensor-based methods; enzyme-linked immunosorbent assay (ELISA) and lateral flow immunoassay which classified as immunological-based methods. In general, rapid detection methods are generally time-efficient, sensitive, specific and labor-saving. The developments of rapid detection methods are vital in prevention and treatment of foodborne diseases. PMID:25628612

Automatic liver segmentation in computed tomography using general-purpose shape modeling methods.

PubMed

Spinczyk, Dominik; Krasoń, Agata

2018-05-29

Liver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods. As part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field. Mutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55[Formula: see text], of which for three of them the coefficient was over 70[Formula: see text], which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5[Formula: see text] CONCLUSIONS: This value of 88.5 [Formula: see text] Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object-the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the model can be modeled mathematically by using various kernel functions, which allows to segment the liver on a comparable level using a smaller learning set.

A Generalized Pivotal Quantity Approach to Analytical Method Validation Based on Total Error.

PubMed

Yang, Harry; Zhang, Jianchun

2015-01-01

The primary purpose of method validation is to demonstrate that the method is fit for its intended use. Traditionally, an analytical method is deemed valid if its performance characteristics such as accuracy and precision are shown to meet prespecified acceptance criteria. However, these acceptance criteria are not directly related to the method's intended purpose, which is usually a gurantee that a high percentage of the test results of future samples will be close to their true values. Alternate "fit for purpose" acceptance criteria based on the concept of total error have been increasingly used. Such criteria allow for assessing method validity, taking into account the relationship between accuracy and precision. Although several statistical test methods have been proposed in literature to test the "fit for purpose" hypothesis, the majority of the methods are not designed to protect the risk of accepting unsuitable methods, thus having the potential to cause uncontrolled consumer's risk. In this paper, we propose a test method based on generalized pivotal quantity inference. Through simulation studies, the performance of the method is compared to five existing approaches. The results show that both the new method and the method based on β-content tolerance interval with a confidence level of 90%, hereafter referred to as the β-content (0.9) method, control Type I error and thus consumer's risk, while the other existing methods do not. It is further demonstrated that the generalized pivotal quantity method is less conservative than the β-content (0.9) method when the analytical methods are biased, whereas it is more conservative when the analytical methods are unbiased. Therefore, selection of either the generalized pivotal quantity or β-content (0.9) method for an analytical method validation depends on the accuracy of the analytical method. It is also shown that the generalized pivotal quantity method has better asymptotic properties than all of the current methods. Analytical methods are often used to ensure safety, efficacy, and quality of medicinal products. According to government regulations and regulatory guidelines, these methods need to be validated through well-designed studies to minimize the risk of accepting unsuitable methods. This article describes a novel statistical test for analytical method validation, which provides better protection for the risk of accepting unsuitable analytical methods. © PDA, Inc. 2015.

Complexity analysis based on generalized deviation for financial markets

NASA Astrophysics Data System (ADS)

Li, Chao; Shang, Pengjian

2018-03-01

In this paper, a new modified method is proposed as a measure to investigate the correlation between past price and future volatility for financial time series, known as the complexity analysis based on generalized deviation. In comparison with the former retarded volatility model, the new approach is both simple and computationally efficient. The method based on the generalized deviation function presents us an exhaustive way showing the quantization of the financial market rules. Robustness of this method is verified by numerical experiments with both artificial and financial time series. Results show that the generalized deviation complexity analysis method not only identifies the volatility of financial time series, but provides a comprehensive way distinguishing the different characteristics between stock indices and individual stocks. Exponential functions can be used to successfully fit the volatility curves and quantify the changes of complexity for stock market data. Then we study the influence for negative domain of deviation coefficient and differences during the volatile periods and calm periods. after the data analysis of the experimental model, we found that the generalized deviation model has definite advantages in exploring the relationship between the historical returns and future volatility.

Generalized Preconditioned Locally Harmonic Residual Eigensolver (GPLHR) v0.1

DOE Office of Scientific and Technical Information (OSTI.GOV)

VECHARYNSKI, EUGENE; YANG, CHAO

The software contains a MATLAB implementation of the Generalized Preconditioned Locally Harmonic Residual (GPLHR) method for solving standard and generalized non-Hermitian eigenproblems. The method is particularly useful for computing a subset of eigenvalues, and their eigen- or Schur vectors, closest to a given shift. The proposed method is based on block iterations and can take advantage of a preconditioner if it is available. It does not need to perform exact shift-and-invert transformation. Standard and generalized eigenproblems are handled in a unified framework.

The Gibbs Variational Method in Thermodynamics of Equilibrium Plasma: 1. General Conditions of Equilibrium and Stability for One-Component Charged Gas

DTIC Science & Technology

2018-04-01

systems containing ionized gases. 2. Gibbs Method in the Integral Form As per the Gibbs general methodology , based on the concept of heterogeneous...ARL-TR-8348 ● APR 2018 US Army Research Laboratory The Gibbs Variational Method in Thermodynamics of Equilibrium Plasma: 1...ARL-TR-8348 ● APR 2018 US Army Research Laboratory The Gibbs Variational Method in Thermodynamics of Equilibrium Plasma: 1. General

Development of a general method for quantifying IgG-based therapeutic monoclonal antibodies in human plasma using protein G purification coupled with a two internal standard calibration strategy using LC-MS/MS.

PubMed

Chiu, Huai-Hsuan; Liao, Hsiao-Wei; Shao, Yu-Yun; Lu, Yen-Shen; Lin, Ching-Hung; Tsai, I-Lin; Kuo, Ching-Hua

2018-08-17

Monoclonal antibody (mAb) drugs have generated much interest in recent years for treating various diseases. Immunoglobulin G (IgG) represents a high percentage of mAb drugs that have been approved by the Food and Drug Administration (FDA). To facilitate therapeutic drug monitoring and pharmacokinetic/pharmacodynamic studies, we developed a general liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify the concentration of IgG-based mAbs in human plasma. Three IgG-based drugs (bevacizumab, nivolumab and pembrolizumab) were selected to demonstrate our method. Protein G beads were used for sample pretreatment due to their universal ability to trap IgG-based drugs. Surrogate peptides that were obtained after trypsin digestion were quantified by using LC-MS/MS. To calibrate sample preparation errors and matrix effects that occur during LC-MS/MS analysis, we used two internal standards (IS) method that include the IgG-based drug-IS tocilizumab and post-column infused IS. Using two internal standards was found to effectively improve quantification accuracy, which was within 15% for all mAb drugs that were tested at three different concentrations. This general method was validated in term of its precision, accuracy, linearity and sensitivity for 3 demonstration mAb drugs. The successful application of the method to clinical samples demonstrated its' applicability in clinical analysis. It is anticipated that this general method could be applied to other mAb-based drugs for use in precision medicine and clinical studies. Copyright © 2018 Elsevier B.V. All rights reserved.

A Classical Based Derivation of Time Dilation Providing First Order Accuracy to Schwarzschild's Solution of Einstein's Field Equations

NASA Astrophysics Data System (ADS)

Austin, Rickey W.

In Einstein's theory of Special Relativity (SR), one method to derive relativistic kinetic energy is via applying the classical work-energy theorem to relativistic momentum. This approach starts with a classical based work-energy theorem and applies SR's momentum to the derivation. One outcome of this derivation is relativistic kinetic energy. From this derivation, it is rather straight forward to form a kinetic energy based time dilation function. In the derivation of General Relativity a common approach is to bypass classical laws as a starting point. Instead a rigorous development of differential geometry and Riemannian space is constructed, from which classical based laws are derived. This is in contrast to SR's approach of starting with classical laws and applying the consequences of the universal speed of light by all observers. A possible method to derive time dilation due to Newtonian gravitational potential energy (NGPE) is to apply SR's approach to deriving relativistic kinetic energy. It will be shown this method gives a first order accuracy compared to Schwarzschild's metric. The SR's kinetic energy and the newly derived NGPE derivation are combined to form a Riemannian metric based on these two energies. A geodesic is derived and calculations compared to Schwarzschild's geodesic for an orbiting test mass about a central, non-rotating, non-charged massive body. The new metric results in high accuracy calculations when compared to Einsteins General Relativity's prediction. The new method provides a candidate approach for starting with classical laws and deriving General Relativity effects. This approach mimics SR's method of starting with classical mechanics when deriving relativistic equations. As a compliment to introducing General Relativity, it provides a plausible scaffolding method from classical physics when teaching introductory General Relativity. A straight forward path from classical laws to General Relativity will be derived. This derivation provides a minimum first order accuracy to Schwarzschild's solution to Einstein's field equations.

Transfer Function of Multi-Stage Active Filters: A Solution Based on Pascal's Triangle and a General Expression

ERIC Educational Resources Information Center

Levesque, Luc

2012-01-01

A method is proposed to simplify analytical computations of the transfer function for electrical circuit filters, which are made from repetitive identical stages. A method based on the construction of Pascal's triangle is introduced and then a general solution from two initial conditions is provided for the repetitive identical stage. The present…

Estimation of Standard Error of Regression Effects in Latent Regression Models Using Binder's Linearization. Research Report. ETS RR-07-09

ERIC Educational Resources Information Center

Li, Deping; Oranje, Andreas

2007-01-01

Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…

75 FR 13745 - Office of Innovation and Improvement Overview Information; Ready To Teach Program-General...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-23

... on rigorous, scientifically based research methods to assess the effectiveness of a particular... and programs; and (B) Includes research that-- (i) Employs systematic, empirical methods that draw on... hypotheses and justify the general conclusions drawn; (iii) Relies on measurements or observational methods...

General framework for dynamic large deformation contact problems based on phantom-node X-FEM

NASA Astrophysics Data System (ADS)

Broumand, P.; Khoei, A. R.

2018-04-01

This paper presents a general framework for modeling dynamic large deformation contact-impact problems based on the phantom-node extended finite element method. The large sliding penalty contact formulation is presented based on a master-slave approach which is implemented within the phantom-node X-FEM and an explicit central difference scheme is used to model the inertial effects. The method is compared with conventional contact X-FEM; advantages, limitations and implementational aspects are also addressed. Several numerical examples are presented to show the robustness and accuracy of the proposed method.

Generalized query-based active learning to identify differentially methylated regions in DNA.

PubMed

Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

2013-01-01

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

Comparison of Multiscale Method of Cells-Based Models for Predicting Elastic Properties of Filament Wound C/C-SiC

NASA Technical Reports Server (NTRS)

Pineda, Evan J.; Fassin, Marek; Bednarcyk, Brett A.; Reese, Stefanie; Simon, Jaan-Willem

2017-01-01

Three different multiscale models, based on the method of cells (generalized and high fidelity) micromechanics models were developed and used to predict the elastic properties of C/C-SiC composites. In particular, the following multiscale modeling strategies were employed: Concurrent multiscale modeling of all phases using the generalized method of cells, synergistic (two-way coupling in space) multiscale modeling with the generalized method of cells, and hierarchical (one-way coupling in space) multiscale modeling with the high fidelity generalized method of cells. The three models are validated against data from a hierarchical multiscale finite element model in the literature for a repeating unit cell of C/C-SiC. Furthermore, the multiscale models are used in conjunction with classical lamination theory to predict the stiffness of C/C-SiC plates manufactured via a wet filament winding and liquid silicon infiltration process recently developed by the German Aerospace Institute.

General imaging of advanced 3D mask objects based on the fully-vectorial extended Nijboer-Zernike (ENZ) theory

NASA Astrophysics Data System (ADS)

van Haver, Sven; Janssen, Olaf T. A.; Braat, Joseph J. M.; Janssen, Augustus J. E. M.; Urbach, H. Paul; Pereira, Silvania F.

2008-03-01

In this paper we introduce a new mask imaging algorithm that is based on the source point integration method (or Abbe method). The method presented here distinguishes itself from existing methods by exploiting the through-focus imaging feature of the Extended Nijboer-Zernike (ENZ) theory of diffraction. An introduction to ENZ-theory and its application in general imaging is provided after which we describe the mask imaging scheme that can be derived from it. The remainder of the paper is devoted to illustrating the advantages of the new method over existing methods (Hopkins-based). To this extent several simulation results are included that illustrate advantages arising from: the accurate incorporation of isolated structures, the rigorous treatment of the object (mask topography) and the fully vectorial through-focus image formation of the ENZ-based algorithm.

Accessing remote data bases using microcomputers

PubMed Central

Saul, Peter D.

1985-01-01

General practitioners' access to remote data bases using microcomputers is increasing, making even the most obscure information readily available. Some of the systems available to general practitioners in the UK are described and the methods of access are outlined. General practitioners should be aware of the advances in technology; data bases are increasing in size, the cost of access is falling and their use is becoming easier. PMID:4020756

Recognition of building group patterns in topographic maps based on graph partitioning and random forest

NASA Astrophysics Data System (ADS)

He, Xianjin; Zhang, Xinchang; Xin, Qinchuan

2018-02-01

Recognition of building group patterns (i.e., the arrangement and form exhibited by a collection of buildings at a given mapping scale) is important to the understanding and modeling of geographic space and is hence essential to a wide range of downstream applications such as map generalization. Most of the existing methods develop rigid rules based on the topographic relationships between building pairs to identify building group patterns and thus their applications are often limited. This study proposes a method to identify a variety of building group patterns that allow for map generalization. The method first identifies building group patterns from potential building clusters based on a machine-learning algorithm and further partitions the building clusters with no recognized patterns based on the graph partitioning method. The proposed method is applied to the datasets of three cities that are representative of the complex urban environment in Southern China. Assessment of the results based on the reference data suggests that the proposed method is able to recognize both regular (e.g., the collinear, curvilinear, and rectangular patterns) and irregular (e.g., the L-shaped, H-shaped, and high-density patterns) building group patterns well, given that the correctness values are consistently nearly 90% and the completeness values are all above 91% for three study areas. The proposed method shows promises in automated recognition of building group patterns that allows for map generalization.

Sensitivity analysis and approximation methods for general eigenvalue problems

NASA Technical Reports Server (NTRS)

Murthy, D. V.; Haftka, R. T.

1986-01-01

Optimization of dynamic systems involving complex non-hermitian matrices is often computationally expensive. Major contributors to the computational expense are the sensitivity analysis and reanalysis of a modified design. The present work seeks to alleviate this computational burden by identifying efficient sensitivity analysis and approximate reanalysis methods. For the algebraic eigenvalue problem involving non-hermitian matrices, algorithms for sensitivity analysis and approximate reanalysis are classified, compared and evaluated for efficiency and accuracy. Proper eigenvector normalization is discussed. An improved method for calculating derivatives of eigenvectors is proposed based on a more rational normalization condition and taking advantage of matrix sparsity. Important numerical aspects of this method are also discussed. To alleviate the problem of reanalysis, various approximation methods for eigenvalues are proposed and evaluated. Linear and quadratic approximations are based directly on the Taylor series. Several approximation methods are developed based on the generalized Rayleigh quotient for the eigenvalue problem. Approximation methods based on trace theorem give high accuracy without needing any derivatives. Operation counts for the computation of the approximations are given. General recommendations are made for the selection of appropriate approximation technique as a function of the matrix size, number of design variables, number of eigenvalues of interest and the number of design points at which approximation is sought.

A new method for calculating differential distributions directly in Mellin space

NASA Astrophysics Data System (ADS)

Mitov, Alexander

2006-12-01

We present a new method for the calculation of differential distributions directly in Mellin space without recourse to the usual momentum-fraction (or z-) space. The method is completely general and can be applied to any process. It is based on solving the integration-by-parts identities when one of the powers of the propagators is an abstract number. The method retains the full dependence on the Mellin variable and can be implemented in any program for solving the IBP identities based on algebraic elimination, like Laporta. General features of the method are: (1) faster reduction, (2) smaller number of master integrals compared to the usual z-space approach and (3) the master integrals satisfy difference instead of differential equations. This approach generalizes previous results related to fully inclusive observables like the recently calculated three-loop space-like anomalous dimensions and coefficient functions in inclusive DIS to more general processes requiring separate treatment of the various physical cuts. Many possible applications of this method exist, the most notable being the direct evaluation of the three-loop time-like splitting functions in QCD.

Hesitant fuzzy linguistic multicriteria decision-making method based on generalized prioritized aggregation operator.

PubMed

Wu, Jia-ting; Wang, Jian-qiang; Wang, Jing; Zhang, Hong-yu; Chen, Xiao-hong

2014-01-01

Based on linguistic term sets and hesitant fuzzy sets, the concept of hesitant fuzzy linguistic sets was introduced. The focus of this paper is the multicriteria decision-making (MCDM) problems in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic numbers (HFLNs). A new approach to solving these problems is proposed, which is based on the generalized prioritized aggregation operator of HFLNs. Firstly, the new operations and comparison method for HFLNs are provided and some linguistic scale functions are applied. Subsequently, two prioritized aggregation operators and a generalized prioritized aggregation operator of HFLNs are developed and applied to MCDM problems. Finally, an illustrative example is given to illustrate the effectiveness and feasibility of the proposed method, which are then compared to the existing approach.

WE-FG-207B-05: Iterative Reconstruction Via Prior Image Constrained Total Generalized Variation for Spectral CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niu, S; Zhang, Y; Ma, J

Purpose: To investigate iterative reconstruction via prior image constrained total generalized variation (PICTGV) for spectral computed tomography (CT) using fewer projections while achieving greater image quality. Methods: The proposed PICTGV method is formulated as an optimization problem, which balances the data fidelity and prior image constrained total generalized variation of reconstructed images in one framework. The PICTGV method is based on structure correlations among images in the energy domain and high-quality images to guide the reconstruction of energy-specific images. In PICTGV method, the high-quality image is reconstructed from all detector-collected X-ray signals and is referred as the broad-spectrum image. Distinctmore » from the existing reconstruction methods applied on the images with first order derivative, the higher order derivative of the images is incorporated into the PICTGV method. An alternating optimization algorithm is used to minimize the PICTGV objective function. We evaluate the performance of PICTGV on noise and artifacts suppressing using phantom studies and compare the method with the conventional filtered back-projection method as well as TGV based method without prior image. Results: On the digital phantom, the proposed method outperforms the existing TGV method in terms of the noise reduction, artifacts suppression, and edge detail preservation. Compared to that obtained by the TGV based method without prior image, the relative root mean square error in the images reconstructed by the proposed method is reduced by over 20%. Conclusion: The authors propose an iterative reconstruction via prior image constrained total generalize variation for spectral CT. Also, we have developed an alternating optimization algorithm and numerically demonstrated the merits of our approach. Results show that the proposed PICTGV method outperforms the TGV method for spectral CT.« less

Generalized Ordinary Differential Equation Models 1

PubMed Central

Miao, Hongyu; Wu, Hulin; Xue, Hongqi

2014-01-01

Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method. PMID:25544787

Generalized Ordinary Differential Equation Models.

PubMed

Miao, Hongyu; Wu, Hulin; Xue, Hongqi

2014-10-01

Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method.

A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

PubMed Central

Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

2017-01-01

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

General purpose nonlinear system solver based on Newton-Krylov method.

DOE Office of Scientific and Technical Information (OSTI.GOV)

2013-12-01

KINSOL is part of a software family called SUNDIALS: SUite of Nonlinear and Differential/Algebraic equation Solvers [1]. KINSOL is a general-purpose nonlinear system solver based on Newton-Krylov and fixed-point solver technologies [2].

PRIFIRA: General regularization using prior-conditioning for fast radio interferometric imaging†

NASA Astrophysics Data System (ADS)

Naghibzadeh, Shahrzad; van der Veen, Alle-Jan

2018-06-01

Image formation in radio astronomy is a large-scale inverse problem that is inherently ill-posed. We present a general algorithmic framework based on a Bayesian-inspired regularized maximum likelihood formulation of the radio astronomical imaging problem with a focus on diffuse emission recovery from limited noisy correlation data. The algorithm is dubbed PRIor-conditioned Fast Iterative Radio Astronomy (PRIFIRA) and is based on a direct embodiment of the regularization operator into the system by right preconditioning. The resulting system is then solved using an iterative method based on projections onto Krylov subspaces. We motivate the use of a beamformed image (which includes the classical "dirty image") as an efficient prior-conditioner. Iterative reweighting schemes generalize the algorithmic framework and can account for different regularization operators that encourage sparsity of the solution. The performance of the proposed method is evaluated based on simulated one- and two-dimensional array arrangements as well as actual data from the core stations of the Low Frequency Array radio telescope antenna configuration, and compared to state-of-the-art imaging techniques. We show the generality of the proposed method in terms of regularization schemes while maintaining a competitive reconstruction quality with the current reconstruction techniques. Furthermore, we show that exploiting Krylov subspace methods together with the proper noise-based stopping criteria results in a great improvement in imaging efficiency.

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

NASA Astrophysics Data System (ADS)

Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

2018-06-01

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

A multiple maximum scatter difference discriminant criterion for facial feature extraction.

PubMed

Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei

2007-12-01

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.

A Rational Examination of Integrating "Classics" into University General Education Curriculum: An Empirical Survey Based on N University

ERIC Educational Resources Information Center

Zhong, Zhenshan; Sun, Mengyao

2018-01-01

The power of general education curriculum comes from the enduring classics. The authors apply research methods such as questionnaire survey, interview, and observation to investigate the state of general education curriculum implementation at N University and analyze problems faced by incorporating classics. Based on this, the authors propose that…

A computational method for solving stochastic Itô–Volterra integral equations based on stochastic operational matrix for generalized hat basis functions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heydari, M.H., E-mail: heydari@stu.yazd.ac.ir; The Laboratory of Quantum Information Processing, Yazd University, Yazd; Hooshmandasl, M.R., E-mail: hooshmandasl@yazd.ac.ir

2014-08-01

In this paper, a new computational method based on the generalized hat basis functions is proposed for solving stochastic Itô–Volterra integral equations. In this way, a new stochastic operational matrix for generalized hat functions on the finite interval [0,T] is obtained. By using these basis functions and their stochastic operational matrix, such problems can be transformed into linear lower triangular systems of algebraic equations which can be directly solved by forward substitution. Also, the rate of convergence of the proposed method is considered and it has been shown that it is O(1/(n{sup 2}) ). Further, in order to show themore » accuracy and reliability of the proposed method, the new approach is compared with the block pulse functions method by some examples. The obtained results reveal that the proposed method is more accurate and efficient in comparison with the block pule functions method.« less

A comparison of moment-based methods of estimation for the log Pearson type 3 distribution

NASA Astrophysics Data System (ADS)

Koutrouvelis, I. A.; Canavos, G. C.

2000-06-01

The log Pearson type 3 distribution is a very important model in statistical hydrology, especially for modeling annual flood series. In this paper we compare the various methods based on moments for estimating quantiles of this distribution. Besides the methods of direct and mixed moments which were found most successful in previous studies and the well-known indirect method of moments, we develop generalized direct moments and generalized mixed moments methods and a new method of adaptive mixed moments. The last method chooses the orders of two moments for the original observations by utilizing information contained in the sample itself. The results of Monte Carlo experiments demonstrated the superiority of this method in estimating flood events of high return periods when a large sample is available and in estimating flood events of low return periods regardless of the sample size. In addition, a comparison of simulation and asymptotic results shows that the adaptive method may be used for the construction of meaningful confidence intervals for design events based on the asymptotic theory even with small samples. The simulation results also point to the specific members of the class of generalized moments estimates which maintain small values for bias and/or mean square error.

Technical Note: Synchrotron-based high-energy x-ray phase sensitive microtomography for biomedical research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Huiqiang; Wu, Xizeng, E-mail: xwu@uabmc.edu, E-mail: tqxiao@sinap.ac.cn; Xiao, Tiqiao, E-mail: xwu@uabmc.edu, E-mail: tqxiao@sinap.ac.cn

Purpose: Propagation-based phase-contrast CT (PPCT) utilizes highly sensitive phase-contrast technology applied to x-ray microtomography. Performing phase retrieval on the acquired angular projections can enhance image contrast and enable quantitative imaging. In this work, the authors demonstrate the validity and advantages of a novel technique for high-resolution PPCT by using the generalized phase-attenuation duality (PAD) method of phase retrieval. Methods: A high-resolution angular projection data set of a fish head specimen was acquired with a monochromatic 60-keV x-ray beam. In one approach, the projection data were directly used for tomographic reconstruction. In two other approaches, the projection data were preprocessed bymore » phase retrieval based on either the linearized PAD method or the generalized PAD method. The reconstructed images from all three approaches were then compared in terms of tissue contrast-to-noise ratio and spatial resolution. Results: The authors’ experimental results demonstrated the validity of the PPCT technique based on the generalized PAD-based method. In addition, the results show that the authors’ technique is superior to the direct PPCT technique as well as the linearized PAD-based PPCT technique in terms of their relative capabilities for tissue discrimination and characterization. Conclusions: This novel PPCT technique demonstrates great potential for biomedical imaging, especially for applications that require high spatial resolution and limited radiation exposure.« less

Review of Statistical Methods for Analysing Healthcare Resources and Costs

PubMed Central

Mihaylova, Borislava; Briggs, Andrew; O'Hagan, Anthony; Thompson, Simon G

2011-01-01

We review statistical methods for analysing healthcare resource use and costs, their ability to address skewness, excess zeros, multimodality and heavy right tails, and their ease for general use. We aim to provide guidance on analysing resource use and costs focusing on randomised trials, although methods often have wider applicability. Twelve broad categories of methods were identified: (I) methods based on the normal distribution, (II) methods following transformation of data, (III) single-distribution generalized linear models (GLMs), (IV) parametric models based on skewed distributions outside the GLM family, (V) models based on mixtures of parametric distributions, (VI) two (or multi)-part and Tobit models, (VII) survival methods, (VIII) non-parametric methods, (IX) methods based on truncation or trimming of data, (X) data components models, (XI) methods based on averaging across models, and (XII) Markov chain methods. Based on this review, our recommendations are that, first, simple methods are preferred in large samples where the near-normality of sample means is assured. Second, in somewhat smaller samples, relatively simple methods, able to deal with one or two of above data characteristics, may be preferable but checking sensitivity to assumptions is necessary. Finally, some more complex methods hold promise, but are relatively untried; their implementation requires substantial expertise and they are not currently recommended for wider applied work. Copyright © 2010 John Wiley & Sons, Ltd. PMID:20799344

Research on the comparison of performance-based concept and force-based concept

NASA Astrophysics Data System (ADS)

Wu, Zeyu; Wang, Dongwei

2011-03-01

There are two ideologies about structure design: force-based concept and performance-based concept. Generally, if the structure operates during elastic stage, the two philosophies usually attain the same results. But beyond that stage, the shortage of force-based method is exposed, and the merit of performance-based is displayed. Pros and cons of each strategy are listed herein, and then which structure is best suitable to each method analyzed. At last, a real structure is evaluated by adaptive pushover method to verify that performance-based method is better than force-based method.

Development of a time-dependent incompressible Navier-Stokes solver based on a fractional-step method

NASA Technical Reports Server (NTRS)

Rosenfeld, Moshe

1990-01-01

The main goals are the development, validation, and application of a fractional step solution method of the time-dependent incompressible Navier-Stokes equations in generalized coordinate systems. A solution method that combines a finite volume discretization with a novel choice of the dependent variables and a fractional step splitting to obtain accurate solutions in arbitrary geometries is extended to include more general situations, including cases with moving grids. The numerical techniques are enhanced to gain efficiency and generality.

Estimation of group means when adjusting for covariates in generalized linear models.

PubMed

Qu, Yongming; Luo, Junxiang

2015-01-01

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

Shape optimization using a NURBS-based interface-enriched generalized FEM

DOE PAGES

Najafi, Ahmad R.; Safdari, Masoud; Tortorelli, Daniel A.; ...

2016-11-26

This study presents a gradient-based shape optimization over a fixed mesh using a non-uniform rational B-splines-based interface-enriched generalized finite element method, applicable to multi-material structures. In the proposed method, non-uniform rational B-splines are used to parameterize the design geometry precisely and compactly by a small number of design variables. An analytical shape sensitivity analysis is developed to compute derivatives of the objective and constraint functions with respect to the design variables. Subtle but important new terms involve the sensitivity of shape functions and their spatial derivatives. As a result, verification and illustrative problems are solved to demonstrate the precision andmore » capability of the method.« less

General design method of ultra-broadband perfect absorbers based on magnetic polaritons.

PubMed

Liu, Yuanbin; Qiu, Jun; Zhao, Junming; Liu, Linhua

2017-10-02

Starting from one-dimensional gratings and the theory of magnetic polaritons (MPs), we propose a general design method of ultra-broadband perfect absorbers. Based on the proposed design method, the obtained absorber can keep the spectrum-average absorptance over 99% at normal incidence in a wide range of wavelengths; this work simultaneously reveals the robustness of the absorber to incident angles and polarization angles of incident light. Furthermore, this work shows that the spectral band of perfect absorption can be flexibly extended to near the infrared regime by adjusting the structure dimension. The findings of this work may facilitate the active design of ultra-broadband absorbers based on plasmonic nanostructures.

Fault management for data systems

NASA Technical Reports Server (NTRS)

Boyd, Mark A.; Iverson, David L.; Patterson-Hine, F. Ann

1993-01-01

Issues related to automating the process of fault management (fault diagnosis and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing fault diagnosis computer programs is outlined. Some existing work in the area of graph-based fault diagnosis is reviewed, and a new fault management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described.

Invariant object recognition based on the generalized discrete radon transform

NASA Astrophysics Data System (ADS)

Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

High Order Modulation Protograph Codes

NASA Technical Reports Server (NTRS)

Nguyen, Thuy V. (Inventor); Nosratinia, Aria (Inventor); Divsalar, Dariush (Inventor)

2014-01-01

Digital communication coding methods for designing protograph-based bit-interleaved code modulation that is general and applies to any modulation. The general coding framework can support not only multiple rates but also adaptive modulation. The method is a two stage lifting approach. In the first stage, an original protograph is lifted to a slightly larger intermediate protograph. The intermediate protograph is then lifted via a circulant matrix to the expected codeword length to form a protograph-based low-density parity-check code.

Development of a time-dependent incompressible Navier-Stokes solver based on a fractional-step method

NASA Technical Reports Server (NTRS)

Rosenfeld, Moshe

1990-01-01

The development, validation and application of a fractional step solution method of the time-dependent incompressible Navier-Stokes equations in generalized coordinate systems are discussed. A solution method that combines a finite-volume discretization with a novel choice of the dependent variables and a fractional step splitting to obtain accurate solutions in arbitrary geometries was previously developed for fixed-grids. In the present research effort, this solution method is extended to include more general situations, including cases with moving grids. The numerical techniques are enhanced to gain efficiency and generality.

Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

NASA Astrophysics Data System (ADS)

Wang, M.; Hu, N. Q.; Qin, G. J.

2011-07-01

In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

Generalized fictitious methods for fluid-structure interactions: Analysis and simulations

NASA Astrophysics Data System (ADS)

Yu, Yue; Baek, Hyoungsu; Karniadakis, George Em

2013-07-01

We present a new fictitious pressure method for fluid-structure interaction (FSI) problems in incompressible flow by generalizing the fictitious mass and damping methods we published previously in [1]. The fictitious pressure method involves modification of the fluid solver whereas the fictitious mass and damping methods modify the structure solver. We analyze all fictitious methods for simplified problems and obtain explicit expressions for the optimal reduction factor (convergence rate index) at the FSI interface [2]. This analysis also demonstrates an apparent similarity of fictitious methods to the FSI approach based on Robin boundary conditions, which have been found to be very effective in FSI problems. We implement all methods, including the semi-implicit Robin based coupling method, in the context of spectral element discretization, which is more sensitive to temporal instabilities than low-order methods. However, the methods we present here are simple and general, and hence applicable to FSI based on any other spatial discretization. In numerical tests, we verify the selection of optimal values for the fictitious parameters for simplified problems and for vortex-induced vibrations (VIV) even at zero mass ratio ("for-ever-resonance"). We also develop an empirical a posteriori analysis for complex geometries and apply it to 3D patient-specific flexible brain arteries with aneurysms for very large deformations. We demonstrate that the fictitious pressure method enhances stability and convergence, and is comparable or better in most cases to the Robin approach or the other fictitious methods.

Using the Logarithmic Concentration Diagram, Log "C", to Teach Acid-Base Equilibrium

ERIC Educational Resources Information Center

Kovac, Jeffrey

2012-01-01

Acid-base equilibrium is one of the most important and most challenging topics in a typical general chemistry course. This article introduces an alternative to the algebraic approach generally used in textbooks, the graphical log "C" method. Log "C" diagrams provide conceptual insight into the behavior of aqueous acid-base systems and allow…

Video-assisted feedback in general practice internships using German general practitioner's guidelines

PubMed Central

Bölter, Regine; Freund, Tobias; Ledig, Thomas; Boll, Bernhard; Szecsenyi, Joachim; Roos, Marco

2012-01-01

Introduction: The planned modification of the Medical Licenses Act in Germany will strengthen the specialty of general practice. Therefore, medical students should get to know the daily routine of general practitioners during their academic studies. At least 10% of students should get the possibility to spend one quarter of the internship, in the last year of their academic studies, in a practice of family medicine. The demonstrated teaching method aims at giving feedback to the student based on video recordings of patient consultations (student-patient) with the help of a checklist. Video-feedback is already successful used in medical teaching in Germany and abroad. This feasibility study aims at assessing the practicability of video-assisted feedback as a teaching method during internship in general practice. Teaching method: First of all, the general practice chooses a guideline as the learning objective. Secondly, a subsequent patient – student – consultation is recorded on video. Afterwards, a video-assisted formative feedback is given by the physician. A checklist with learning objectives (communication, medical examination, a structured case report according to the guideline) is used to structure the feedback content. Feasibility: The feasibility was assessed by a semi structured interview in order to gain insight into barriers and challenges for future implementation. The teaching method was performed in one general practice. Afterwards the teaching physician and the trainee intern were interviewed. The following four main categories were identified: feasibility, performance, implementation in daily routine, challenges of the teaching concept. The results of the feasibility study show general practicability of this approach. Installing a video camera in one examination room may solve technical problems. The trainee intern mentioned theoretical and practical benefits using the guideline. The teaching physician noted the challenge to reflect on his daily routines in the light of evidence-based guidelines. Conclusion: This teaching method supports quality control and standardizing of learning objectives during the internship in general practice by using general practice guidelines. The use of a checklist enhances this method in general practice. We consider the presented teaching method in the context of the planned modification of the Medical Licenses Act is part of quality control and standardisation of medical teaching during general practice internships. In order to validate these presumptions, further, evaluation of this method concerning the learning objectives using the guidelines of general practice need to be carried out. PMID:23255963

Measuring Efficiency of Secondary Healthcare Providers in Slovenia

PubMed Central

Blatnik, Patricia; Bojnec, Štefan; Tušak, Matej

2017-01-01

Abstract The chief aim of this study was to analyze secondary healthcare providers' efficiency, focusing on the efficiency analysis of Slovene general hospitals. We intended to present a complete picture of technical, allocative, and cost or economic efficiency of general hospitals. Methods We researched the aspects of efficiency with two econometric methods. First, we calculated the necessary quotients of efficiency with the stochastic frontier analyze (SFA), which are realized by econometric evaluation of stochastic frontier functions; then, with the data envelopment analyze (DEA), we calculated the necessary quotients that are based on the linear programming method. Results Results on measures of efficiency showed that the two chosen methods produced two different conclusions. The SFA method concluded Celje General Hospital is the most efficient general hospital, whereas the DEA method concluded Brežice General Hospital was the hospital to be declared as the most efficient hospital. Conclusion Our results are a useful tool that can aid managers, payers, and designers of healthcare policy to better understand how general hospitals operate. The participants can accordingly decide with less difficulty on any further business operations of general hospitals, having the best practices of general hospitals at their disposal. PMID:28730180

A general dead-time correction method based on live-time stamping. Application to the measurement of short-lived radionuclides.

PubMed

Chauvenet, B; Bobin, C; Bouchard, J

2017-12-01

Dead-time correction formulae are established in the general case of superimposed non-homogeneous Poisson processes. Based on the same principles as conventional live-timed counting, this method exploits the additional information made available using digital signal processing systems, and especially the possibility to store the time stamps of live-time intervals. No approximation needs to be made to obtain those formulae. Estimates of the variances of corrected rates are also presented. This method is applied to the activity measurement of short-lived radionuclides. Copyright © 2017 Elsevier Ltd. All rights reserved.

Purely numerical approach for analyzing flow to a well intercepting a vertical fracture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Narasimhan, T.N.; Palen, W.A.

1979-03-01

A numerical method, based on an Integral Finite Difference approach, is presented to investigate wells intercepting fractures in general and vertical fractures in particular. Such features as finite conductivity, wellbore storage, damage, and fracture deformability and its influence as permeability are easily handled. The advantage of the numerical approach is that it is based on fewer assumptions than analytic solutions and hence has greater generality. Illustrative examples are given to validate the method against known solutions. New results are presenteed to demonstrate the applicability of the method to problems not apparently considered in the literature so far.

A general method for the quantitative assessment of mineral pigments.

PubMed

Ares, M C Zurita; Fernández, J M

2016-01-01

A general method for the estimation of mineral pigment contents in different bases has been proposed using a sole set of calibration curves, (one for each pigment), calculated for a white standard base, thus elaborating patterns for each utilized base is not necessary. The method can be used in different bases and its validity had ev en been proved in strongly tinted bases. The method consists of a novel procedure that combines diffuse reflectance spectroscopy, second derivatives and the Kubelka-Munk function. This technique has proved to be at least one order of magnitude more sensitive than X-Ray diffraction for colored compounds, since it allowed the determination of the pigment amount in colored samples containing 0.5 wt% of pigment that was not detected by X-Ray Diffraction. The method can be used to estimate the concentration of mineral pigments in a wide variety of either natural or artificial materials, since it does not requiere the calculation of each pigment pattern in every base. This fact could have important industrial consequences, as the proposed method would be more convenient, faster and cheaper. Copyright © 2015 Elsevier B.V. All rights reserved.

Cluster and propensity based approximation of a network

PubMed Central

2013-01-01

Background The models in this article generalize current models for both correlation networks and multigraph networks. Correlation networks are widely applied in genomics research. In contrast to general networks, it is straightforward to test the statistical significance of an edge in a correlation network. It is also easy to decompose the underlying correlation matrix and generate informative network statistics such as the module eigenvector. However, correlation networks only capture the connections between numeric variables. An open question is whether one can find suitable decompositions of the similarity measures employed in constructing general networks. Multigraph networks are attractive because they support likelihood based inference. Unfortunately, it is unclear how to adjust current statistical methods to detect the clusters inherent in many data sets. Results Here we present an intuitive and parsimonious parametrization of a general similarity measure such as a network adjacency matrix. The cluster and propensity based approximation (CPBA) of a network not only generalizes correlation network methods but also multigraph methods. In particular, it gives rise to a novel and more realistic multigraph model that accounts for clustering and provides likelihood based tests for assessing the significance of an edge after controlling for clustering. We present a novel Majorization-Minimization (MM) algorithm for estimating the parameters of the CPBA. To illustrate the practical utility of the CPBA of a network, we apply it to gene expression data and to a bi-partite network model for diseases and disease genes from the Online Mendelian Inheritance in Man (OMIM). Conclusions The CPBA of a network is theoretically appealing since a) it generalizes correlation and multigraph network methods, b) it improves likelihood based significance tests for edge counts, c) it directly models higher-order relationships between clusters, and d) it suggests novel clustering algorithms. The CPBA of a network is implemented in Fortran 95 and bundled in the freely available R package PropClust. PMID:23497424

An unsupervised method for estimating the global horizontal irradiance from photovoltaic power measurements

NASA Astrophysics Data System (ADS)

Nespoli, Lorenzo; Medici, Vasco

2017-12-01

In this paper, we present a method to determine the global horizontal irradiance (GHI) from the power measurements of one or more PV systems, located in the same neighborhood. The method is completely unsupervised and is based on a physical model of a PV plant. The precise assessment of solar irradiance is pivotal for the forecast of the electric power generated by photovoltaic (PV) plants. However, on-ground measurements are expensive and are generally not performed for small and medium-sized PV plants. Satellite-based services represent a valid alternative to on site measurements, but their space-time resolution is limited. Results from two case studies located in Switzerland are presented. The performance of the proposed method at assessing GHI is compared with that of free and commercial satellite services. Our results show that the presented method is generally better than satellite-based services, especially at high temporal resolutions.

Theory and applications of structured light single pixel imaging

NASA Astrophysics Data System (ADS)

Stokoe, Robert J.; Stockton, Patrick A.; Pezeshki, Ali; Bartels, Randy A.

2018-02-01

Many single-pixel imaging techniques have been developed in recent years. Though the methods of image acquisition vary considerably, the methods share unifying features that make general analysis possible. Furthermore, the methods developed thus far are based on intuitive processes that enable simple and physically-motivated reconstruction algorithms, however, this approach may not leverage the full potential of single-pixel imaging. We present a general theoretical framework of single-pixel imaging based on frame theory, which enables general, mathematically rigorous analysis. We apply our theoretical framework to existing single-pixel imaging techniques, as well as provide a foundation for developing more-advanced methods of image acquisition and reconstruction. The proposed frame theoretic framework for single-pixel imaging results in improved noise robustness, decrease in acquisition time, and can take advantage of special properties of the specimen under study. By building on this framework, new methods of imaging with a single element detector can be developed to realize the full potential associated with single-pixel imaging.

Prediction of dynamical systems by symbolic regression

NASA Astrophysics Data System (ADS)

Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.

2016-07-01

We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.

Deformed exponentials and portfolio selection

NASA Astrophysics Data System (ADS)

Rodrigues, Ana Flávia P.; Guerreiro, Igor M.; Cavalcante, Charles Casimiro

In this paper, we present a method for portfolio selection based on the consideration on deformed exponentials in order to generalize the methods based on the gaussianity of the returns in portfolio, such as the Markowitz model. The proposed method generalizes the idea of optimizing mean-variance and mean-divergence models and allows a more accurate behavior for situations where heavy-tails distributions are necessary to describe the returns in a given time instant, such as those observed in economic crises. Numerical results show the proposed method outperforms the Markowitz portfolio for the cumulated returns with a good convergence rate of the weights for the assets which are searched by means of a natural gradient algorithm.

Implementation of a Self-Management System for Students with Disabilities in General Education Settings

ERIC Educational Resources Information Center

Schulze, Margaret A.

2016-01-01

Despite the fact that self-management procedures have a robust literature base attesting to their efficacy with students with disabilities, the use of these strategies in general education settings remains limited. This mixed methods study examined the implementation of self-management procedures using both quantitative and qualitative methods.…

Reasoning Maps: A Generally Applicable Method for Characterizing Hypothesis-Testing Behaviour. Research Report

ERIC Educational Resources Information Center

White, Brian

2004-01-01

This paper presents a generally applicable method for characterizing subjects' hypothesis-testing behaviour based on a synthesis that extends on previous work. Beginning with a transcript of subjects' speech and videotape of their actions, a Reasoning Map is created that depicts the flow of their hypotheses, tests, predictions, results, and…

Design and Implementation of a Studio-Based General Chemistry Course

ERIC Educational Resources Information Center

Gottfried, Amy C.; Sweeder, Ryan D.; Bartolin, Jeffrey M.; Hessler, Jessica A.; Reynolds, Benjamin P.; Stewart, Ian C.; Coppola, Brian P.; Holl, Mark Banaszak M.

2007-01-01

The design and implementation of a new value-added general chemistry course, which could use the studio instructional method to incorporate the existing educational research is reviewed. These teaching methods and activities were woven into the course to provide the students with ways of learning chemical concepts and practicing scientific…

The Basic Principles and Methods of the Music Curriculum for the General Education School.

ERIC Educational Resources Information Center

Kabalevskii, Dmitrii

1988-01-01

Delineates the foundations of Dmitri Kabalevskii's music curriculum for general education in the Soviet Union. Stresses teaching music as part of life itself. Bases teaching principles and methods on the song, dance, and march, termed "The Three Whales." Offers extensive lesson plans focusing on first grade music activities. (CH)

Matter in general relativity

NASA Technical Reports Server (NTRS)

Ray, J. R.

1982-01-01

Two theories of matter in general relativity, the fluid theory and the kinetic theory, were studied. Results include: (1) a discussion of various methods of completing the fluid equations; (2) a method of constructing charged general relativistic solutions in kinetic theory; and (3) a proof and discussion of the incompatibility of perfect fluid solutions in anisotropic cosmologies. Interpretations of NASA gravitational experiments using the above mentioned results were started. Two papers were prepared for publications based on this work.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

PubMed

Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

Video-assisted feedback in general practice internships using German general practitioner's guidelines.

PubMed

Bölter, Regine; Freund, Tobias; Ledig, Thomas; Boll, Bernhard; Szecsenyi, Joachim; Roos, Marco

2012-01-01

The planned modification of the Medical Licenses Act in Germany will strengthen the specialty of general practice. Therefore, medical students should get to know the daily routine of general practitioners during their academic studies. At least 10% of students should get the possibility to spend one quarter of the internship, in the last year of their academic studies, in a practice of family medicine. The demonstrated teaching method aims at giving feedback to the student based on video recordings of patient consultations (student-patient) with the help of a checklist. Video-feedback is already successful used in medical teaching in Germany and abroad. This feasibility study aims at assessing the practicability of video-assisted feedback as a teaching method during internship in general practice. First of all, the general practice chooses a guideline as the learning objective. Secondly, a subsequent patient - student - consultation is recorded on video. Afterwards, a video-assisted formative feedback is given by the physician. A checklist with learning objectives (communication, medical examination, a structured case report according to the guideline) is used to structure the feedback content. The feasibility was assessed by a semi structured interview in order to gain insight into barriers and challenges for future implementation. The teaching method was performed in one general practice. Afterwards the teaching physician and the trainee intern were interviewed. The Following four main categories were identified: feasibility, performance, implementation in daily routine, challenges of the teaching concept.The results of the feasibility study show general practicability of this approach. Installing a video camera in one examination room may solve technical problems. The trainee intern mentioned theoretical and practical benefits using the guideline. The teaching physician noted the challenge to reflect on his daily routines in the light of evidence-based guidelines. This teaching method supports quality control and standardizing of learning objectives during the internship in general practice by using general practice guidelines. The use of a checklist enhances this method in general practice. We consider the presented teaching method in the context of the planned modification of the Medical Licenses Act is part of quality control and standardisation of medical teaching during general practice internships. In order to validate these presumptions, further, evaluation of this method concerning the learning objectives using the guidelines of general practice need to be carried out.

Method of conditional moments (MCM) for the Chemical Master Equation: a unified framework for the method of moments and hybrid stochastic-deterministic models.

PubMed

Hasenauer, J; Wolf, V; Kazeroonian, A; Theis, F J

2014-09-01

The time-evolution of continuous-time discrete-state biochemical processes is governed by the Chemical Master Equation (CME), which describes the probability of the molecular counts of each chemical species. As the corresponding number of discrete states is, for most processes, large, a direct numerical simulation of the CME is in general infeasible. In this paper we introduce the method of conditional moments (MCM), a novel approximation method for the solution of the CME. The MCM employs a discrete stochastic description for low-copy number species and a moment-based description for medium/high-copy number species. The moments of the medium/high-copy number species are conditioned on the state of the low abundance species, which allows us to capture complex correlation structures arising, e.g., for multi-attractor and oscillatory systems. We prove that the MCM provides a generalization of previous approximations of the CME based on hybrid modeling and moment-based methods. Furthermore, it improves upon these existing methods, as we illustrate using a model for the dynamics of stochastic single-gene expression. This application example shows that due to the more general structure, the MCM allows for the approximation of multi-modal distributions.

A novel knowledge-based potential for RNA 3D structure evaluation

NASA Astrophysics Data System (ADS)

Yang, Yi; Gu, Qi; Zhang, Ben-Gong; Shi, Ya-Zhou; Shao, Zhi-Gang

2018-03-01

Ribonucleic acids (RNAs) play a vital role in biology, and knowledge of their three-dimensional (3D) structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation. Project supported by the National Science Foundation of China (Grants Nos. 11605125, 11105054, 11274124, and 11401448).

Smeared spectrum jamming suppression based on generalized S transform and threshold segmentation

NASA Astrophysics Data System (ADS)

Li, Xin; Wang, Chunyang; Tan, Ming; Fu, Xiaolong

2018-04-01

Smeared Spectrum (SMSP) jamming is an effective jamming in countering linear frequency modulation (LFM) radar. According to the time-frequency distribution difference between jamming and echo, a jamming suppression method based on Generalized S transform (GST) and threshold segmentation is proposed. The sub-pulse period is firstly estimated based on auto correlation function firstly. Secondly, the time-frequency image and the related gray scale image are achieved based on GST. Finally, the Tsallis cross entropy is utilized to compute the optimized segmentation threshold, and then the jamming suppression filter is constructed based on the threshold. The simulation results show that the proposed method is of good performance in the suppression of false targets produced by SMSP.

A new family of stable elements for the Stokes problem based on a mixed Galerkin/least-squares finite element formulation

NASA Technical Reports Server (NTRS)

Franca, Leopoldo P.; Loula, Abimael F. D.; Hughes, Thomas J. R.; Miranda, Isidoro

1989-01-01

Adding to the classical Hellinger-Reissner formulation, a residual form of the equilibrium equation, a new Galerkin/least-squares finite element method is derived. It fits within the framework of a mixed finite element method and is stable for rather general combinations of stress and velocity interpolations, including equal-order discontinuous stress and continuous velocity interpolations which are unstable within the Galerkin approach. Error estimates are presented based on a generalization of the Babuska-Brezzi theory. Numerical results (not presented herein) have confirmed these estimates as well as the good accuracy and stability of the method.

Address tracing for parallel machines

NASA Technical Reports Server (NTRS)

Stunkel, Craig B.; Janssens, Bob; Fuchs, W. Kent

1991-01-01

Recently implemented parallel system address-tracing methods based on several metrics are surveyed. The issues specific to collection of traces for both shared and distributed memory parallel computers are highlighted. Five general categories of address-trace collection methods are examined: hardware-captured, interrupt-based, simulation-based, altered microcode-based, and instrumented program-based traces. The problems unique to shared memory and distributed memory multiprocessors are examined separately.

Interleaved EPI diffusion imaging using SPIRiT-based reconstruction with virtual coil compression.

PubMed

Dong, Zijing; Wang, Fuyixue; Ma, Xiaodong; Zhang, Zhe; Dai, Erpeng; Yuan, Chun; Guo, Hua

2018-03-01

To develop a novel diffusion imaging reconstruction framework based on iterative self-consistent parallel imaging reconstruction (SPIRiT) for multishot interleaved echo planar imaging (iEPI), with computation acceleration by virtual coil compression. As a general approach for autocalibrating parallel imaging, SPIRiT improves the performance of traditional generalized autocalibrating partially parallel acquisitions (GRAPPA) methods in that the formulation with self-consistency is better conditioned, suggesting SPIRiT to be a better candidate in k-space-based reconstruction. In this study, a general SPIRiT framework is adopted to incorporate both coil sensitivity and phase variation information as virtual coils and then is applied to 2D navigated iEPI diffusion imaging. To reduce the reconstruction time when using a large number of coils and shots, a novel shot-coil compression method is proposed for computation acceleration in Cartesian sampling. Simulations and in vivo experiments were conducted to evaluate the performance of the proposed method. Compared with the conventional coil compression, the shot-coil compression achieved higher compression rates with reduced errors. The simulation and in vivo experiments demonstrate that the SPIRiT-based reconstruction outperformed the existing method, realigned GRAPPA, and provided superior images with reduced artifacts. The SPIRiT-based reconstruction with virtual coil compression is a reliable method for high-resolution iEPI diffusion imaging. Magn Reson Med 79:1525-1531, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

A Bootstrap Generalization of Modified Parallel Analysis for IRT Dimensionality Assessment

ERIC Educational Resources Information Center

Finch, Holmes; Monahan, Patrick

2008-01-01

This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA…

Comparison of GEOS-5 AGCM planetary boundary layer depths computed with various definitions

NASA Astrophysics Data System (ADS)

McGrath-Spangler, E. L.; Molod, A.

2014-07-01

Accurate models of planetary boundary layer (PBL) processes are important for forecasting weather and climate. The present study compares seven methods of calculating PBL depth in the GEOS-5 atmospheric general circulation model (AGCM) over land. These methods depend on the eddy diffusion coefficients, bulk and local Richardson numbers, and the turbulent kinetic energy. The computed PBL depths are aggregated to the Köppen-Geiger climate classes, and some limited comparisons are made using radiosonde profiles. Most methods produce similar midday PBL depths, although in the warm, moist climate classes the bulk Richardson number method gives midday results that are lower than those given by the eddy diffusion coefficient methods. Additional analysis revealed that methods sensitive to turbulence driven by radiative cooling produce greater PBL depths, this effect being most significant during the evening transition. Nocturnal PBLs based on Richardson number methods are generally shallower than eddy diffusion coefficient based estimates. The bulk Richardson number estimate is recommended as the PBL height to inform the choice of the turbulent length scale, based on the similarity to other methods during the day, and the improved nighttime behavior.

A Subspace Semi-Definite programming-based Underestimation (SSDU) method for stochastic global optimization in protein docking*

PubMed Central

Nan, Feng; Moghadasi, Mohammad; Vakili, Pirooz; Vajda, Sandor; Kozakov, Dima; Ch. Paschalidis, Ioannis

2015-01-01

We propose a new stochastic global optimization method targeting protein docking problems. The method is based on finding a general convex polynomial underestimator to the binding energy function in a permissive subspace that possesses a funnel-like structure. We use Principal Component Analysis (PCA) to determine such permissive subspaces. The problem of finding the general convex polynomial underestimator is reduced into the problem of ensuring that a certain polynomial is a Sum-of-Squares (SOS), which can be done via semi-definite programming. The underestimator is then used to bias sampling of the energy function in order to recover a deep minimum. We show that the proposed method significantly improves the quality of docked conformations compared to existing methods. PMID:25914440

Micromechanics of metal matrix composites using the Generalized Method of Cells model (GMC) user's guide

NASA Technical Reports Server (NTRS)

Aboudi, Jacob; Pindera, Marek-Jerzy

1992-01-01

A user's guide for the program gmc.f is presented. The program is based on the generalized method of cells model (GMC) which is capable via a micromechanical analysis, of predicting the overall, inelastic behavior of unidirectional, multi-phase composites from the knowledge of the properties of the viscoplastic constituents. In particular, the program is sufficiently general to predict the response of unidirectional composites having variable fiber shapes and arrays.

Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology.

PubMed

Hardcastle, Thomas J

2016-01-15

High-throughput data are now commonplace in biological research. Rapidly changing technologies and application mean that novel methods for detecting differential behaviour that account for a 'large P, small n' setting are required at an increasing rate. The development of such methods is, in general, being done on an ad hoc basis, requiring further development cycles and a lack of standardization between analyses. We present here a generalized method for identifying differential behaviour within high-throughput biological data through empirical Bayesian methods. This approach is based on our baySeq algorithm for identification of differential expression in RNA-seq data based on a negative binomial distribution, and in paired data based on a beta-binomial distribution. Here we show how the same empirical Bayesian approach can be applied to any parametric distribution, removing the need for lengthy development of novel methods for differently distributed data. Comparisons with existing methods developed to address specific problems in high-throughput biological data show that these generic methods can achieve equivalent or better performance. A number of enhancements to the basic algorithm are also presented to increase flexibility and reduce computational costs. The methods are implemented in the R baySeq (v2) package, available on Bioconductor http://www.bioconductor.org/packages/release/bioc/html/baySeq.html. tjh48@cam.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Møller-Plesset perturbation theory gradient in the generalized hybrid orbital quantum mechanical and molecular mechanical method

NASA Astrophysics Data System (ADS)

Jung, Jaewoon; Sugita, Yuji; Ten-no, S.

2010-02-01

An analytic gradient expression is formulated and implemented for the second-order Møller-Plesset perturbation theory (MP2) based on the generalized hybrid orbital QM/MM method. The method enables us to obtain an accurate geometry at a reasonable computational cost. The performance of the method is assessed for various isomers of alanine dipepetide. We also compare the optimized structures of fumaramide-derived [2]rotaxane and cAMP-dependent protein kinase with experiment.

A General and Flexible Approach to Estimating the Social Relations Model Using Bayesian Methods

ERIC Educational Resources Information Center

Ludtke, Oliver; Robitzsch, Alexander; Kenny, David A.; Trautwein, Ulrich

2013-01-01

The social relations model (SRM) is a conceptual, methodological, and analytical approach that is widely used to examine dyadic behaviors and interpersonal perception within groups. This article introduces a general and flexible approach to estimating the parameters of the SRM that is based on Bayesian methods using Markov chain Monte Carlo…

Survival analysis using inverse probability of treatment weighted methods based on the generalized propensity score.

PubMed

Sugihara, Masahiro

2010-01-01

In survival analysis, treatment effects are commonly evaluated based on survival curves and hazard ratios as causal treatment effects. In observational studies, these estimates may be biased due to confounding factors. The inverse probability of treatment weighted (IPTW) method based on the propensity score is one of the approaches utilized to adjust for confounding factors between binary treatment groups. As a generalization of this methodology, we developed an exact formula for an IPTW log-rank test based on the generalized propensity score for survival data. This makes it possible to compare the group differences of IPTW Kaplan-Meier estimators of survival curves using an IPTW log-rank test for multi-valued treatments. As causal treatment effects, the hazard ratio can be estimated using the IPTW approach. If the treatments correspond to ordered levels of a treatment, the proposed method can be easily extended to the analysis of treatment effect patterns with contrast statistics. In this paper, the proposed method is illustrated with data from the Kyushu Lipid Intervention Study (KLIS), which investigated the primary preventive effects of pravastatin on coronary heart disease (CHD). The results of the proposed method suggested that pravastatin treatment reduces the risk of CHD and that compliance to pravastatin treatment is important for the prevention of CHD. (c) 2009 John Wiley & Sons, Ltd.

Applicability of the polynomial chaos expansion method for personalization of a cardiovascular pulse wave propagation model.

PubMed

Huberts, W; Donders, W P; Delhaas, T; van de Vosse, F N

2014-12-01

Patient-specific modeling requires model personalization, which can be achieved in an efficient manner by parameter fixing and parameter prioritization. An efficient variance-based method is using generalized polynomial chaos expansion (gPCE), but it has not been applied in the context of model personalization, nor has it ever been compared with standard variance-based methods for models with many parameters. In this work, we apply the gPCE method to a previously reported pulse wave propagation model and compare the conclusions for model personalization with that of a reference analysis performed with Saltelli's efficient Monte Carlo method. We furthermore differentiate two approaches for obtaining the expansion coefficients: one based on spectral projection (gPCE-P) and one based on least squares regression (gPCE-R). It was found that in general the gPCE yields similar conclusions as the reference analysis but at much lower cost, as long as the polynomial metamodel does not contain unnecessary high order terms. Furthermore, the gPCE-R approach generally yielded better results than gPCE-P. The weak performance of the gPCE-P can be attributed to the assessment of the expansion coefficients using the Smolyak algorithm, which might be hampered by the high number of model parameters and/or by possible non-smoothness in the output space. Copyright © 2014 John Wiley & Sons, Ltd.

Comparing Methods for UAV-Based Autonomous Surveillance

NASA Technical Reports Server (NTRS)

Freed, Michael; Harris, Robert; Shafto, Michael

2004-01-01

We describe an approach to evaluating algorithmic and human performance in directing UAV-based surveillance. Its key elements are a decision-theoretic framework for measuring the utility of a surveillance schedule and an evaluation testbed consisting of 243 scenarios covering a well-defined space of possible missions. We apply this approach to two example UAV-based surveillance methods, a TSP-based algorithm and a human-directed approach, then compare them to identify general strengths, and weaknesses of each method.

Transient analysis of 1D inhomogeneous media by dynamic inhomogeneous finite element method

NASA Astrophysics Data System (ADS)

Yang, Zailin; Wang, Yao; Hei, Baoping

2013-12-01

The dynamic inhomogeneous finite element method is studied for use in the transient analysis of onedimensional inhomogeneous media. The general formula of the inhomogeneous consistent mass matrix is established based on the shape function. In order to research the advantages of this method, it is compared with the general finite element method. A linear bar element is chosen for the discretization tests of material parameters with two fictitious distributions. And, a numerical example is solved to observe the differences in the results between these two methods. Some characteristics of the dynamic inhomogeneous finite element method that demonstrate its advantages are obtained through comparison with the general finite element method. It is found that the method can be used to solve elastic wave motion problems with a large element scale and a large number of iteration steps.

Generalized method calculating the effective diffusion coefficient in periodic channels.

PubMed

Kalinay, Pavol

2015-01-07

The method calculating the effective diffusion coefficient in an arbitrary periodic two-dimensional channel, presented in our previous paper [P. Kalinay, J. Chem. Phys. 141, 144101 (2014)], is generalized to 3D channels of cylindrical symmetry, as well as to 2D or 3D channels with particles driven by a constant longitudinal external driving force. The next possible extensions are also indicated. The former calculation was based on calculus in the complex plane, suitable for the stationary diffusion in 2D domains. The method is reformulated here using standard tools of functional analysis, enabling the generalization.

Inverse solutions for electrical impedance tomography based on conjugate gradients methods

NASA Astrophysics Data System (ADS)

Wang, M.

2002-01-01

A multistep inverse solution for two-dimensional electric field distribution is developed to deal with the nonlinear inverse problem of electric field distribution in relation to its boundary condition and the problem of divergence due to errors introduced by the ill-conditioned sensitivity matrix and the noise produced by electrode modelling and instruments. This solution is based on a normalized linear approximation method where the change in mutual impedance is derived from the sensitivity theorem and a method of error vector decomposition. This paper presents an algebraic solution of the linear equations at each inverse step, using a generalized conjugate gradients method. Limiting the number of iterations in the generalized conjugate gradients method controls the artificial errors introduced by the assumption of linearity and the ill-conditioned sensitivity matrix. The solution of the nonlinear problem is approached using a multistep inversion. This paper also reviews the mathematical and physical definitions of the sensitivity back-projection algorithm based on the sensitivity theorem. Simulations and discussion based on the multistep algorithm, the sensitivity coefficient back-projection method and the Newton-Raphson method are given. Examples of imaging gas-liquid mixing and a human hand in brine are presented.

Existence and stability, and discrete BB and rank conditions, for general mixed-hybrid finite elements in elasticity

NASA Technical Reports Server (NTRS)

Xue, W.-M.; Atluri, S. N.

1985-01-01

In this paper, all possible forms of mixed-hybrid finite element methods that are based on multi-field variational principles are examined as to the conditions for existence, stability, and uniqueness of their solutions. The reasons as to why certain 'simplified hybrid-mixed methods' in general, and the so-called 'simplified hybrid-displacement method' in particular (based on the so-called simplified variational principles), become unstable, are discussed. A comprehensive discussion of the 'discrete' BB-conditions, and the rank conditions, of the matrices arising in mixed-hybrid methods, is given. Some recent studies aimed at the assurance of such rank conditions, and the related problem of the avoidance of spurious kinematic modes, are presented.

Dynamic Chest Image Analysis: Evaluation of Model-Based Pulmonary Perfusion Analysis With Pyramid Images

DTIC Science & Technology

2001-10-25

Image Analysis aims to develop model-based computer analysis and visualization methods for showing focal and general abnormalities of lung ventilation and perfusion based on a sequence of digital chest fluoroscopy frames collected with the Dynamic Pulmonary Imaging technique 18,5,17,6. We have proposed and evaluated a multiresolutional method with an explicit ventilation model based on pyramid images for ventilation analysis. We have further extended the method for ventilation analysis to pulmonary perfusion. This paper focuses on the clinical evaluation of our method for

A new self-shielding method based on a detailed cross-section representation in the resolved energy domain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saygin, H.; Hebert, A.

The calculation of a dilution cross section {bar {sigma}}{sub e} is the most important step in the self-shielding formalism based on the equivalence principle. If a dilution cross section that accurately characterizes the physical situation can be calculated, it can then be used for calculating the effective resonance integrals and obtaining accurate self-shielded cross sections. A new technique for the calculation of equivalent cross sections based on the formalism of Riemann integration in the resolved energy domain is proposed. This new method is compared to the generalized Stamm`ler method, which is also based on an equivalence principle, for a two-regionmore » cylindrical cell and for a small pressurized water reactor assembly in two dimensions. The accuracy of each computing approach is obtained using reference results obtained from a fine-group slowing-down code named CESCOL. It is shown that the proposed method leads to slightly better performance than the generalized Stamm`ler approach.« less

Real-time traffic sign recognition based on a general purpose GPU and deep-learning

PubMed Central

Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). PMID:28264011

Range image segmentation using Zernike moment-based generalized edge detector

NASA Technical Reports Server (NTRS)

Ghosal, S.; Mehrotra, R.

1992-01-01

The authors proposed a novel Zernike moment-based generalized step edge detection method which can be used for segmenting range and intensity images. A generalized step edge detector is developed to identify different kinds of edges in range images. These edge maps are thinned and linked to provide final segmentation. A generalized edge is modeled in terms of five parameters: orientation, two slopes, one step jump at the location of the edge, and the background gray level. Two complex and two real Zernike moment-based masks are required to determine all these parameters of the edge model. Theoretical noise analysis is performed to show that these operators are quite noise tolerant. Experimental results are included to demonstrate edge-based segmentation technique.

Effective Biot theory and its generalization to poroviscoelastic models

NASA Astrophysics Data System (ADS)

Liu, Xu; Greenhalgh, Stewart; Zhou, Bing; Greenhalgh, Mark

2018-02-01

A method is suggested to express the effective bulk modulus of the solid frame of a poroelastic material as a function of the saturated bulk modulus. This method enables effective Biot theory to be described through the use of seismic dispersion measurements or other models developed for the effective saturated bulk modulus. The effective Biot theory is generalized to a poroviscoelastic model of which the moduli are represented by the relaxation functions of the generalized fractional Zener model. The latter covers the general Zener and the Cole-Cole models as special cases. A global search method is described to determine the parameters of the relaxation functions, and a simple deterministic method is also developed to find the defining parameters of the single Cole-Cole model. These methods enable poroviscoelastic models to be constructed, which are based on measured seismic attenuation functions, and ensure that the model dispersion characteristics match the observations.

Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows

NASA Astrophysics Data System (ADS)

Herkül, Kristjan; Peterson, Anneliis; Paekivi, Sander

2017-06-01

Both basic science and marine spatial planning are in a need of high resolution spatially continuous data on seabed habitats and biota. As conventional point-wise sampling is unable to cover large spatial extents in high detail, it must be supplemented with remote sensing and modeling in order to fulfill the scientific and management needs. The combined use of in situ sampling, sonar scanning, and mathematical modeling is becoming the main method for mapping both abiotic and biotic seabed features. Further development and testing of the methods in varying locations and environmental settings is essential for moving towards unified and generally accepted methodology. To fill the relevant research gap in the Baltic Sea, we used multibeam sonar and mathematical modeling methods - generalized additive models (GAM) and random forest (RF) - together with underwater video to map seabed substrate and epibenthos of offshore shallows. In addition to testing the general applicability of the proposed complex of techniques, the predictive power of different sonar-based variables and modeling algorithms were tested. Mean depth, followed by mean backscatter, were the most influential variables in most of the models. Generally, mean values of sonar-based variables had higher predictive power than their standard deviations. The predictive accuracy of RF was higher than that of GAM. To conclude, we found the method to be feasible and with predictive accuracy similar to previous studies of sonar-based mapping.

Divergence preserving discrete surface integral methods for Maxwell's curl equations using non-orthogonal unstructured grids

NASA Technical Reports Server (NTRS)

Madsen, Niel K.

1992-01-01

Several new discrete surface integral (DSI) methods for solving Maxwell's equations in the time-domain are presented. These methods, which allow the use of general nonorthogonal mixed-polyhedral unstructured grids, are direct generalizations of the canonical staggered-grid finite difference method. These methods are conservative in that they locally preserve divergence or charge. Employing mixed polyhedral cells, (hexahedral, tetrahedral, etc.) these methods allow more accurate modeling of non-rectangular structures and objects because the traditional stair-stepped boundary approximations associated with the orthogonal grid based finite difference methods can be avoided. Numerical results demonstrating the accuracy of these new methods are presented.

Comparison of GEOS-5 AGCM Planetary Boundary Layer Depths Computed with Various Definitions

NASA Technical Reports Server (NTRS)

Mcgrath-Spangler, E. L.; Molod, A.

2014-01-01

Accurate models of planetary boundary layer (PBL) processes are important for forecasting weather and climate. The present study compares seven methods of calculating PBL depth in the GEOS-5 atmospheric general circulation model (AGCM) over land. These methods depend on the eddy diffusion coefficients, bulk and local Richardson numbers, and the turbulent kinetic energy. The computed PBL depths are aggregated to the Koppen climate classes, and some limited comparisons are made using radiosonde profiles. Most methods produce similar midday PBL depths, although in the warm, moist climate classes, the bulk Richardson number method gives midday results that are lower than those given by the eddy diffusion coefficient methods. Additional analysis revealed that methods sensitive to turbulence driven by radiative cooling produce greater PBL depths, this effect being most significant during the evening transition. Nocturnal PBLs based on Richardson number are generally shallower than eddy diffusion coefficient based estimates. The bulk Richardson number estimate is recommended as the PBL height to inform the choice of the turbulent length scale, based on the similarity to other methods during the day, and the improved nighttime behavior.

Comparison of GEOS-5 AGCM planetary boundary layer depths computed with various definitions

NASA Astrophysics Data System (ADS)

McGrath-Spangler, E. L.; Molod, A.

2014-03-01

Accurate models of planetary boundary layer (PBL) processes are important for forecasting weather and climate. The present study compares seven methods of calculating PBL depth in the GEOS-5 atmospheric general circulation model (AGCM) over land. These methods depend on the eddy diffusion coefficients, bulk and local Richardson numbers, and the turbulent kinetic energy. The computed PBL depths are aggregated to the Köppen climate classes, and some limited comparisons are made using radiosonde profiles. Most methods produce similar midday PBL depths, although in the warm, moist climate classes, the bulk Richardson number method gives midday results that are lower than those given by the eddy diffusion coefficient methods. Additional analysis revealed that methods sensitive to turbulence driven by radiative cooling produce greater PBL depths, this effect being most significant during the evening transition. Nocturnal PBLs based on Richardson number are generally shallower than eddy diffusion coefficient based estimates. The bulk Richardson number estimate is recommended as the PBL height to inform the choice of the turbulent length scale, based on the similarity to other methods during the day, and the improved nighttime behavior.

Multiconstrained gene clustering based on generalized projections

PubMed Central

2010-01-01

Background Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem. Results We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods. Conclusions The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions. PMID:20356386

LANZ: Software solving the large sparse symmetric generalized eigenproblem

NASA Technical Reports Server (NTRS)

Jones, Mark T.; Patrick, Merrell L.

1990-01-01

A package, LANZ, for solving the large symmetric generalized eigenproblem is described. The package was tested on four different architectures: Convex 200, CRAY Y-MP, Sun-3, and Sun-4. The package uses a Lanczos' method and is based on recent research into solving the generalized eigenproblem.

A General Architecture for Intelligent Tutoring of Diagnostic Classification Problem Solving

PubMed Central

Crowley, Rebecca S.; Medvedeva, Olga

2003-01-01

We report on a general architecture for creating knowledge-based medical training systems to teach diagnostic classification problem solving. The approach is informed by our previous work describing the development of expertise in classification problem solving in Pathology. The architecture envelops the traditional Intelligent Tutoring System design within the Unified Problem-solving Method description Language (UPML) architecture, supporting component modularity and reuse. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. We outline the advantages and limitations of this general approach, and describe it’s implementation in SlideTutor–a developing Intelligent Tutoring System in Dermatopathology. PMID:14728159

Team-Based Learning Reduces Attrition in a First-Semester General Chemistry Course

ERIC Educational Resources Information Center

Comeford, Lorrie

2016-01-01

Team-based learning (TBL) is an instructional method that has been shown to reduce attrition and increase student learning in a number of disciplines. TBL was implemented in a first-semester general chemistry course, and its effect on attrition was assessed. Attrition from sections before implementing TBL (fall 2008 to fall 2009) was compared with…

Infrared and Raman Spectroscopy: A Discovery-Based Activity for the General Chemistry Curriculum

ERIC Educational Resources Information Center

Borgsmiller, Karen L.; O'Connell, Dylan J.; Klauenberg, Kathryn M.; Wilson, Peter M.; Stromberg, Christopher J.

2012-01-01

A discovery-based method is described for incorporating the concepts of IR and Raman spectroscopy into the general chemistry curriculum. Students use three sets of springs to model the properties of single, double, and triple covalent bonds. Then, Gaussian 03W molecular modeling software is used to illustrate the relationship between bond…

General method to find the attractors of discrete dynamic models of biological systems.

PubMed

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

General method to find the attractors of discrete dynamic models of biological systems

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

Assessment of parametric uncertainty for groundwater reactive transport modeling,

USGS Publications Warehouse

Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun

2014-01-01

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Generalized analog thresholding for spike acquisition at ultralow sampling rates

PubMed Central

He, Bryan D.; Wein, Alex; Varshney, Lav R.; Kusuma, Julius; Richardson, Andrew G.

2015-01-01

Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. PMID:25904712

Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2014-02-01

Inverse parameter estimation of process-based models is a long-standing problem in many scientific disciplines. A key question for inverse parameter estimation is how to define the metric that quantifies how well model predictions fit to the data. This metric can be expressed by general cost or objective functions, but statistical inversion methods require a particular metric, the probability of observing the data given the model parameters, known as the likelihood. For technical and computational reasons, likelihoods for process-based stochastic models are usually based on general assumptions about variability in the observed data, and not on the stochasticity generated by the model. Only in recent years have new methods become available that allow the generation of likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional Markov chain Monte Carlo (MCMC) sampler, performs well in retrieving known parameter values from virtual inventory data generated by the forest model. We analyze the results of the parameter estimation, examine its sensitivity to the choice and aggregation of model outputs and observed data (summary statistics), and demonstrate the application of this method by fitting the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss how this approach differs from approximate Bayesian computation (ABC), another method commonly used to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can be successfully applied to process-based models of high complexity. The methodology is particularly suitable for heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models.

Efficient segmentation of 3D fluoroscopic datasets from mobile C-arm

NASA Astrophysics Data System (ADS)

Styner, Martin A.; Talib, Haydar; Singh, Digvijay; Nolte, Lutz-Peter

2004-05-01

The emerging mobile fluoroscopic 3D technology linked with a navigation system combines the advantages of CT-based and C-arm-based navigation. The intra-operative, automatic segmentation of 3D fluoroscopy datasets enables the combined visualization of surgical instruments and anatomical structures for enhanced planning, surgical eye-navigation and landmark digitization. We performed a thorough evaluation of several segmentation algorithms using a large set of data from different anatomical regions and man-made phantom objects. The analyzed segmentation methods include automatic thresholding, morphological operations, an adapted region growing method and an implicit 3D geodesic snake method. In regard to computational efficiency, all methods performed within acceptable limits on a standard Desktop PC (30sec-5min). In general, the best results were obtained with datasets from long bones, followed by extremities. The segmentations of spine, pelvis and shoulder datasets were generally of poorer quality. As expected, the threshold-based methods produced the worst results. The combined thresholding and morphological operations methods were considered appropriate for a smaller set of clean images. The region growing method performed generally much better in regard to computational efficiency and segmentation correctness, especially for datasets of joints, and lumbar and cervical spine regions. The less efficient implicit snake method was able to additionally remove wrongly segmented skin tissue regions. This study presents a step towards efficient intra-operative segmentation of 3D fluoroscopy datasets, but there is room for improvement. Next, we plan to study model-based approaches for datasets from the knee and hip joint region, which would be thenceforth applied to all anatomical regions in our continuing development of an ideal segmentation procedure for 3D fluoroscopic images.

Detection of S-Nitrosothiols

PubMed Central

Diers, Anne R.; Keszler, Agnes; Hogg, Neil

2015-01-01

BACKGROUND S-Nitrosothiols have been recognized as biologically-relevant products of nitric oxide that are involved in many of the diverse activities of this free radical. SCOPE OF REVIEW This review serves to discuss current methods for the detection and analysis of protein S-nitrosothiols. The major methods of S-nitrosothiol detection include chemiluminescence-based methods and switch-based methods, each of which comes in various flavors with advantages and caveats. MAJOR CONCLUSIONS The detection of S-nitrosothiols is challenging and prone to many artifacts. Accurate measurements require an understanding of the underlying chemistry of the methods involved and the use of appropriate controls. GENERAL SIGNIFICANCE Nothing is more important to a field of research than robust methodology that is generally trusted. The field of S-Nitrosation has developed such methods but, as S-nitrosothiols are easy to introduce as artifacts, it is vital that current users learn from the lessons of the past. PMID:23988402

Guided SAR image despeckling with probabilistic non local weights

NASA Astrophysics Data System (ADS)

Gokul, Jithin; Nair, Madhu S.; Rajan, Jeny

2017-12-01

SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method.

Dereverberation and denoising based on generalized spectral subtraction by multi-channel LMS algorithm using a small-scale microphone array

NASA Astrophysics Data System (ADS)

Wang, Longbiao; Odani, Kyohei; Kai, Atsuhiko

2012-12-01

A blind dereverberation method based on power spectral subtraction (SS) using a multi-channel least mean squares algorithm was previously proposed to suppress the reverberant speech without additive noise. The results of isolated word speech recognition experiments showed that this method achieved significant improvements over conventional cepstral mean normalization (CMN) in a reverberant environment. In this paper, we propose a blind dereverberation method based on generalized spectral subtraction (GSS), which has been shown to be effective for noise reduction, instead of power SS. Furthermore, we extend the missing feature theory (MFT), which was initially proposed to enhance the robustness of additive noise, to dereverberation. A one-stage dereverberation and denoising method based on GSS is presented to simultaneously suppress both the additive noise and nonstationary multiplicative noise (reverberation). The proposed dereverberation method based on GSS with MFT is evaluated on a large vocabulary continuous speech recognition task. When the additive noise was absent, the dereverberation method based on GSS with MFT using only 2 microphones achieves a relative word error reduction rate of 11.4 and 32.6% compared to the dereverberation method based on power SS and the conventional CMN, respectively. For the reverberant and noisy speech, the dereverberation and denoising method based on GSS achieves a relative word error reduction rate of 12.8% compared to the conventional CMN with GSS-based additive noise reduction method. We also analyze the effective factors of the compensation parameter estimation for the dereverberation method based on SS, such as the number of channels (the number of microphones), the length of reverberation to be suppressed, and the length of the utterance used for parameter estimation. The experimental results showed that the SS-based method is robust in a variety of reverberant environments for both isolated and continuous speech recognition and under various parameter estimation conditions.

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.

PubMed

Zhang, Zhaoyang; Fang, Hua; Wang, Honggang

2016-06-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services.

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

PubMed Central

Zhang, Zhaoyang; Wang, Honggang

2016-01-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering is more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services. PMID:27126063

Real-time cartesian force feedback control of a teleoperated robot

NASA Technical Reports Server (NTRS)

Campbell, Perry

1989-01-01

Active cartesian force control of a teleoperated robot is investigated. An economical microcomputer based control method was tested. Limitations are discussed and methods of performance improvement suggested. To demonstrate the performance of this technique, a preliminary test was performed with success. A general purpose bilateral force reflecting hand controller is currently being constructed based on this control method.

Statistical study of generalized nonlinear phase step estimation methods in phase-shifting interferometry.

PubMed

Langoju, Rajesh; Patil, Abhijit; Rastogi, Pramod

2007-11-20

Signal processing methods based on maximum-likelihood theory, discrete chirp Fourier transform, and spectral estimation methods have enabled accurate measurement of phase in phase-shifting interferometry in the presence of nonlinear response of the piezoelectric transducer to the applied voltage. We present the statistical study of these generalized nonlinear phase step estimation methods to identify the best method by deriving the Cramér-Rao bound. We also address important aspects of these methods for implementation in practical applications and compare the performance of the best-identified method with other bench marking algorithms in the presence of harmonics and noise.

Evaluation of Alternative Altitude Scaling Methods for Thermal Ice Protection System in NASA Icing Research Tunnel

NASA Technical Reports Server (NTRS)

Lee, Sam; Addy, Harold; Broeren, Andy P.; Orchard, David M.

2017-01-01

A test was conducted at NASA Icing Research Tunnel to evaluate altitude scaling methods for thermal ice protection system. Two scaling methods based on Weber number were compared against a method based on the Reynolds number. The results generally agreed with the previous set of tests conducted in NRCC Altitude Icing Wind Tunnel. The Weber number based scaling methods resulted in smaller runback ice mass than the Reynolds number based scaling method. The ice accretions from the Weber number based scaling method also formed farther upstream. However there were large differences in the accreted ice mass between the two Weber number based scaling methods. The difference became greater when the speed was increased. This indicated that there may be some Reynolds number effects that isnt fully accounted for and warrants further study.

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

PubMed

Kim, Ju-Won; Park, Seunghee

2018-01-02

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

A full vectorial generalized discontinuous Galerkin beam propagation method (GDG-BPM) for nonsmooth electromagnetic fields in waveguides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan Kai; Cai Wei; Ji Xia

2008-07-20

In this paper, we propose a new full vectorial generalized discontinuous Galerkin beam propagation method (GDG-BPM) to accurately handle the discontinuities in electromagnetic fields associated with wave propagations in inhomogeneous optical waveguides. The numerical method is a combination of the traditional beam propagation method (BPM) with a newly developed generalized discontinuous Galerkin (GDG) method [K. Fan, W. Cai, X. Ji, A generalized discontinuous Galerkin method (GDG) for Schroedinger equations with nonsmooth solutions, J. Comput. Phys. 227 (2008) 2387-2410]. The GDG method is based on a reformulation, using distributional variables to account for solution jumps across material interfaces, of Schroedinger equationsmore » resulting from paraxial approximations of vector Helmholtz equations. Four versions of the GDG-BPM are obtained for either the electric or magnetic field components. Modeling of wave propagations in various optical fibers using the full vectorial GDG-BPM is included. Numerical results validate the high order accuracy and the flexibility of the method for various types of interface jump conditions.« less

Extreme learning machine for ranking: generalization analysis and applications.

PubMed

Chen, Hong; Peng, Jiangtao; Zhou, Yicong; Li, Luoqing; Pan, Zhibin

2014-05-01

The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM. The generalization analysis is established for the ELM-based ranking (ELMRank) in terms of the covering numbers of hypothesis space. Empirical results on the benchmark datasets show the competitive performance of the ELMRank over the state-of-the-art ranking methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Survey of methods for calculating sensitivity of general eigenproblems

NASA Technical Reports Server (NTRS)

Murthy, Durbha V.; Haftka, Raphael T.

1987-01-01

A survey of methods for sensitivity analysis of the algebraic eigenvalue problem for non-Hermitian matrices is presented. In addition, a modification of one method based on a better normalizing condition is proposed. Methods are classified as Direct or Adjoint and are evaluated for efficiency. Operation counts are presented in terms of matrix size, number of design variables and number of eigenvalues and eigenvectors of interest. The effect of the sparsity of the matrix and its derivatives is also considered, and typical solution times are given. General guidelines are established for the selection of the most efficient method.

Frequency standards based on ultracold atoms in tests of general relativity, navigation and gravimetry

NASA Astrophysics Data System (ADS)

Khabarova, K. Yu.; Kudeyarov, K. S.; Kolachevsky, N. N.

2017-06-01

Research and development in the field of optical clocks based on ultracold atoms and ions have enabled the relative uncertainty in frequency to be reduced down to a few parts in 1018. The use of novel, precise frequency comparison methods opens up new possibilities for basic research (sensitive tests of general relativity, a search for a drift of fundamental constants and a search for ‘dark matter’) as well as for state-of-the-art navigation and gravimetry. We discuss the key methods that are used in creating precision clocks (including transportable clocks) based on ultracold atoms and ions and the feasibility of using them in resolving current relativistic gravimetry issues.

Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Zheng, Jinde; Pan, Haiyang; Yang, Shubao; Cheng, Junsheng

2018-01-01

Multiscale permutation entropy (MPE) is a recently proposed nonlinear dynamic method for measuring the randomness and detecting the nonlinear dynamic change of time series and can be used effectively to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing. To solve the drawback of coarse graining process in MPE, an improved MPE method called generalized composite multiscale permutation entropy (GCMPE) was proposed in this paper. Also the influence of parameters on GCMPE and its comparison with the MPE are studied by analyzing simulation data. GCMPE was applied to the fault feature extraction from vibration signal of rolling bearing and then based on the GCMPE, Laplacian score for feature selection and the Particle swarm optimization based support vector machine, a new fault diagnosis method for rolling bearing was put forward in this paper. Finally, the proposed method was applied to analyze the experimental data of rolling bearing. The analysis results show that the proposed method can effectively realize the fault diagnosis of rolling bearing and has a higher fault recognition rate than the existing methods.

Generalized Buneman Pruning for Inferring the Most Parsimonious Multi-state Phylogeny

NASA Astrophysics Data System (ADS)

Misra, Navodit; Blelloch, Guy; Ravi, R.; Schwartz, Russell

Accurate reconstruction of phylogenies remains a key challenge in evolutionary biology. Most biologically plausible formulations of the problem are formally NP-hard, with no known efficient solution. The standard in practice are fast heuristic methods that are empirically known to work very well in general, but can yield results arbitrarily far from optimal. Practical exact methods, which yield exponential worst-case running times but generally much better times in practice, provide an important alternative. We report progress in this direction by introducing a provably optimal method for the weighted multi-state maximum parsimony phylogeny problem. The method is based on generalizing the notion of the Buneman graph, a construction key to efficient exact methods for binary sequences, so as to apply to sequences with arbitrary finite numbers of states with arbitrary state transition weights. We implement an integer linear programming (ILP) method for the multi-state problem using this generalized Buneman graph and demonstrate that the resulting method is able to solve data sets that are intractable by prior exact methods in run times comparable with popular heuristics. Our work provides the first method for provably optimal maximum parsimony phylogeny inference that is practical for multi-state data sets of more than a few characters.

System and method for air temperature control in an oxygen transport membrane based reactor

DOEpatents

Kelly, Sean M

2016-09-27

A system and method for air temperature control in an oxygen transport membrane based reactor is provided. The system and method involves introducing a specific quantity of cooling air or trim air in between stages in a multistage oxygen transport membrane based reactor or furnace to maintain generally consistent surface temperatures of the oxygen transport membrane elements and associated reactors. The associated reactors may include reforming reactors, boilers or process gas heaters.

System and method for temperature control in an oxygen transport membrane based reactor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kelly, Sean M.

A system and method for temperature control in an oxygen transport membrane based reactor is provided. The system and method involves introducing a specific quantity of cooling air or trim air in between stages in a multistage oxygen transport membrane based reactor or furnace to maintain generally consistent surface temperatures of the oxygen transport membrane elements and associated reactors. The associated reactors may include reforming reactors, boilers or process gas heaters.

Applications of Small Area Estimation to Generalization with Subclassification by Propensity Scores

ERIC Educational Resources Information Center

Chan, Wendy

2018-01-01

Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score-based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods…

School-Based Management: An Approach to Decision-Making Quality in Egyptian General Secondary Schools

ERIC Educational Resources Information Center

Elmelegy, Reda Ibrahim

2015-01-01

The current research aims at clarifying how school-based management (SBM) can contribute to achieve the decision-making quality in Egyptian general secondary schools and determine the requirements of quality decision-making. It depends on the descriptive method in order to acknowledge the basics of the SBM and its relationship with the quality of…

The unbiasedness of a generalized mirage boundary correction method for Monte Carlo integration estimators of volume

Treesearch

Thomas B. Lynch; Jeffrey H. Gove

2014-01-01

The typical "double counting" application of the mirage method of boundary correction cannot be applied to sampling systems such as critical height sampling (CHS) that are based on a Monte Carlo sample of a tree (or debris) attribute because the critical height (or other random attribute) sampled from a mirage point is generally not equal to the critical...

An analysis of general chain systems

NASA Technical Reports Server (NTRS)

Passerello, C. E.; Huston, R. L.

1972-01-01

A general analysis of dynamic systems consisting of connected rigid bodies is presented. The number of bodies and their manner of connection is arbitrary so long as no closed loops are formed. The analysis represents a dynamic finite element method, which is computer-oriented and designed so that nonworking, interval constraint forces are automatically eliminated. The method is based upon Lagrange's form of d'Alembert's principle. Shifter matrix transformations are used with the geometrical aspects of the analysis. The method is illustrated with a space manipulator.

Secure optical generalized filter bank multi-carrier system based on cubic constellation masked method.

PubMed

Zhang, Lijia; Liu, Bo; Xin, Xiangjun

2015-06-15

A secure optical generalized filter bank multi-carrier (GFBMC) system with carrier-less amplitude-phase (CAP) modulation is proposed in this Letter. The security is realized through cubic constellation-masked method. Large key space and more flexibility masking can be obtained by cubic constellation masking aligning with the filter bank. An experiment of 18 Gb/s encrypted GFBMC/CAP system with 25-km single-mode fiber transmission is performed to demonstrate the feasibility of the proposed method.

Comparison of modeling methods to predict the spatial distribution of deep-sea coral and sponge in the Gulf of Alaska

NASA Astrophysics Data System (ADS)

Rooper, Christopher N.; Zimmermann, Mark; Prescott, Megan M.

2017-08-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska's marine waters, and are associated with many different species of fishes and invertebrates. These ecosystems are vulnerable to the effects of commercial fishing activities and climate change. We compared four commonly used species distribution models (general linear models, generalized additive models, boosted regression trees and random forest models) and an ensemble model to predict the presence or absence and abundance of six groups of benthic invertebrate taxa in the Gulf of Alaska. All four model types performed adequately on training data for predicting presence and absence, with regression forest models having the best overall performance measured by the area under the receiver-operating-curve (AUC). The models also performed well on the test data for presence and absence with average AUCs ranging from 0.66 to 0.82. For the test data, ensemble models performed the best. For abundance data, there was an obvious demarcation in performance between the two regression-based methods (general linear models and generalized additive models), and the tree-based models. The boosted regression tree and random forest models out-performed the other models by a wide margin on both the training and testing data. However, there was a significant drop-off in performance for all models of invertebrate abundance ( 50%) when moving from the training data to the testing data. Ensemble model performance was between the tree-based and regression-based methods. The maps of predictions from the models for both presence and abundance agreed very well across model types, with an increase in variability in predictions for the abundance data. We conclude that where data conforms well to the modeled distribution (such as the presence-absence data and binomial distribution in this study), the four types of models will provide similar results, although the regression-type models may be more consistent with biological theory. For data with highly zero-inflated distributions and non-normal distributions such as the abundance data from this study, the tree-based methods performed better. Ensemble models that averaged predictions across the four model types, performed better than the GLM or GAM models but slightly poorer than the tree-based methods, suggesting ensemble models might be more robust to overfitting than tree methods, while mitigating some of the disadvantages in predictive performance of regression methods.

Finite basis representations with nondirect product basis functions having structure similar to that of spherical harmonics.

PubMed

Czakó, Gábor; Szalay, Viktor; Császár, Attila G

2006-01-07

The currently most efficient finite basis representation (FBR) method [Corey et al., in Numerical Grid Methods and Their Applications to Schrodinger Equation, NATO ASI Series C, edited by C. Cerjan (Kluwer Academic, New York, 1993), Vol. 412, p. 1; Bramley et al., J. Chem. Phys. 100, 6175 (1994)] designed specifically to deal with nondirect product bases of structures phi(n) (l)(s)f(l)(u), chi(m) (l)(t)phi(n) (l)(s)f(l)(u), etc., employs very special l-independent grids and results in a symmetric FBR. While highly efficient, this method is not general enough. For instance, it cannot deal with nondirect product bases of the above structure efficiently if the functions phi(n) (l)(s) [and/or chi(m) (l)(t)] are discrete variable representation (DVR) functions of the infinite type. The optimal-generalized FBR(DVR) method [V. Szalay, J. Chem. Phys. 105, 6940 (1996)] is designed to deal with general, i.e., direct and/or nondirect product, bases and grids. This robust method, however, is too general, and its direct application can result in inefficient computer codes [Czako et al., J. Chem. Phys. 122, 024101 (2005)]. It is shown here how the optimal-generalized FBR method can be simplified in the case of nondirect product bases of structures phi(n) (l)(s)f(l)(u), chi(m) (l)(t)phi(n) (l)(s)f(l)(u), etc. As a result the commonly used symmetric FBR is recovered and simplified nonsymmetric FBRs utilizing very special l-dependent grids are obtained. The nonsymmetric FBRs are more general than the symmetric FBR in that they can be employed efficiently even when the functions phi(n) (l)(s) [and/or chi(m) (l)(t)] are DVR functions of the infinite type. Arithmetic operation counts and a simple numerical example presented show unambiguously that setting up the Hamiltonian matrix requires significantly less computer time when using one of the proposed nonsymmetric FBRs than that in the symmetric FBR. Therefore, application of this nonsymmetric FBR is more efficient than that of the symmetric FBR when one wants to diagonalize the Hamiltonian matrix either by a direct or via a basis-set contraction method. Enormous decrease of computer time can be achieved, with respect to a direct application of the optimal-generalized FBR, by employing one of the simplified nonsymmetric FBRs as is demonstrated in noniterative calculations of the low-lying vibrational energy levels of the H3+ molecular ion. The arithmetic operation counts of the Hamiltonian matrix vector products and the properties of a recently developed diagonalization method [Andreozzi et al., J. Phys. A Math. Gen. 35, L61 (2002)] suggest that the nonsymmetric FBR applied along with this particular diagonalization method is suitable to large scale iterative calculations. Whether or not the nonsymmetric FBR is competitive with the symmetric FBR in large-scale iterative calculations still has to be investigated numerically.

An Ellipsoidal Particle-Finite Element Method for Hypervelocity Impact Simulation. Chapter 1

NASA Technical Reports Server (NTRS)

Shivarama, Ravishankar; Fahrenthold, Eric P.

2004-01-01

A number of coupled particle-element and hybrid particle-element methods have been developed for the simulation of hypervelocity impact problems, to avoid certain disadvantages associated with the use of pure continuum based or pure particle based methods. To date these methods have employed spherical particles. In recent work a hybrid formulation has been extended to the ellipsoidal particle case. A model formulation approach based on Lagrange's equations, with particles entropies serving as generalized coordinates, avoids the angular momentum conservation problems which have been reported with ellipsoidal smooth particle hydrodynamics models.

Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.

2000-01-01

The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.

Generalized sample entropy analysis for traffic signals based on similarity measure

NASA Astrophysics Data System (ADS)

Shang, Du; Xu, Mengjia; Shang, Pengjian

2017-05-01

Sample entropy is a prevailing method used to quantify the complexity of a time series. In this paper a modified method of generalized sample entropy and surrogate data analysis is proposed as a new measure to assess the complexity of a complex dynamical system such as traffic signals. The method based on similarity distance presents a different way of signals patterns match showing distinct behaviors of complexity. Simulations are conducted over synthetic data and traffic signals for providing the comparative study, which is provided to show the power of the new method. Compared with previous sample entropy and surrogate data analysis, the new method has two main advantages. The first one is that it overcomes the limitation about the relationship between the dimension parameter and the length of series. The second one is that the modified sample entropy functions can be used to quantitatively distinguish time series from different complex systems by the similar measure.

Terminology model discovery using natural language processing and visualization techniques.

PubMed

Zhou, Li; Tao, Ying; Cimino, James J; Chen, Elizabeth S; Liu, Hongfang; Lussier, Yves A; Hripcsak, George; Friedman, Carol

2006-12-01

Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.

Solving Graph Laplacian Systems Through Recursive Bisections and Two-Grid Preconditioning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ponce, Colin; Vassilevski, Panayot S.

2016-02-18

We present a parallelizable direct method for computing the solution to graph Laplacian-based linear systems derived from graphs that can be hierarchically bipartitioned with small edge cuts. For a graph of size n with constant-size edge cuts, our method decomposes a graph Laplacian in time O(n log n), and then uses that decomposition to perform a linear solve in time O(n log n). We then use the developed technique to design a preconditioner for graph Laplacians that do not have this property. Finally, we augment this preconditioner with a two-grid method that accounts for much of the preconditioner's weaknesses. Wemore » present an analysis of this method, as well as a general theorem for the condition number of a general class of two-grid support graph-based preconditioners. Numerical experiments illustrate the performance of the studied methods.« less

A Review of Depth and Normal Fusion Algorithms

PubMed Central

Štolc, Svorad; Pock, Thomas

2018-01-01

Geometric surface information such as depth maps and surface normals can be acquired by various methods such as stereo light fields, shape from shading and photometric stereo techniques. We compare several algorithms which deal with the combination of depth with surface normal information in order to reconstruct a refined depth map. The reasons for performance differences are examined from the perspective of alternative formulations of surface normals for depth reconstruction. We review and analyze methods in a systematic way. Based on our findings, we introduce a new generalized fusion method, which is formulated as a least squares problem and outperforms previous methods in the depth error domain by introducing a novel normal weighting that performs closer to the geodesic distance measure. Furthermore, a novel method is introduced based on Total Generalized Variation (TGV) which further outperforms previous approaches in terms of the geodesic normal distance error and maintains comparable quality in the depth error domain. PMID:29389903

Reliability-based design optimization using a generalized subset simulation method and posterior approximation

NASA Astrophysics Data System (ADS)

Ma, Yuan-Zhuo; Li, Hong-Shuang; Yao, Wei-Xing

2018-05-01

The evaluation of the probabilistic constraints in reliability-based design optimization (RBDO) problems has always been significant and challenging work, which strongly affects the performance of RBDO methods. This article deals with RBDO problems using a recently developed generalized subset simulation (GSS) method and a posterior approximation approach. The posterior approximation approach is used to transform all the probabilistic constraints into ordinary constraints as in deterministic optimization. The assessment of multiple failure probabilities required by the posterior approximation approach is achieved by GSS in a single run at all supporting points, which are selected by a proper experimental design scheme combining Sobol' sequences and Bucher's design. Sequentially, the transformed deterministic design optimization problem can be solved by optimization algorithms, for example, the sequential quadratic programming method. Three optimization problems are used to demonstrate the efficiency and accuracy of the proposed method.

Zubarev's Nonequilibrium Statistical Operator Method in the Generalized Statistics of Multiparticle Systems

NASA Astrophysics Data System (ADS)

Glushak, P. A.; Markiv, B. B.; Tokarchuk, M. V.

2018-01-01

We present a generalization of Zubarev's nonequilibrium statistical operator method based on the principle of maximum Renyi entropy. In the framework of this approach, we obtain transport equations for the basic set of parameters of the reduced description of nonequilibrium processes in a classical system of interacting particles using Liouville equations with fractional derivatives. For a classical systems of particles in a medium with a fractal structure, we obtain a non-Markovian diffusion equation with fractional spatial derivatives. For a concrete model of the frequency dependence of a memory function, we obtain generalized Kettano-type diffusion equation with the spatial and temporal fractality taken into account. We present a generalization of nonequilibrium thermofield dynamics in Zubarev's nonequilibrium statistical operator method in the framework of Renyi statistics.

αAMG based on Weighted Matching for Systems of Elliptic PDEs Arising From Displacement and Mixed Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

D'Ambra, P.; Vassilevski, P. S.

2014-05-30

Adaptive Algebraic Multigrid (or Multilevel) Methods (αAMG) are introduced to improve robustness and efficiency of classical algebraic multigrid methods in dealing with problems where no a-priori knowledge or assumptions on the near-null kernel of the underlined matrix are available. Recently we proposed an adaptive (bootstrap) AMG method, αAMG, aimed to obtain a composite solver with a desired convergence rate. Each new multigrid component relies on a current (general) smooth vector and exploits pairwise aggregation based on weighted matching in a matrix graph to define a new automatic, general-purpose coarsening process, which we refer to as “the compatible weighted matching”. Inmore » this work, we present results that broaden the applicability of our method to different finite element discretizations of elliptic PDEs. In particular, we consider systems arising from displacement methods in linear elasticity problems and saddle-point systems that appear in the application of the mixed method to Darcy problems.« less

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

NASA Astrophysics Data System (ADS)

Jia, Rui-Sheng; Gong, Yue; Peng, Yan-Jun; Sun, Hong-Mei; Zhang, Xing-Li; Lu, Xin-Ming

2018-05-01

Microseismic signals are generally considered to follow the Gauss distribution. A comparison of the dynamic characteristics of sample variance and the symmetry of microseismic signals with the signals which follow α-stable distribution reveals that the microseismic signals have obvious pulse characteristics and that the probability density curve of the microseismic signal is approximately symmetric. Thus, the hypothesis that microseismic signals follow the symmetric α-stable distribution is proposed. On the premise of this hypothesis, the characteristic exponent α of the microseismic signals is obtained by utilizing the fractional low-order statistics, and then a new method of time difference of arrival (TDOA) estimation of microseismic signals based on fractional low-order covariance (FLOC) is proposed. Upon applying this method to the TDOA estimation of Ricker wavelet simulation signals and real microseismic signals, experimental results show that the FLOC method, which is based on the assumption of the symmetric α-stable distribution, leads to enhanced spatial resolution of the TDOA estimation relative to the generalized cross correlation (GCC) method, which is based on the assumption of the Gaussian distribution.

Generalized nonequilibrium vertex correction method in coherent medium theory for quantum transport simulation of disordered nanoelectronics

NASA Astrophysics Data System (ADS)

Yan, Jiawei; Ke, Youqi

2016-07-01

Electron transport properties of nanoelectronics can be significantly influenced by the inevitable and randomly distributed impurities/defects. For theoretical simulation of disordered nanoscale electronics, one is interested in both the configurationally averaged transport property and its statistical fluctuation that tells device-to-device variability induced by disorder. However, due to the lack of an effective method to do disorder averaging under the nonequilibrium condition, the important effects of disorders on electron transport remain largely unexplored or poorly understood. In this work, we report a general formalism of Green's function based nonequilibrium effective medium theory to calculate the disordered nanoelectronics. In this method, based on a generalized coherent potential approximation for the Keldysh nonequilibrium Green's function, we developed a generalized nonequilibrium vertex correction method to calculate the average of a two-Keldysh-Green's-function correlator. We obtain nine nonequilibrium vertex correction terms, as a complete family, to express the average of any two-Green's-function correlator and find they can be solved by a set of linear equations. As an important result, the averaged nonequilibrium density matrix, averaged current, disorder-induced current fluctuation, and averaged shot noise, which involve different two-Green's-function correlators, can all be derived and computed in an effective and unified way. To test the general applicability of this method, we applied it to compute the transmission coefficient and its fluctuation with a square-lattice tight-binding model and compared with the exact results and other previously proposed approximations. Our results show very good agreement with the exact results for a wide range of disorder concentrations and energies. In addition, to incorporate with density functional theory to realize first-principles quantum transport simulation, we have also derived a general form of conditionally averaged nonequilibrium Green's function for multicomponent disorders.

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability.

PubMed

Yeung, Dit-Yan; Chang, Hong; Dai, Guang

2008-11-01

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.

VOFTools - A software package of calculation tools for volume of fluid methods using general convex grids

NASA Astrophysics Data System (ADS)

López, J.; Hernández, J.; Gómez, P.; Faura, F.

2018-02-01

The VOFTools library includes efficient analytical and geometrical routines for (1) area/volume computation, (2) truncation operations that typically arise in VOF (volume of fluid) methods, (3) area/volume conservation enforcement (VCE) in PLIC (piecewise linear interface calculation) reconstruction and(4) computation of the distance from a given point to the reconstructed interface. The computation of a polyhedron volume uses an efficient formula based on a quadrilateral decomposition and a 2D projection of each polyhedron face. The analytical VCE method is based on coupling an interpolation procedure to bracket the solution with an improved final calculation step based on the above volume computation formula. Although the library was originally created to help develop highly accurate advection and reconstruction schemes in the context of VOF methods, it may have more general applications. To assess the performance of the supplied routines, different tests, which are provided in FORTRAN and C, were implemented for several 2D and 3D geometries.

A general strategy for hybrid thin film fabrication and transfer onto arbitrary substrates.

PubMed

Zhang, Yong; Magan, John J; Blau, Werner J

2014-04-28

The development of thin film-based structures/devices often requires thin films to be transferred onto arbitrary substrates/surfaces. Controllable and non-destructive transfer method, although highly desired, remains quite challenging. Here we report a general method for fabrication and transfer of hybrid (ultra)thin films. The proposed solution-based in-situ transfer method shows not only its robust ability for thin film transfer onto arbitrary substrates but also its highly controlled and non-destructive characteristic. With a hole structure as the support, fully-stretched free-standing thin film is prepared. The successful transfer to a curved surface demonstrates the possibility for production of thin film-coated complex optical components. Ultrathin (35 nm) hybrid film transferred onto PET (50 μm thick) shows high transparency (>90% in visible range), conductivity (1.54 × 10(4) S/m), and flexibility (radius of curvature down to mm scale). The reported transfer method would provide a powerful route towards complex thin film-based structures/devices.

A General Strategy for Hybrid Thin Film Fabrication and Transfer onto Arbitrary Substrates

PubMed Central

Zhang, Yong; Magan, John J.; Blau, Werner J.

2014-01-01

The development of thin film-based structures/devices often requires thin films to be transferred onto arbitrary substrates/surfaces. Controllable and non-destructive transfer method, although highly desired, remains quite challenging. Here we report a general method for fabrication and transfer of hybrid (ultra)thin films. The proposed solution-based in-situ transfer method shows not only its robust ability for thin film transfer onto arbitrary substrates but also its highly controlled and non-destructive characteristic. With a hole structure as the support, fully-stretched free-standing thin film is prepared. The successful transfer to a curved surface demonstrates the possibility for production of thin film-coated complex optical components. Ultrathin (35 nm) hybrid film transferred onto PET (50 μm thick) shows high transparency (>90% in visible range), conductivity (1.54 × 104 S/m), and flexibility (radius of curvature down to mm scale). The reported transfer method would provide a powerful route towards complex thin film-based structures/devices. PMID:24769689

The generalized scattering coefficient method for plane wave scattering in layered structures

NASA Astrophysics Data System (ADS)

Liu, Yu; Li, Chao; Wang, Huai-Yu; Zhou, Yun-Song

2017-02-01

The generalized scattering coefficient (GSC) method is pedagogically derived and employed to study the scattering of plane waves in homogeneous and inhomogeneous layered structures. The numerical stabilities and accuracies of this method and other commonly used numerical methods are discussed and compared. For homogeneous layered structures, concise scattering formulas with clear physical interpretations and strong numerical stability are obtained by introducing the GSCs. For inhomogeneous layered structures, three numerical methods are employed: the staircase approximation method, the power series expansion method, and the differential equation based on the GSCs. We investigate the accuracies and convergence behaviors of these methods by comparing their predictions to the exact results. The conclusions are as follows. The staircase approximation method has a slow convergence in spite of its simple and intuitive implementation, and a fine stratification within the inhomogeneous layer is required for obtaining accurate results. The expansion method results are sensitive to the expansion order, and the treatment becomes very complicated for relatively complex configurations, which restricts its applicability. By contrast, the GSC-based differential equation possesses a simple implementation while providing fast and accurate results.

Factors concerned with sanitary landfill site selection: General discussion

NASA Technical Reports Server (NTRS)

Graff, W. J.; Stone, L. J.

1972-01-01

A general view of factors affecting site selection for sanitary landfill sites is presented. Examinations were made of operational methods, possible environment pollution, types of waste to be disposed, base and cover materials, and the economics involved in the operation.

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

Evidence based general practice: a retrospective study of interventions in one training practice.

PubMed Central

Gill, P.; Dowell, A. C.; Neal, R. D.; Smith, N.; Heywood, P.; Wilson, A. E.

1996-01-01

OBJECTIVES--To estimate the proportion of interventions in general practice that are based on evidence from clinical trials and to assess the appropriateness of such an evaluation. DESIGN--Retrospective review of case notes. SETTING--One suburban training general practice. SUBJECTS--122 consecutive doctor-patient consultations over two days. MAIN OUTCOME MEASURES--Proportions of interventions based on randomised controlled trials (from literature search with Medline, pharmaceutical databases, and standard textbooks), on convincing non-experimental evidence, and without substantial evidence. RESULTS--21 of the 122 consultations recorded were excluded due to insufficient data; 31 of the interventions were based on randomised controlled trial evidence and 51 based on convincing non-experimental evidence. Hence 82/101 (81%) of interventions were based on evidence meeting our criteria. CONCLUSIONS--Most interventions within general practice are based on evidence from clinical trials, but the methods used in such trials may not be the most appropriate to apply to this setting. PMID:8608291

Generalized Flip-Flop Input Equations Based on a Four-Valued Boolean Algebra

NASA Technical Reports Server (NTRS)

Tucker, Jerry H.; Tapia, Moiez A.

1996-01-01

A procedure is developed for obtaining generalized flip-flop input equations, and a concise method is presented for representing these equations. The procedure is based on solving a four-valued characteristic equation of the flip-flop, and can encompass flip-flops that are too complex to approach intuitively. The technique is presented using Karnaugh maps, but could easily be implemented in software.

New algorithms for solving third- and fifth-order two point boundary value problems based on nonsymmetric generalized Jacobi Petrov–Galerkin method

PubMed Central

Doha, E.H.; Abd-Elhameed, W.M.; Youssri, Y.H.

2014-01-01

Two families of certain nonsymmetric generalized Jacobi polynomials with negative integer indexes are employed for solving third- and fifth-order two point boundary value problems governed by homogeneous and nonhomogeneous boundary conditions using a dual Petrov–Galerkin method. The idea behind our method is to use trial functions satisfying the underlying boundary conditions of the differential equations and the test functions satisfying the dual boundary conditions. The resulting linear systems from the application of our method are specially structured and they can be efficiently inverted. The use of generalized Jacobi polynomials simplify the theoretical and numerical analysis of the method and also leads to accurate and efficient numerical algorithms. The presented numerical results indicate that the proposed numerical algorithms are reliable and very efficient. PMID:26425358

Eigenmode computation of cavities with perturbed geometry using matrix perturbation methods applied on generalized eigenvalue problems

NASA Astrophysics Data System (ADS)

Gorgizadeh, Shahnam; Flisgen, Thomas; van Rienen, Ursula

2018-07-01

Generalized eigenvalue problems are standard problems in computational sciences. They may arise in electromagnetic fields from the discretization of the Helmholtz equation by for example the finite element method (FEM). Geometrical perturbations of the structure under concern lead to a new generalized eigenvalue problems with different system matrices. Geometrical perturbations may arise by manufacturing tolerances, harsh operating conditions or during shape optimization. Directly solving the eigenvalue problem for each perturbation is computationally costly. The perturbed eigenpairs can be approximated using eigenpair derivatives. Two common approaches for the calculation of eigenpair derivatives, namely modal superposition method and direct algebraic methods, are discussed in this paper. Based on the direct algebraic methods an iterative algorithm is developed for efficiently calculating the eigenvalues and eigenvectors of the perturbed geometry from the eigenvalues and eigenvectors of the unperturbed geometry.

48 CFR 1432.102 - Description of contract financing methods.

Code of Federal Regulations, 2010 CFR

2010-10-01

... financing methods. 1432.102 Section 1432.102 Federal Acquisition Regulations System DEPARTMENT OF THE INTERIOR GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING Non-Commercial Item Purchase Financing 1432.102 Description of contract financing methods. Use of progress payments based on a percentage or stage...

48 CFR 432.102 - Description of contract financing methods.

Code of Federal Regulations, 2010 CFR

2010-10-01

... financing methods. 432.102 Section 432.102 Federal Acquisition Regulations System DEPARTMENT OF AGRICULTURE GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING Non-Commercial Item Purchase Financing 432.102 Description of contract financing methods. Progress payments based on a percentage or stage of completion are...

48 CFR 932.102 - Description of contract financing methods.

Code of Federal Regulations, 2010 CFR

2010-10-01

... financing methods. 932.102 Section 932.102 Federal Acquisition Regulations System DEPARTMENT OF ENERGY GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING Non-Commercial Item Purchase Financing 932.102 Description of contract financing methods. (e)(2) Progress payments based on a percentage or stage of...

FIA's volume-to-biomass conversion method (CRM) generally underestimates biomass in comparison to published equations

Treesearch

David. C. Chojnacky

2012-01-01

An update of the Jenkins et al. (2003) biomass estimation equations for North American tree species resulted in 35 generalized equations developed from published equations. These 35 equations, which predict aboveground biomass of individual species grouped according to a taxa classification (based on genus or family and sometimes specific gravity), generally predicted...

The Factor Structure of ADHD in a General Population of Primary School Children

ERIC Educational Resources Information Center

Ullebo, Anne Karin; Breivik, Kyrre; Gillberg, Christopher; Lundervold, Astri J.; Posserud, Maj-Britt

2012-01-01

Objective: To examine whether a bifactor model with a general ADHD factor and domain specific factors of inattention, hyperactivity and impulsivity was supported in a large general population sample of children. We also explored the utility of forming subscales based on the domain-specific factors. Methods: Child mental health questionnaires were…

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Chromatographic and computational assessment of lipophilicity using sum of ranking differences and generalized pair-correlation.

PubMed

Andrić, Filip; Héberger, Károly

2015-02-06

Lipophilicity (logP) represents one of the most studied and most frequently used fundamental physicochemical properties. At present there are several possibilities for its quantitative expression and many of them stems from chromatographic experiments. Numerous attempts have been made to compare different computational methods, chromatographic methods vs. computational approaches, as well as chromatographic methods and direct shake-flask procedure without definite results or these findings are not accepted generally. In the present work numerous chromatographically derived lipophilicity measures in combination with diverse computational methods were ranked and clustered using the novel variable discrimination and ranking approaches based on the sum of ranking differences and the generalized pair correlation method. Available literature logP data measured on HILIC, and classical reversed-phase combining different classes of compounds have been compared with most frequently used multivariate data analysis techniques (principal component and hierarchical cluster analysis) as well as with the conclusions in the original sources. Chromatographic lipophilicity measures obtained under typical reversed-phase conditions outperform the majority of computationally estimated logPs. Oppositely, in the case of HILIC none of the many proposed chromatographic indices overcomes any of the computationally assessed logPs. Only two of them (logkmin and kmin) may be selected as recommended chromatographic lipophilicity measures. Both ranking approaches, sum of ranking differences and generalized pair correlation method, although based on different backgrounds, provides highly similar variable ordering and grouping leading to the same conclusions. Copyright © 2015. Published by Elsevier B.V.

Study on Privacy Protection Algorithm Based on K-Anonymity

NASA Astrophysics Data System (ADS)

FeiFei, Zhao; LiFeng, Dong; Kun, Wang; Yang, Li

Basing on the study of K-Anonymity algorithm in privacy protection issue, this paper proposed a "Degree Priority" method of visiting Lattice nodes on the generalization tree to improve the performance of K-Anonymity algorithm. This paper also proposed a "Two Times K-anonymity" methods to reduce the information loss in the process of K-Anonymity. Finally, we used experimental results to demonstrate the effectiveness of these methods.

Fast Fourier transform-based solution of 2D and 3D magnetization problems in type-II superconductivity

NASA Astrophysics Data System (ADS)

Prigozhin, Leonid; Sokolovsky, Vladimir

2018-05-01

We consider the fast Fourier transform (FFT) based numerical method for thin film magnetization problems (Vestgården and Johansen 2012 Supercond. Sci. Technol. 25 104001), compare it with the finite element methods, and evaluate its accuracy. Proposed modifications of this method implementation ensure stable convergence of iterations and enhance its efficiency. A new method, also based on the FFT, is developed for 3D bulk magnetization problems. This method is based on a magnetic field formulation, different from the popular h-formulation of eddy current problems typically employed with the edge finite elements. The method is simple, easy to implement, and can be used with a general current–voltage relation; its efficiency is illustrated by numerical simulations.

Dielectrophoresis-Based Sample Handling in General-Purpose Programmable Diagnostic Instruments

PubMed Central

Gascoyne, Peter R. C.; Vykoukal, Jody V.

2009-01-01

As the molecular origins of disease are better understood, the need for affordable, rapid, and automated technologies that enable microscale molecular diagnostics has become apparent. Widespread use of microsystems that perform sample preparation and molecular analysis could ensure that the benefits of new biomedical discoveries are realized by a maximum number of people, even those in environments lacking any infrastructure. While progress has been made in developing miniaturized diagnostic systems, samples are generally processed off-device using labor-intensive and time-consuming traditional sample preparation methods. We present the concept of an integrated programmable general-purpose sample analysis processor (GSAP) architecture where raw samples are routed to separation and analysis functional blocks contained within a single device. Several dielectrophoresis-based methods that could serve as the foundation for building GSAP functional blocks are reviewed including methods for cell and particle sorting, cell focusing, cell ac impedance analysis, cell lysis, and the manipulation of molecules and reagent droplets. PMID:19684877

Testing non-inferiority of a new treatment in three-arm clinical trials with binary endpoints.

PubMed

Tang, Nian-Sheng; Yu, Bin; Tang, Man-Lai

2014-12-18

A two-arm non-inferiority trial without a placebo is usually adopted to demonstrate that an experimental treatment is not worse than a reference treatment by a small pre-specified non-inferiority margin due to ethical concerns. Selection of the non-inferiority margin and establishment of assay sensitivity are two major issues in the design, analysis and interpretation for two-arm non-inferiority trials. Alternatively, a three-arm non-inferiority clinical trial including a placebo is usually conducted to assess the assay sensitivity and internal validity of a trial. Recently, some large-sample approaches have been developed to assess the non-inferiority of a new treatment based on the three-arm trial design. However, these methods behave badly with small sample sizes in the three arms. This manuscript aims to develop some reliable small-sample methods to test three-arm non-inferiority. Saddlepoint approximation, exact and approximate unconditional, and bootstrap-resampling methods are developed to calculate p-values of the Wald-type, score and likelihood ratio tests. Simulation studies are conducted to evaluate their performance in terms of type I error rate and power. Our empirical results show that the saddlepoint approximation method generally behaves better than the asymptotic method based on the Wald-type test statistic. For small sample sizes, approximate unconditional and bootstrap-resampling methods based on the score test statistic perform better in the sense that their corresponding type I error rates are generally closer to the prespecified nominal level than those of other test procedures. Both approximate unconditional and bootstrap-resampling test procedures based on the score test statistic are generally recommended for three-arm non-inferiority trials with binary outcomes.

A high speed model-based approach for wavefront sensorless adaptive optics systems

NASA Astrophysics Data System (ADS)

Lianghua, Wen; Yang, Ping; Shuai, Wang; Wenjing, Liu; Shanqiu, Chen; Xu, Bing

2018-02-01

To improve temporal-frequency property of wavefront sensorless adaptive optics (AO) systems, a fast general model-based aberration correction algorithm is presented. The fast general model-based approach is based on the approximately linear relation between the mean square of the aberration gradients and the second moment of far-field intensity distribution. The presented model-based method is capable of completing a mode aberration effective correction just applying one disturbing onto the deformable mirror(one correction by one disturbing), which is reconstructed by the singular value decomposing the correlation matrix of the Zernike functions' gradients. Numerical simulations of AO corrections under the various random and dynamic aberrations are implemented. The simulation results indicate that the equivalent control bandwidth is 2-3 times than that of the previous method with one aberration correction after applying N times disturbing onto the deformable mirror (one correction by N disturbing).

A general method to correct PET data for tissue metabolites using a dual-scan approach.

PubMed

Gunn, R N; Yap, J T; Wells, P; Osman, S; Price, P; Jones, T; Cunningham, V J

2000-04-01

This article presents and analyses a general method of correcting for the presence of radiolabeled metabolites from a parent radiotracer in tissue during PET scanning. The method is based on a dual-scan approach, i.e., parent scan together with an independent supplementary scan in which the radiolabeled metabolite of interest itself is administered. The method corrects for the presence of systemically derived radiolabeled metabolite delivered to the tissues of interest through the blood. Data from the supplementary scan are analyzed to obtain the tissue impulse response function for the metabolite. The time course of the radiolabeled metabolite in plasma in the parent scan is convolved with its tissue impulse response function to derive a correction term. This is not a simple subtraction technique but 1 that takes account of the different time-activity curves of the radiolabeled metabolite in the 2 scans. The method, its implications, and its limitations are discussed with respect to [11C]thymidine and its principal metabolite 11CO2. The general method, based on a dual-scan approach, can be used to correct for radiolabeled metabolites in tissues of interest during PET scanning. The correction accounts for radiolabeled metabolites that are derived systemically and delivered to the tissues of interest through the blood.

ACCOUNTING FOR CALIBRATION UNCERTAINTIES IN X-RAY ANALYSIS: EFFECTIVE AREAS IN SPECTRAL FITTING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Hyunsook; Kashyap, Vinay L.; Drake, Jeremy J.

2011-04-20

While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can bemore » applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.« less

Assessing Commercial and Alternative Poultry Processing Methods using Microbiome Analyses

USDA-ARS?s Scientific Manuscript database

Assessing poultry processing methods/strategies has historically used culture-based methods to assess bacterial changes or reductions, both in terms of general microbial communities (e.g. total aerobic bacteria) or zoonotic pathogens of interest (e.g. Salmonella, Campylobacter). The advent of next ...

Detection of abrupt changes in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

What do evidence-based secondary journals tell us about the publication of clinically important articles in primary healthcare journals?

PubMed Central

McKibbon, Kathleen Ann; Wilczynski, Nancy L; Haynes, Robert Brian

2004-01-01

Background We conducted this analysis to determine i) which journals publish high-quality, clinically relevant studies in internal medicine, general/family practice, general practice nursing, and mental health; and ii) the proportion of clinically relevant articles in each journal. Methods We performed an analytic survey of a hand search of 170 general medicine, general healthcare, and specialty journals for 2000. Research staff assessed individual articles by using explicit criteria for scientific merit for healthcare application. Practitioners assessed the clinical importance of these articles. Outcome measures were the number of high-quality, clinically relevant studies published in the 170 journal titles and how many of these were published in each of four discipline-specific, secondary "evidence-based" journals (ACP Journal Club for internal medicine and its subspecialties; Evidence-Based Medicine for general/family practice; Evidence-Based Nursing for general practice nursing; and Evidence-Based Mental Health for all aspects of mental health). Original studies and review articles were classified for purpose: therapy and prevention, screening and diagnosis, prognosis, etiology and harm, economics and cost, clinical prediction guides, and qualitative studies. Results We evaluated 60,352 articles from 170 journal titles. The pass criteria of high-quality methods and clinically relevant material were met by 3059 original articles and 1073 review articles. For ACP Journal Club (internal medicine), four titles supplied 56.5% of the articles and 27 titles supplied the other 43.5%. For Evidence-Based Medicine (general/family practice), five titles supplied 50.7% of the articles and 40 titles supplied the remaining 49.3%. For Evidence-Based Nursing (general practice nursing), seven titles supplied 51.0% of the articles and 34 additional titles supplied 49.0%. For Evidence-Based Mental Health (mental health), nine titles supplied 53.2% of the articles and 34 additional titles supplied 46.8%. For the disciplines of internal medicine, general/family practice, and mental health (but not general practice nursing), the number of clinically important articles was correlated withScience Citation Index (SCI) Impact Factors. Conclusions Although many clinical journals publish high-quality, clinically relevant and important original studies and systematic reviews, the articles for each discipline studied were concentrated in a small subset of journals. This subset varied according to healthcare discipline; however, many of the important articles for all disciplines in this study were published in broad-based healthcare journals rather than subspecialty or discipline-specific journals. PMID:15350200

Treecode-based generalized Born method

NASA Astrophysics Data System (ADS)

Xu, Zhenli; Cheng, Xiaolin; Yang, Haizhao

2011-02-01

We have developed a treecode-based O(Nlog N) algorithm for the generalized Born (GB) implicit solvation model. Our treecode-based GB (tGB) is based on the GBr6 [J. Phys. Chem. B 111, 3055 (2007)], an analytical GB method with a pairwise descreening approximation for the R6 volume integral expression. The algorithm is composed of a cutoff scheme for the effective Born radii calculation, and a treecode implementation of the GB charge-charge pair interactions. Test results demonstrate that the tGB algorithm can reproduce the vdW surface based Poisson solvation energy with an average relative error less than 0.6% while providing an almost linear-scaling calculation for a representative set of 25 proteins with different sizes (from 2815 atoms to 65456 atoms). For a typical system of 10k atoms, the tGB calculation is three times faster than the direct summation as implemented in the original GBr6 model. Thus, our tGB method provides an efficient way for performing implicit solvent GB simulations of larger biomolecular systems at longer time scales.

Clinical impact of dosimetric changes for volumetric modulated arc therapy in log file-based patient dose calculations.

PubMed

Katsuta, Yoshiyuki; Kadoya, Noriyuki; Fujita, Yukio; Shimizu, Eiji; Matsunaga, Kenichi; Matsushita, Haruo; Majima, Kazuhiro; Jingu, Keiichi

2017-10-01

A log file-based method cannot detect dosimetric changes due to linac component miscalibration because log files are insensitive to miscalibration. Herein, clinical impacts of dosimetric changes on a log file-based method were determined. Five head-and-neck and five prostate plans were applied. Miscalibration-simulated log files were generated by inducing a linac component miscalibration into the log file. Miscalibration magnitudes for leaf, gantry, and collimator at the general tolerance level were ±0.5mm, ±1°, and ±1°, respectively, and at a tighter tolerance level achievable on current linac were ±0.3mm, ±0.5°, and ±0.5°, respectively. Re-calculations were performed on patient anatomy using log file data. Changes in tumor control probability/normal tissue complication probability from treatment planning system dose to re-calculated dose at the general tolerance level was 1.8% on planning target volume (PTV) and 2.4% on organs at risk (OARs) in both plans. These changes at the tighter tolerance level were improved to 1.0% on PTV and to 1.5% on OARs, with a statistically significant difference. We determined the clinical impacts of dosimetric changes on a log file-based method using a general tolerance level and a tighter tolerance level for linac miscalibration and found that a tighter tolerance level significantly improved the accuracy of the log file-based method. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sciarrino, Fabio; Dipartimento di Fisica and Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia, Universita 'La Sapienza', Rome 00185; De Martini, Francesco

The optimal phase-covariant quantum cloning machine (PQCM) broadcasts the information associated to an input qubit into a multiqubit system, exploiting a partial a priori knowledge of the input state. This additional a priori information leads to a higher fidelity than for the universal cloning. The present article first analyzes different innovative schemes to implement the 1{yields}3 PQCM. The method is then generalized to any 1{yields}M machine for an odd value of M by a theoretical approach based on the general angular momentum formalism. Finally different experimental schemes based either on linear or nonlinear methods and valid for single photon polarizationmore » encoded qubits are discussed.« less

Quasi-kernel polynomials and convergence results for quasi-minimal residual iterations

NASA Technical Reports Server (NTRS)

Freund, Roland W.

1992-01-01

Recently, Freund and Nachtigal have proposed a novel polynominal-based iteration, the quasi-minimal residual algorithm (QMR), for solving general nonsingular non-Hermitian linear systems. Motivated by the QMR method, we have introduced the general concept of quasi-kernel polynomials, and we have shown that the QMR algorithm is based on a particular instance of quasi-kernel polynomials. In this paper, we continue our study of quasi-kernel polynomials. In particular, we derive bounds for the norms of quasi-kernel polynomials. These results are then applied to obtain convergence theorems both for the QMR method and for a transpose-free variant of QMR, the TFQMR algorithm.

Model-Based Prognostics of Hybrid Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal

2015-01-01

Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.

Light transport and general aviation aircraft icing research requirements

NASA Technical Reports Server (NTRS)

Breeze, R. K.; Clark, G. M.

1981-01-01

A short term and a long term icing research and technology program plan was drafted for NASA LeRC based on 33 separate research items. The specific items listed resulted from a comprehensive literature search, organized and assisted by a computer management file and an industry/Government agency survey. Assessment of the current facilities and icing technology was accomplished by presenting summaries of ice sensitive components and protection methods; and assessments of penalty evaluation, the experimental data base, ice accretion prediction methods, research facilities, new protection methods, ice protection requirements, and icing instrumentation. The intent of the research plan was to determine what icing research NASA LeRC must do or sponsor to ultimately provide for increased utilization and safety of light transport and general aviation aircraft.

Label-free functional nucleic acid sensors for detecting target agents

DOEpatents

Lu, Yi; Xiang, Yu

2015-01-13

A general methodology to design label-free fluorescent functional nucleic acid sensors using a vacant site approach and an abasic site approach is described. In one example, a method for designing label-free fluorescent functional nucleic acid sensors (e.g., those that include a DNAzyme, aptamer or aptazyme) that have a tunable dynamic range through the introduction of an abasic site (e.g., dSpacer) or a vacant site into the functional nucleic acids. Also provided is a general method for designing label-free fluorescent aptamer sensors based on the regulation of malachite green (MG) fluorescence. A general method for designing label-free fluorescent catalytic and molecular beacons (CAMBs) is also provided. The methods demonstrated here can be used to design many other label-free fluorescent sensors to detect a wide range of analytes. Sensors and methods of using the disclosed sensors are also provided.

Supervision--growing and building a sustainable general practice supervisor system.

PubMed

Thomson, Jennifer S; Anderson, Katrina J; Mara, Paul R; Stevenson, Alexander D

2011-06-06

This article explores various models and ideas for future sustainable general practice vocational training supervision in Australia. The general practitioner supervisor in the clinical practice setting is currently central to training the future general practice workforce. Finding ways to recruit, retain and motivate both new and experienced GP teachers is discussed, as is the creation of career paths for such teachers. Some of the newer methods of practice-based teaching are considered for further development, including vertically integrated teaching, e-learning, wave consulting and teaching on the run, teaching teams and remote teaching. Approaches to supporting and resourcing teaching and the required infrastructure are also considered. Further research into sustaining the practice-based general practice supervision model will be required.

Gas Pressure Monitored Iodide-Catalyzed Decomposition Kinetics of H[subscript 2]O[subscript 2]: Initial-Rate and Integrated-Rate Methods in the General Chemistry Lab

ERIC Educational Resources Information Center

Nyasulu, Frazier; Barlag, Rebecca

2010-01-01

The reaction kinetics of the iodide-catalyzed decomposition of [subscript 2]O[subscript 2] using the integrated-rate method is described. The method is based on the measurement of the total gas pressure using a datalogger and pressure sensor. This is a modification of a previously reported experiment based on the initial-rate approach. (Contains 2…

Numerical solution to generalized Burgers'-Fisher equation using Exp-function method hybridized with heuristic computation.

PubMed

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems.

Numerical Solution to Generalized Burgers'-Fisher Equation Using Exp-Function Method Hybridized with Heuristic Computation

PubMed Central

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems. PMID:25811858

48 CFR 970.1100-1 - Performance-based contracting.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SUPPLEMENTARY REGULATIONS DOE MANAGEMENT AND OPERATING CONTRACTS Describing Agency Needs 970.1100-1 Performance... practicable, performance-based contracting methods in its management and operating contracts. The Office of...-based contracting concepts and methodologies that may be generally applied to management and operating...

Estimation of selected streamflow statistics for a network of low-flow partial-record stations in areas affected by Base Realignment and Closure (BRAC) in Maryland

USGS Publications Warehouse

Ries, Kernell G.; Eng, Ken

2010-01-01

The U.S. Geological Survey, in cooperation with the Maryland Department of the Environment, operated a network of 20 low-flow partial-record stations during 2008 in a region that extends from southwest of Baltimore to the northeastern corner of Maryland to obtain estimates of selected streamflow statistics at the station locations. The study area is expected to face a substantial influx of new residents and businesses as a result of military and civilian personnel transfers associated with the Federal Base Realignment and Closure Act of 2005. The estimated streamflow statistics, which include monthly 85-percent duration flows, the 10-year recurrence-interval minimum base flow, and the 7-day, 10-year low flow, are needed to provide a better understanding of the availability of water resources in the area to be affected by base-realignment activities. Streamflow measurements collected for this study at the low-flow partial-record stations and measurements collected previously for 8 of the 20 stations were related to concurrent daily flows at nearby index streamgages to estimate the streamflow statistics. Three methods were used to estimate the streamflow statistics and two methods were used to select the index streamgages. Of the three methods used to estimate the streamflow statistics, two of them--the Moments and MOVE1 methods--rely on correlating the streamflow measurements at the low-flow partial-record stations with concurrent streamflows at nearby, hydrologically similar index streamgages to determine the estimates. These methods, recommended for use by the U.S. Geological Survey, generally require about 10 streamflow measurements at the low-flow partial-record station. The third method transfers the streamflow statistics from the index streamgage to the partial-record station based on the average of the ratios of the measured streamflows at the partial-record station to the concurrent streamflows at the index streamgage. This method can be used with as few as one pair of streamflow measurements made on a single streamflow recession at the low-flow partial-record station, although additional pairs of measurements will increase the accuracy of the estimates. Errors associated with the two correlation methods generally were lower than the errors associated with the flow-ratio method, but the advantages of the flow-ratio method are that it can produce reasonably accurate estimates from streamflow measurements much faster and at lower cost than estimates obtained using the correlation methods. The two index-streamgage selection methods were (1) selection based on the highest correlation coefficient between the low-flow partial-record station and the index streamgages, and (2) selection based on Euclidean distance, where the Euclidean distance was computed as a function of geographic proximity and the basin characteristics: drainage area, percentage of forested area, percentage of impervious area, and the base-flow recession time constant, t. Method 1 generally selected index streamgages that were significantly closer to the low-flow partial-record stations than method 2. The errors associated with the estimated streamflow statistics generally were lower for method 1 than for method 2, but the differences were not statistically significant. The flow-ratio method for estimating streamflow statistics at low-flow partial-record stations was shown to be independent from the two correlation-based estimation methods. As a result, final estimates were determined for eight low-flow partial-record stations by weighting estimates from the flow-ratio method with estimates from one of the two correlation methods according to the respective variances of the estimates. Average standard errors of estimate for the final estimates ranged from 90.0 to 7.0 percent, with an average value of 26.5 percent. Average standard errors of estimate for the weighted estimates were, on average, 4.3 percent less than the best average standard errors of estima

Spectrophotometric and spectrofluorimetric methods for determination of certain biologically active phenolic drugs in their bulk powders and different pharmaceutical formulations

NASA Astrophysics Data System (ADS)

Omar, Mahmoud A.; Badr El-Din, Kalid M.; Salem, Hesham; Abdelmageed, Osama H.

2018-03-01

Two simple and sensitive spectrophotometric and spectrofluorimetric methods for the determination of terbutaline sulfate, fenoterol hydrobromide, etilefrine hydrochloride, isoxsuprine hydrochloride, ethamsylate, doxycycline hyclate have been developed. Both methods were based on the oxidation of the cited drugs with cerium (IV) in acid medium. The spectrophotometric method was based on measurement of the absorbance difference (ΔA), which represents the excess cerium (IV), at 317 nm for each drug. On the other hand, the spectrofluorimetric method was based on measurement of the fluorescent of the produced cerium (III) at emission wavelength 354 nm (λexcitation = 255 nm) for the concentrations studied for each drug. For both methods, the variables affecting the reactions were carefully investigated and the conditions were optimized. Linear relationships were found between either ΔA or the fluorescent of the produced cerium (III) values and the concentration of the studied drugs in a general concentration range of 2.0-24.0 μg mL- 1, 20.0-24.0 ng mL- 1 with good correlation coefficients in the following range 0.9990-0.9999, 0.9990-0.9993 for spectrophotometric and spectrofluorimetric methods respectively. The limits of detection and quantitation of spectrophotometric method were found in general concentration range 0.190-0.787 and 0.634-2.624 μg mL- 1respectively. For spectrofluorimetric method, the limits of detection and quantitation were found in general concentration range 4.77-9.52 and 15.91-31.74 ng mL- 1 respectively. The stoichiometry of the reaction was determined, and the reactions pathways were postulated. The analytical performance of the methods, in terms of accuracy and precision, were statistically validated and the results obtained were satisfactory. The methods have been successfully applied to the determination of the cited drugs in their commercial pharmaceutical formulations. Statistical comparison of the results with the reference methods showed excellent agreement and proved that no significant difference in the accuracy and precision.

PASTA for Proteins.

PubMed

Collins, Kodi; Warnow, Tandy

2018-06-19

PASTA is a multiple sequence method that uses divide-and-conquer plus iteration to enable base alignment methods to scale with high accuracy to large sequence datasets. By default, PASTA included MAFFT L-INS-i; our new extension of PASTA enables the use of MAFFT G-INS-i, MAFFT Homologs, CONTRAlign, and ProbCons. We analyzed the performance of each base method and PASTA using these base methods on 224 datasets from BAliBASE 4 with at least 50 sequences. We show that PASTA enables the most accurate base methods to scale to larger datasets at reduced computational effort, and generally improves alignment and tree accuracy on the largest BAliBASE datasets. PASTA is available at https://github.com/kodicollins/pasta and has also been integrated into the original PASTA repository at https://github.com/smirarab/pasta. Supplementary data are available at Bioinformatics online.

[GSH fermentation process modeling using entropy-criterion based RBF neural network model].

PubMed

Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng

2008-05-01

The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.

Decentralised control of continuous Petri nets

NASA Astrophysics Data System (ADS)

Wang, Liewei; Wang, Xu

2017-05-01

This paper focuses on decentralised control of systems modelled by continuous Petri nets, in which a target marking control problem is discussed. In some previous works, an efficient ON/OFF strategy-based minimum-time controller was developed. Nevertheless, the convergence is only proved for subclasses like Choice-Free nets. For a general net, the pre-conditions of applying the ON/OFF strategy are not given; therefore, the application scope of the method is unclear. In this work, we provide two sufficient conditions of applying the ON/OFF strategy-based controller to general nets. Furthermore, an extended algorithm for general nets is proposed, in which control laws are computed based on some limited information, without knowing the detailed structure of subsystems.

Intensity-based dual model method for generation of synthetic CT images from standard T2-weighted MR images - Generalized technique for four different MR scanners.

PubMed

Koivula, Lauri; Kapanen, Mika; Seppälä, Tiina; Collan, Juhani; Dowling, Jason A; Greer, Peter B; Gustafsson, Christian; Gunnlaugsson, Adalsteinn; Olsson, Lars E; Wee, Leonard; Korhonen, Juha

2017-12-01

Recent studies have shown that it is possible to conduct entire radiotherapy treatment planning (RTP) workflow using only MR images. This study aims to develop a generalized intensity-based method to generate synthetic CT (sCT) images from standard T2-weighted (T2 w ) MR images of the pelvis. This study developed a generalized dual model HU conversion method to convert standard T2 w MR image intensity values to synthetic HU values, separately inside and outside of atlas-segmented bone volume contour. The method was developed and evaluated with 20 and 35 prostate cancer patients, respectively. MR images with scanning sequences in clinical use were acquired with four different MR scanners of three vendors. For the generated synthetic CT (sCT) images of the 35 prostate patients, the mean (and maximal) HU differences in soft and bony tissue volumes were 16 ± 6 HUs (34 HUs) and -46 ± 56 HUs (181 HUs), respectively, against the true CT images. The average of the PTV mean dose difference in sCTs compared to those in true CTs was -0.6 ± 0.4% (-1.3%). The study provides a generalized method for sCT creation from standard T2 w images of the pelvis. The method produced clinically acceptable dose calculation results for all the included scanners and MR sequences. Copyright © 2017 Elsevier B.V. All rights reserved.

A general method for decomposing the causes of socioeconomic inequality in health.

PubMed

Heckley, Gawain; Gerdtham, Ulf-G; Kjellsson, Gustav

2016-07-01

We introduce a general decomposition method applicable to all forms of bivariate rank dependent indices of socioeconomic inequality in health, including the concentration index. The technique is based on recentered influence function regression and requires only the application of OLS to a transformed variable with similar interpretation. Our method requires few identifying assumptions to yield valid estimates in most common empirical applications, unlike current methods favoured in the literature. Using the Swedish Twin Registry and a within twin pair fixed effects identification strategy, our new method finds no evidence of a causal effect of education on income-related health inequality. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

NASA Astrophysics Data System (ADS)

Jiang, Fei-Bo; Dai, Qian-Wei; Dong, Li

2016-06-01

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Multidisciplinary optimization of a controlled space structure using 150 design variables

NASA Technical Reports Server (NTRS)

James, Benjamin B.

1992-01-01

A general optimization-based method for the design of large space platforms through integration of the disciplines of structural dynamics and control is presented. The method uses the global sensitivity equations approach and is especially appropriate for preliminary design problems in which the structural and control analyses are tightly coupled. The method is capable of coordinating general purpose structural analysis, multivariable control, and optimization codes, and thus, can be adapted to a variety of controls-structures integrated design projects. The method is used to minimize the total weight of a space platform while maintaining a specified vibration decay rate after slewing maneuvers.

A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty

NASA Astrophysics Data System (ADS)

Tian, Wenli; Cao, Chengxuan

2017-03-01

A generalized interval fuzzy mixed integer programming model is proposed for the multimodal freight transportation problem under uncertainty, in which the optimal mode of transport and the optimal amount of each type of freight transported through each path need to be decided. For practical purposes, three mathematical methods, i.e. the interval ranking method, fuzzy linear programming method and linear weighted summation method, are applied to obtain equivalents of constraints and parameters, and then a fuzzy expected value model is presented. A heuristic algorithm based on a greedy criterion and the linear relaxation algorithm are designed to solve the model.

Relation between scattering and production amplitude--Case of intermediate {sigma}-particle in {pi}{pi}-system--

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ishida, Muneyuki; Ishida, Shin; Ishida, Taku

1998-05-29

The relation between scattering and production amplitudes are investigated, using a simple field theoretical model, from the general viewpoint of unitarity and the applicability of final state interaction (FSI-) theorem. The IA-method and VMW-method, which are applied to our phenomenological analyses [2,3] suggesting the {sigma}-existence, are obtained as the physical state representations of scattering and production amplitudes, respectively. Moreover, the VMW-method is shown to be an effective method to obtain the resonance properties from general production processes, while the conventional analyses based on the 'universality' of {pi}{pi}-scattering amplitude are powerless for this purpose.

Relation between scattering and production amplitude—Case of intermediate σ-particle in ππ-system—

NASA Astrophysics Data System (ADS)

Ishida, Muneyuki; Ishida, Shin; Ishida, Taku

1998-05-01

The relation between scattering and production amplitudes are investigated, using a simple field theoretical model, from the general viewpoint of unitarity and the applicability of final state interaction (FSI-) theorem. The IA-method and VMW-method, which are applied to our phenomenological analyses [2,3] suggesting the σ-existence, are obtained as the physical state representations of scattering and production amplitudes, respectively. Moreover, the VMW-method is shown to be an effective method to obtain the resonance properties from general production processes, while the conventional analyses based on the "universality" of ππ-scattering amplitude are powerless for this purpose.

Efficient solution of the simplified P N equations

DOE PAGES

Hamilton, Steven P.; Evans, Thomas M.

2014-12-23

We show new solver strategies for the multigroup SPN equations for nuclear reactor analysis. By forming the complete matrix over space, moments, and energy a robust set of solution strategies may be applied. Moreover, power iteration, shifted power iteration, Rayleigh quotient iteration, Arnoldi's method, and a generalized Davidson method, each using algebraic and physics-based multigrid preconditioners, have been compared on C5G7 MOX test problem as well as an operational PWR model. These results show that the most ecient approach is the generalized Davidson method, that is 30-40 times faster than traditional power iteration and 6-10 times faster than Arnoldi's method.

Wang-Landau Reaction Ensemble Method: Simulation of Weak Polyelectrolytes and General Acid-Base Reactions.

PubMed

Landsgesell, Jonas; Holm, Christian; Smiatek, Jens

2017-02-14

We present a novel method for the study of weak polyelectrolytes and general acid-base reactions in molecular dynamics and Monte Carlo simulations. The approach combines the advantages of the reaction ensemble and the Wang-Landau sampling method. Deprotonation and protonation reactions are simulated explicitly with the help of the reaction ensemble method, while the accurate sampling of the corresponding phase space is achieved by the Wang-Landau approach. The combination of both techniques provides a sufficient statistical accuracy such that meaningful estimates for the density of states and the partition sum can be obtained. With regard to these estimates, several thermodynamic observables like the heat capacity or reaction free energies can be calculated. We demonstrate that the computation times for the calculation of titration curves with a high statistical accuracy can be significantly decreased when compared to the original reaction ensemble method. The applicability of our approach is validated by the study of weak polyelectrolytes and their thermodynamic properties.

Comparison of the Chebyshev Method and the Generalized Crank-Nicholson Method for time Propagation in Quantum Mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Formanek, Martin; Vana, Martin; Houfek, Karel

2010-09-30

We compare efficiency of two methods for numerical solution of the time-dependent Schroedinger equation, namely the Chebyshev method and the recently introduced generalized Crank-Nicholson method. As a testing system the free propagation of a particle in one dimension is used. The space discretization is based on the high-order finite diferences to approximate accurately the kinetic energy operator in the Hamiltonian. We show that the choice of the more effective method depends on how many wave functions must be calculated during the given time interval to obtain relevant and reasonably accurate information about the system, i.e. on the choice of themore » time step.« less

New Operational Matrices for Solving Fractional Differential Equations on the Half-Line

PubMed Central

2015-01-01

In this paper, the fractional-order generalized Laguerre operational matrices (FGLOM) of fractional derivatives and fractional integration are derived. These operational matrices are used together with spectral tau method for solving linear fractional differential equations (FDEs) of order ν (0 < ν < 1) on the half line. An upper bound of the absolute errors is obtained for the approximate and exact solutions. Fractional-order generalized Laguerre pseudo-spectral approximation is investigated for solving nonlinear initial value problem of fractional order ν. The extension of the fractional-order generalized Laguerre pseudo-spectral method is given to solve systems of FDEs. We present the advantages of using the spectral schemes based on fractional-order generalized Laguerre functions and compare them with other methods. Several numerical examples are implemented for FDEs and systems of FDEs including linear and nonlinear terms. We demonstrate the high accuracy and the efficiency of the proposed techniques. PMID:25996369

New operational matrices for solving fractional differential equations on the half-line.

PubMed

Bhrawy, Ali H; Taha, Taha M; Alzahrani, Ebraheem O; Alzahrani, Ebrahim O; Baleanu, Dumitru; Alzahrani, Abdulrahim A

2015-01-01

In this paper, the fractional-order generalized Laguerre operational matrices (FGLOM) of fractional derivatives and fractional integration are derived. These operational matrices are used together with spectral tau method for solving linear fractional differential equations (FDEs) of order ν (0 < ν < 1) on the half line. An upper bound of the absolute errors is obtained for the approximate and exact solutions. Fractional-order generalized Laguerre pseudo-spectral approximation is investigated for solving nonlinear initial value problem of fractional order ν. The extension of the fractional-order generalized Laguerre pseudo-spectral method is given to solve systems of FDEs. We present the advantages of using the spectral schemes based on fractional-order generalized Laguerre functions and compare them with other methods. Several numerical examples are implemented for FDEs and systems of FDEs including linear and nonlinear terms. We demonstrate the high accuracy and the efficiency of the proposed techniques.

A Framework of Covariance Projection on Constraint Manifold for Data Fusion.

PubMed

Bakr, Muhammad Abu; Lee, Sukhan

2018-05-17

A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and measurements, which is defined in the extended space with all the redundant sources of data such as state predictions and measurements considered as independent variables. By the general framework, we mean that it is able to fuse any correlated data sources while directly incorporating constraints and identifying inconsistent data without any prior information. The proposed method, referred to here as the Covariance Projection (CP) method, provides an unbiased and optimal solution in the sense of minimum mean square error (MMSE), if the projection is based on the minimum weighted distance on the constraint manifold. The proposed method not only offers a generalization of the conventional formula for handling constraints and data inconsistency, but also provides a new insight into data fusion in terms of a geometric-algebraic point of view. Simulation results are provided to show the effectiveness of the proposed method in handling constraints and data inconsistency.

What is a good index? Problems with statistically based indicators and the Malmquist index as alternative

USDA-ARS?s Scientific Manuscript database

Conventional multivariate statistical methods have been used for decades to calculate environmental indicators. These methods generally work fine if they are used in a situation where the method can be tailored to the data. But there is some skepticism that the methods might fail in the context of s...

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

DOE PAGES

Suderman, Ryan T.; Mitra, Eshan David; Lin, Yen Ting; ...

2018-03-28

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termedmore » network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.« less

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suderman, Ryan T.; Mitra, Eshan David; Lin, Yen Ting

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termedmore » network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.« less

Free surface profiles in river flows: Can standard energy-based gradually-varied flow computations be pursued?

NASA Astrophysics Data System (ADS)

Cantero, Francisco; Castro-Orgaz, Oscar; Garcia-Marín, Amanda; Ayuso, José Luis; Dey, Subhasish

2015-10-01

Is the energy equation for gradually-varied flow the best approximation for the free surface profile computations in river flows? Determination of flood inundation in rivers and natural waterways is based on the hydraulic computation of flow profiles. This is usually done using energy-based gradually-varied flow models, like HEC-RAS, that adopts a vertical division method for discharge prediction in compound channel sections. However, this discharge prediction method is not so accurate in the context of advancements over the last three decades. This paper firstly presents a study of the impact of discharge prediction on the gradually-varied flow computations by comparing thirteen different methods for compound channels, where both energy and momentum equations are applied. The discharge, velocity distribution coefficients, specific energy, momentum and flow profiles are determined. After the study of gradually-varied flow predictions, a new theory is developed to produce higher-order energy and momentum equations for rapidly-varied flow in compound channels. These generalized equations enable to describe the flow profiles with more generality than the gradually-varied flow computations. As an outcome, results of gradually-varied flow provide realistic conclusions for computations of flow in compound channels, showing that momentum-based models are in general more accurate; whereas the new theory developed for rapidly-varied flow opens a new research direction, so far not investigated in flows through compound channels.

Modelling of extreme rainfall events in Peninsular Malaysia based on annual maximum and partial duration series

NASA Astrophysics Data System (ADS)

Zin, Wan Zawiah Wan; Shinyie, Wendy Ling; Jemain, Abdul Aziz

2015-02-01

In this study, two series of data for extreme rainfall events are generated based on Annual Maximum and Partial Duration Methods, derived from 102 rain-gauge stations in Peninsular from 1982-2012. To determine the optimal threshold for each station, several requirements must be satisfied and Adapted Hill estimator is employed for this purpose. A semi-parametric bootstrap is then used to estimate the mean square error (MSE) of the estimator at each threshold and the optimal threshold is selected based on the smallest MSE. The mean annual frequency is also checked to ensure that it lies in the range of one to five and the resulting data is also de-clustered to ensure independence. The two data series are then fitted to Generalized Extreme Value and Generalized Pareto distributions for annual maximum and partial duration series, respectively. The parameter estimation methods used are the Maximum Likelihood and the L-moment methods. Two goodness of fit tests are then used to evaluate the best-fitted distribution. The results showed that the Partial Duration series with Generalized Pareto distribution and Maximum Likelihood parameter estimation provides the best representation for extreme rainfall events in Peninsular Malaysia for majority of the stations studied. Based on these findings, several return values are also derived and spatial mapping are constructed to identify the distribution characteristic of extreme rainfall in Peninsular Malaysia.

Minimal residual method provides optimal regularization parameter for diffuse optical tomography

NASA Astrophysics Data System (ADS)

Jagannath, Ravi Prasad K.; Yalavarthy, Phaneendra K.

2012-10-01

The inverse problem in the diffuse optical tomography is known to be nonlinear, ill-posed, and sometimes under-determined, requiring regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of this regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter.

Minimal residual method provides optimal regularization parameter for diffuse optical tomography.

PubMed

Jagannath, Ravi Prasad K; Yalavarthy, Phaneendra K

2012-10-01

The inverse problem in the diffuse optical tomography is known to be nonlinear, ill-posed, and sometimes under-determined, requiring regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of this regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter.

The Co-simulation of Humanoid Robot Based on Solidworks, ADAMS and Simulink

NASA Astrophysics Data System (ADS)

Song, Dalei; Zheng, Lidan; Wang, Li; Qi, Weiwei; Li, Yanli

A simulation method of adaptive controller is proposed for the humanoid robot system based on co-simulation of Solidworks, ADAMS and Simulink. A complex mathematical modeling process is avoided by this method, and the real time dynamic simulating function of Simulink would be exerted adequately. This method could be generalized to other complicated control system. This method is adopted to build and analyse the model of humanoid robot. The trajectory tracking and adaptive controller design also proceed based on it. The effect of trajectory tracking is evaluated by fitting-curve theory of least squares method. The anti-interference capability of the robot is improved a lot through comparative analysis.

A Graphical Approach to Teaching Amplifier Design at the Undergraduate Level

ERIC Educational Resources Information Center

Assaad, R. S.; Silva-Martinez, J.

2009-01-01

Current methods of teaching basic amplifier design at the undergraduate level need further development to match today's technological advances. The general class approach to amplifier design is analytical and heavily based on mathematical manipulations. However, the students mathematical abilities are generally modest, creating a void in which…

48 CFR 915.404-4-71-1 - General.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false General. 915.404-4-71-1 Section 915.404-4-71-1 Federal Acquisition Regulations System DEPARTMENT OF ENERGY CONTRACTING METHODS AND...) Reward contractors based on the complexity of work; (2) Reward contractors who demonstrate and establish...

Evidence conflict measure based on OWA operator in open world

PubMed Central

Wang, Shiyu; Liu, Xiang; Zheng, Hanqing; Wei, Boya

2017-01-01

Dempster-Shafer evidence theory has been extensively used in many information fusion systems since it was proposed by Dempster and extended by Shafer. Many scholars have been conducted on conflict management of Dempster-Shafer evidence theory in past decades. However, how to determine a potent parameter to measure evidence conflict, when the given environment is in an open world, namely the frame of discernment is incomplete, is still an open issue. In this paper, a new method which combines generalized conflict coefficient, generalized evidence distance, and generalized interval correlation coefficient based on ordered weighted averaging (OWA) operator, to measure the conflict of evidence is presented. Through ordered weighted average of these three parameters, the combinatorial coefficient can still measure the conflict effectively when one or two parameters are not valid. Several numerical examples demonstrate the effectiveness of the proposed method. PMID:28542271

Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG.

PubMed

Natarajan, Annamalai; Angarita, Gustavo; Gaiser, Edward; Malison, Robert; Ganesan, Deepak; Marlin, Benjamin M

2016-09-01

Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.

NetProt: Complex-based Feature Selection.

PubMed

Goh, Wilson Wen Bin; Wong, Limsoon

2017-08-04

Protein complex-based feature selection (PCBFS) provides unparalleled reproducibility with high phenotypic relevance on proteomics data. Currently, there are five PCBFS paradigms, but not all representative methods have been implemented or made readily available. To allow general users to take advantage of these methods, we developed the R-package NetProt, which provides implementations of representative feature-selection methods. NetProt also provides methods for generating simulated differential data and generating pseudocomplexes for complex-based performance benchmarking. The NetProt open source R package is available for download from https://github.com/gohwils/NetProt/releases/ , and online documentation is available at http://rpubs.com/gohwils/204259 .

Newton–Hooke-type symmetry of anisotropic oscillators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, P.M., E-mail: zhpm@impcas.ac.cn; Horvathy, P.A., E-mail: horvathy@lmpt.univ-tours.fr; Laboratoire de Mathématiques et de Physique Théorique, Université de Tours

2013-06-15

Rotation-less Newton–Hooke-type symmetry, found recently in the Hill problem, and instrumental for explaining the center-of-mass decomposition, is generalized to an arbitrary anisotropic oscillator in the plane. Conversely, the latter system is shown, by the orbit method, to be the most general one with such a symmetry. Full Newton–Hooke symmetry is recovered in the isotropic case. Star escape from a galaxy is studied as an application. -- Highlights: ► Rotation-less Newton–Hooke (NH) symmetry is generalized to an arbitrary anisotropic oscillator. ► The orbit method is used to find the most general case for rotation-less NH symmetry. ► The NH symmetry ismore » decomposed into Heisenberg algebras based on chiral decomposition.« less

A numerical method to solve the 1D and the 2D reaction diffusion equation based on Bessel functions and Jacobian free Newton-Krylov subspace methods

NASA Astrophysics Data System (ADS)

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.

Orthonormal vector general polynomials derived from the Cartesian gradient of the orthonormal Zernike-based polynomials.

PubMed

Mafusire, Cosmas; Krüger, Tjaart P J

2018-06-01

The concept of orthonormal vector circle polynomials is revisited by deriving a set from the Cartesian gradient of Zernike polynomials in a unit circle using a matrix-based approach. The heart of this model is a closed-form matrix equation of the gradient of Zernike circle polynomials expressed as a linear combination of lower-order Zernike circle polynomials related through a gradient matrix. This is a sparse matrix whose elements are two-dimensional standard basis transverse Euclidean vectors. Using the outer product form of the Cholesky decomposition, the gradient matrix is used to calculate a new matrix, which we used to express the Cartesian gradient of the Zernike circle polynomials as a linear combination of orthonormal vector circle polynomials. Since this new matrix is singular, the orthonormal vector polynomials are recovered by reducing the matrix to its row echelon form using the Gauss-Jordan elimination method. We extend the model to derive orthonormal vector general polynomials, which are orthonormal in a general pupil by performing a similarity transformation on the gradient matrix to give its equivalent in the general pupil. The outer form of the Gram-Schmidt procedure and the Gauss-Jordan elimination method are then applied to the general pupil to generate the orthonormal vector general polynomials from the gradient of the orthonormal Zernike-based polynomials. The performance of the model is demonstrated with a simulated wavefront in a square pupil inscribed in a unit circle.

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

Different perspectives on economic base.

Treesearch

Lisa K. Crone; Richard W. Haynes; Nicholas E. Reyna

1999-01-01

Two general approaches for measuring the economic base are discussed. Each method is used to define the economic base for each of the counties included in the Interior Columbia Basin Ecosystem Management Project area. A more detailed look at four selected counties results in similar findings from different approaches. Limitations of economic base analysis also are...

Tailored reflectors for illumination.

PubMed

Jenkins, D; Winston, R

1996-04-01

We report on tailored reflector design methods that allow the placement of general illumination patterns onto a target plane. The use of a new integral design method based on the edge-ray principle of nonimaging optics gives much more compact reflector shapes by eliminating the need for a gap between the source and the reflector profile. In addition, the reflectivity of the reflector is incorporated as a design parameter. We show the performance of design for constant irradiance on a distant plane, and we show how a leading-edge-ray method may be used to achieve general illumination patterns on nearby targets.

Explanatory and illustrative visualization of special and general relativity.

PubMed

Weiskopf, Daniel; Borchers, Marc; Ertl, Thomas; Falk, Martin; Fechtig, Oliver; Frank, Regine; Grave, Frank; King, Andreas; Kraus, Ute; Müller, Thomas; Nollert, Hans-Peter; Rica Mendez, Isabel; Ruder, Hanns; Schafhitzel, Tobias; Schär, Sonja; Zahn, Corvin; Zatloukal, Michael

2006-01-01

This paper describes methods for explanatory and illustrative visualizations used to communicate aspects of Einstein's theories of special and general relativity, their geometric structure, and of the related fields of cosmology and astrophysics. Our illustrations target a general audience of laypersons interested in relativity. We discuss visualization strategies, motivated by physics education and the didactics of mathematics, and describe what kind of visualization methods have proven to be useful for different types of media, such as still images in popular science magazines, film contributions to TV shows, oral presentations, or interactive museum installations. Our primary approach is to adopt an egocentric point of view: The recipients of a visualization participate in a visually enriched thought experiment that allows them to experience or explore a relativistic scenario. In addition, we often combine egocentric visualizations with more abstract illustrations based on an outside view in order to provide several presentations of the same phenomenon. Although our visualization tools often build upon existing methods and implementations, the underlying techniques have been improved by several novel technical contributions like image-based special relativistic rendering on GPUs, special relativistic 4D ray tracing for accelerating scene objects, an extension of general relativistic ray tracing to manifolds described by multiple charts, GPU-based interactive visualization of gravitational light deflection, as well as planetary terrain rendering. The usefulness and effectiveness of our visualizations are demonstrated by reporting on experiences with, and feedback from, recipients of visualizations and collaborators.

Scene-based nonuniformity corrections for optical and SWIR pushbroom sensors.

PubMed

Leathers, Robert; Downes, Trijntje; Priest, Richard

2005-06-27

We propose and evaluate several scene-based methods for computing nonuniformity corrections for visible or near-infrared pushbroom sensors. These methods can be used to compute new nonuniformity correction values or to repair or refine existing radiometric calibrations. For a given data set, the preferred method depends on the quality of the data, the type of scenes being imaged, and the existence and quality of a laboratory calibration. We demonstrate our methods with data from several different sensor systems and provide a generalized approach to be taken for any new data set.

Rotor dynamic simulation and system identification methods for application to vacuum whirl data

NASA Technical Reports Server (NTRS)

Berman, A.; Giansante, N.; Flannelly, W. G.

1980-01-01

Methods of using rotor vacuum whirl data to improve the ability to model helicopter rotors were developed. The work consisted of the formulation of the equations of motion of elastic blades on a hub using a Galerkin method; the development of a general computer program for simulation of these equations; the study and implementation of a procedure for determining physical parameters based on measured data; and the application of a method for computing the normal modes and natural frequencies based on test data.

A symmetric Trefftz-DG formulation based on a local boundary element method for the solution of the Helmholtz equation

NASA Astrophysics Data System (ADS)

Barucq, H.; Bendali, A.; Fares, M.; Mattesi, V.; Tordeux, S.

2017-02-01

A general symmetric Trefftz Discontinuous Galerkin method is built for solving the Helmholtz equation with piecewise constant coefficients. The construction of the corresponding local solutions to the Helmholtz equation is based on a boundary element method. A series of numerical experiments displays an excellent stability of the method relatively to the penalty parameters, and more importantly its outstanding ability to reduce the instabilities known as the "pollution effect" in the literature on numerical simulations of long-range wave propagation.

Evaluating the Generalization Value of Process-based Models in a Deep-in-time Machine Learning framework

NASA Astrophysics Data System (ADS)

Shen, C.; Fang, K.

2017-12-01

Deep Learning (DL) methods have made revolutionary strides in recent years. A core value proposition of DL is that abstract notions and patterns can be extracted purely from data, without the need for domain expertise. Process-based models (PBM), on the other hand, can be regarded as repositories of human knowledge or hypotheses about how systems function. Here, through computational examples, we argue that there is merit in integrating PBMs with DL due to the imbalance and lack of data in many situations, especially in hydrology. We trained a deep-in-time neural network, the Long Short-Term Memory (LSTM), to learn soil moisture dynamics from Soil Moisture Active Passive (SMAP) Level 3 product. We show that when PBM solutions are integrated into LSTM, the network is able to better generalize across regions. LSTM is able to better utilize PBM solutions than simpler statistical methods. Our results suggest PBMs have generalization value which should be carefully assessed and utilized. We also emphasize that when properly regularized, the deep network is robust and is of superior testing performance compared to simpler methods.

Collapse of ultrashort spatiotemporal pulses described by the cubic generalized Kadomtsev-Petviashvili equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leblond, Herve; Kremer, David; Mihalache, Dumitru

2010-03-15

By using a reductive perturbation method, we derive from Maxwell-Bloch equations a cubic generalized Kadomtsev-Petviashvili equation for ultrashort spatiotemporal optical pulse propagation in cubic (Kerr-like) media without the use of the slowly varying envelope approximation. We calculate the collapse threshold for the propagation of few-cycle spatiotemporal pulses described by the generic cubic generalized Kadomtsev-Petviashvili equation by a direct numerical method and compare it to analytic results based on a rigorous virial theorem. Besides, typical evolution of the spectrum (integrated over the transverse spatial coordinate) is given and a strongly asymmetric spectral broadening of ultrashort spatiotemporal pulses during collapse is evidenced.

A probability-based approach for assessment of roadway safety hardware.

DOT National Transportation Integrated Search

2017-03-14

This report presents a general probability-based approach for assessment of roadway safety hardware (RSH). It was achieved using a reliability : analysis method and computational techniques. With the development of high-fidelity finite element (FE) m...

Anchoring quartet-based phylogenetic distances and applications to species tree reconstruction.

PubMed

Sayyari, Erfan; Mirarab, Siavash

2016-11-11

Inferring species trees from gene trees using the coalescent-based summary methods has been the subject of much attention, yet new scalable and accurate methods are needed. We introduce DISTIQUE, a new statistically consistent summary method for inferring species trees from gene trees under the coalescent model. We generalize our results to arbitrary phylogenetic inference problems; we show that two arbitrarily chosen leaves, called anchors, can be used to estimate relative distances between all other pairs of leaves by inferring relevant quartet trees. This results in a family of distance-based tree inference methods, with running times ranging between quadratic to quartic in the number of leaves. We show in simulated studies that DISTIQUE has comparable accuracy to leading coalescent-based summary methods and reduced running times.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Podoshvedov, S. A., E-mail: podoshvedov@mail.ru

A method to generate Schroedinger cat states in free propagating optical fields based on the use of displaced states (or displacement operators) is developed. Some optical schemes with photon-added coherent states are studied. The schemes are modifications of the general method based on a sequence of displacements and photon additions or subtractions adjusted to generate Schroedinger cat states of a larger size. The effects of detection inefficiency are taken into account.

Problem-Based Learning: Instructor Characteristics, Competencies, and Professional Development

DTIC Science & Technology

2011-01-01

cognitive learning objectives addressed by student -centered instruction . For instance, experiential learning , a variation of which is used at the...based learning in grade school science or mathematics . However, the measures could be modified to focus on adult PBL (or student -centered learning ... student -centered learning methods, the findings should generalize across instructional methods of interest to the Army. Further research is required

Quantifying the quality of medical x-ray images: An evaluation based on normal anatomy for lumbar spine and chest radiography

NASA Astrophysics Data System (ADS)

Tingberg, Anders Martin

Optimisation in diagnostic radiology requires accurate methods for determination of patient absorbed dose and clinical image quality. Simple methods for evaluation of clinical image quality are at present scarce and this project aims at developing such methods. Two methods are used and further developed; fulfillment of image criteria (IC) and visual grading analysis (VGA). Clinical image quality descriptors are defined based on these two methods: image criteria score (ICS) and visual grading analysis score (VGAS), respectively. For both methods the basis is the Image Criteria of the ``European Guidelines on Quality Criteria for Diagnostic Radiographic Images''. Both methods have proved to be useful for evaluation of clinical image quality. The two methods complement each other: IC is an absolute method, which means that the quality of images of different patients and produced with different radiographic techniques can be compared with each other. The separating power of IC is, however, weaker than that of VGA. VGA is the best method for comparing images produced with different radiographic techniques and has strong separating power, but the results are relative, since the quality of an image is compared to the quality of a reference image. The usefulness of the two methods has been verified by comparing the results from both of them with results from a generally accepted method for evaluation of clinical image quality, receiver operating characteristics (ROC). The results of the comparison between the two methods based on visibility of anatomical structures and the method based on detection of pathological structures (free-response forced error) indicate that the former two methods can be used for evaluation of clinical image quality as efficiently as the method based on ROC. More studies are, however, needed for us to be able to draw a general conclusion, including studies of other organs, using other radiographic techniques, etc. The results of the experimental evaluation of clinical image quality are compared with physical quantities calculated with a theoretical model based on a voxel phantom, and correlations are found. The results demonstrate that the computer model can be a useful toot in planning further experimental studies.

A Comparison of Imputation Methods for Bayesian Factor Analysis Models

ERIC Educational Resources Information Center

Merkle, Edgar C.

2011-01-01

Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted…

The Precision Efficacy Analysis for Regression Sample Size Method.

ERIC Educational Resources Information Center

Brooks, Gordon P.; Barcikowski, Robert S.

The general purpose of this study was to examine the efficiency of the Precision Efficacy Analysis for Regression (PEAR) method for choosing appropriate sample sizes in regression studies used for precision. The PEAR method, which is based on the algebraic manipulation of an accepted cross-validity formula, essentially uses an effect size to…

STOCK Mechanics:. a General Theory and Method of Energy Conservation with Applications on Djia

NASA Astrophysics Data System (ADS)

Tuncay, Çağlar

A new method, based on the original theory of conservation of sum of kinetic and potential energy defined for prices is proposed and applied on the Dow Jones Industrials Average (DJIA). The general trends averaged over months or years gave a roughly conserved total energy, with three different potential energies, i.e., positive definite quadratic, negative definite quadratic and linear potential energy for exponential rises (and falls), sinusoidal oscillations and parabolic trajectories, respectively. Corresponding expressions for force (impact) are also given.

Numerical realization of the variational method for generating self-trapped beams

NASA Astrophysics Data System (ADS)

Duque, Erick I.; Lopez-Aguayo, Servando; Malomed, Boris A.

2018-03-01

We introduce a numerical variational method based on the Rayleigh-Ritz optimization principle for predicting two-dimensional self-trapped beams in nonlinear media. This technique overcomes the limitation of the traditional variational approximation in performing analytical Lagrangian integration and differentiation. Approximate soliton solutions of a generalized nonlinear Schr\\"odinger equation are obtained, demonstrating robustness of the beams of various types (fundamental, vortices, multipoles, azimuthons) in the course of their propagation. The algorithm offers possibilities to produce more sophisticated soliton profiles in general nonlinear models.

Detection and Characterization of Exoplanets using Projections on Karhunen-Loeve Eigenimages: Forward Modeling

NASA Astrophysics Data System (ADS)

Pueyo, Laurent

2016-01-01

A new class of high-contrast image analysis algorithms, that empirically fit and subtract systematic noise has lead to recent discoveries of faint exoplanet /substellar companions and scattered light images of circumstellar disks. The consensus emerging in the community is that these methods are extremely efficient at enhancing the detectability of faint astrophysical signal, but do generally create systematic biases in their observed properties. This poster provides a solution this outstanding problem. We present an analytical derivation of a linear expansion that captures the impact of astrophysical over/self-subtraction in current image analysis techniques. We examine the general case for which the reference images of the astrophysical scene moves azimuthally and/or radially across the field of view as a result of the observation strategy. Our new method method is based on perturbing the covariance matrix underlying any least-squares speckles problem and propagating this perturbation through the data analysis algorithm. This work is presented in the framework of Karhunen-Loeve Image Processing (KLIP) but it can be easily generalized to methods relying on linear combination of images (instead of eigen-modes). Based on this linear expansion, obtained in the most general case, we then demonstrate practical applications of this new algorithm. We first consider the case of the spectral extraction of faint point sources in IFS data and illustrate, using public Gemini Planet Imager commissioning data, that our novel perturbation based Forward Modeling (which we named KLIP-FM) can indeed alleviate algorithmic biases. We then apply KLIP-FM to the detection of point sources and show how it decreases the rate of false negatives while keeping the rate of false positives unchanged when compared to classical KLIP. This can potentially have important consequences on the design of follow-up strategies of ongoing direct imaging surveys.

Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology.

PubMed

Schaff, James C; Gao, Fei; Li, Ye; Novak, Igor L; Slepchenko, Boris M

2016-12-01

Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium 'sparks' as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell.

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

NASA Astrophysics Data System (ADS)

Jia, Huizhen; Sun, Quansen; Ji, Zexuan; Wang, Tonghan; Chen, Qiang

2014-11-01

The goal of no-reference/blind image quality assessment (NR-IQA) is to devise a perceptual model that can accurately predict the quality of a distorted image as human opinions, in which feature extraction is an important issue. However, the features used in the state-of-the-art "general purpose" NR-IQA algorithms are usually natural scene statistics (NSS) based or are perceptually relevant; therefore, the performance of these models is limited. To further improve the performance of NR-IQA, we propose a general purpose NR-IQA algorithm which combines NSS-based features with perceptually relevant features. The new method extracts features in both the spatial and gradient domains. In the spatial domain, we extract the point-wise statistics for single pixel values which are characterized by a generalized Gaussian distribution model to form the underlying features. In the gradient domain, statistical features based on neighboring gradient magnitude similarity are extracted. Then a mapping is learned to predict quality scores using a support vector regression. The experimental results on the benchmark image databases demonstrate that the proposed algorithm correlates highly with human judgments of quality and leads to significant performance improvements over state-of-the-art methods.

General Purpose Data-Driven Online System Health Monitoring with Applications to Space Operations

NASA Technical Reports Server (NTRS)

Iverson, David L.; Spirkovska, Lilly; Schwabacher, Mark

2010-01-01

Modern space transportation and ground support system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional parameter limit checking, or model-based or rule-based methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults, failures, or precursors of significant failures. The Inductive Monitoring System (IMS) is a general purpose, data-driven system health monitoring software tool that has been successfully applied to several aerospace applications and is under evaluation for anomaly detection in vehicle and ground equipment for next generation launch systems. After an introduction to IMS application development, we discuss these NASA online monitoring applications, including the integration of IMS with complementary model-based and rule-based methods. Although the examples presented in this paper are from space operations applications, IMS is a general-purpose health-monitoring tool that is also applicable to power generation and transmission system monitoring.

Management and Prevalence of Long-Term Conditions in Primary Health Care for Adults with Intellectual Disabilities Compared with the General Population: A Population-Based Cohort Study

ERIC Educational Resources Information Center

Cooper, Sally-Ann; Hughes-McCormack, Laura; Greenlaw, Nicola; McConnachie, Alex; Allan, Linda; Baltzer, Marion; McArthur, Laura; Henderson, Angela; Melville, Craig; McSkimming, Paula; Morrison, Jill

2018-01-01

Background: In the UK, general practitioners/family physicians receive pay for performance on management of long-term conditions, according to best-practice indicators. Method: Management of long-term conditions was compared between 721 adults with intellectual disabilities and the general population (n = 764,672). Prevalence of long-term…

Effects of the Multiple Solutions and Question Prompts on Generalization and Justification for Non-Routine Mathematical Problem Solving in a Computer Game Context

ERIC Educational Resources Information Center

Lee, Chun-Yi; Chen, Ming-Jang; Chang, Wen-Long

2014-01-01

The aim of this study is to investigate the effects of solution methods and question prompts on generalization and justification of non-routine problem solving for Grade 9 students. The learning activities are based on the context of the frog jumping game. In addition, related computer tools were used to support generalization and justification of…

Application of Complex Adaptive Systems in Portfolio Management

ERIC Educational Resources Information Center

Su, Zheyuan

2017-01-01

Simulation-based methods are becoming a promising research tool in financial markets. A general Complex Adaptive System can be tailored to different application scenarios. Based on the current research, we built two models that would benefit portfolio management by utilizing Complex Adaptive Systems (CAS) in Agent-based Modeling (ABM) approach.…

Stochastic search, optimization and regression with energy applications

NASA Astrophysics Data System (ADS)

Hannah, Lauren A.

Designing clean energy systems will be an important task over the next few decades. One of the major roadblocks is a lack of mathematical tools to economically evaluate those energy systems. However, solutions to these mathematical problems are also of interest to the operations research and statistical communities in general. This thesis studies three problems that are of interest to the energy community itself or provide support for solution methods: R&D portfolio optimization, nonparametric regression and stochastic search with an observable state variable. First, we consider the one stage R&D portfolio optimization problem to avoid the sequential decision process associated with the multi-stage. The one stage problem is still difficult because of a non-convex, combinatorial decision space and a non-convex objective function. We propose a heuristic solution method that uses marginal project values---which depend on the selected portfolio---to create a linear objective function. In conjunction with the 0-1 decision space, this new problem can be solved as a knapsack linear program. This method scales well to large decision spaces. We also propose an alternate, provably convergent algorithm that does not exploit problem structure. These methods are compared on a solid oxide fuel cell R&D portfolio problem. Next, we propose Dirichlet Process mixtures of Generalized Linear Models (DPGLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean function estimate. We also give examples for when those conditions hold, including models for compactly supported continuous distributions and a model with continuous covariates and categorical response. We empirically analyze the properties of the DP-GLM and why it provides better results than existing Dirichlet process mixture regression models. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression like CART, Bayesian trees and Gaussian processes. Compared to existing techniques, the DP-GLM provides a single model (and corresponding inference algorithms) that performs well in many regression settings. Finally, we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whose behavior depends on an exogenous state variable which affects the shape of the objective function. Currently, there is no general purpose algorithm to solve this class of problems. We use nonparametric density estimation to take observations from the joint state-outcome distribution and use them to infer the optimal decision for a given query state. We propose two solution methods that depend on the problem characteristics: function-based and gradient-based optimization. We examine two weighting schemes, kernel-based weights and Dirichlet process-based weights, for use with the solution methods. The weights and solution methods are tested on a synthetic multi-product newsvendor problem and the hour-ahead wind commitment problem. Our results show that in some cases Dirichlet process weights offer substantial benefits over kernel based weights and more generally that nonparametric estimation methods provide good solutions to otherwise intractable problems.

A New Method for Calibrating Perceptual Salience across Dimensions in Infants: The Case of Color vs. Luminance

ERIC Educational Resources Information Center

Kaldy, Zsuzsa; Blaser, Erik A.; Leslie, Alan M.

2006-01-01

We report a new method for calibrating differences in perceptual salience across feature dimensions, in infants. The problem of inter-dimensional salience arises in many areas of infant studies, but a general method for addressing the problem has not previously been described. Our method is based on a preferential looking paradigm, adapted to…

Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!

NASA Astrophysics Data System (ADS)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2017-04-01

Machine learning (ML) is considered to be a promising approach to hydrological processes forecasting. We conduct a comparison between several stochastic and ML point estimation methods by performing large-scale computational experiments based on simulations. The purpose is to provide generalized results, while the respective comparisons in the literature are usually based on case studies. The stochastic methods used include simple methods, models from the frequently used families of Autoregressive Moving Average (ARMA), Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Exponential Smoothing models. The ML methods used are Random Forests (RF), Support Vector Machines (SVM) and Neural Networks (NN). The comparison refers to the multi-step ahead forecasting properties of the methods. A total of 20 methods are used, among which 9 are the ML methods. 12 simulation experiments are performed, while each of them uses 2 000 simulated time series of 310 observations. The time series are simulated using stochastic processes from the families of ARMA and ARFIMA models. Each time series is split into a fitting (first 300 observations) and a testing set (last 10 observations). The comparative assessment of the methods is based on 18 metrics, that quantify the methods' performance according to several criteria related to the accurate forecasting of the testing set, the capturing of its variation and the correlation between the testing and forecasted values. The most important outcome of this study is that there is not a uniformly better or worse method. However, there are methods that are regularly better or worse than others with respect to specific metrics. It appears that, although a general ranking of the methods is not possible, their classification based on their similar or contrasting performance in the various metrics is possible to some extent. Another important conclusion is that more sophisticated methods do not necessarily provide better forecasts compared to simpler methods. It is pointed out that the ML methods do not differ dramatically from the stochastic methods, while it is interesting that the NN, RF and SVM algorithms used in this study offer potentially very good performance in terms of accuracy. It should be noted that, although this study focuses on hydrological processes, the results are of general scientific interest. Another important point in this study is the use of several methods and metrics. Using fewer methods and fewer metrics would have led to a very different overall picture, particularly if those fewer metrics corresponded to fewer criteria. For this reason, we consider that the proposed methodology is appropriate for the evaluation of forecasting methods.

Development of Cross-Assembly Phage PCR-Based Methods for Human Fecal Source Identification

EPA Science Inventory

Technologies that can characterize human fecal pollution in environmental waters offer many advantages over traditional general indicator approaches. However, many human-associated methods cross-react with non-human animal sources and lack suitable sensitivity for fecal source id...

A Fixed-point Scheme for the Numerical Construction of Magnetohydrostatic Atmospheres in Three Dimensions

NASA Astrophysics Data System (ADS)

Gilchrist, S. A.; Braun, D. C.; Barnes, G.

2016-12-01

Magnetohydrostatic models of the solar atmosphere are often based on idealized analytic solutions because the underlying equations are too difficult to solve in full generality. Numerical approaches, too, are often limited in scope and have tended to focus on the two-dimensional problem. In this article we develop a numerical method for solving the nonlinear magnetohydrostatic equations in three dimensions. Our method is a fixed-point iteration scheme that extends the method of Grad and Rubin ( Proc. 2nd Int. Conf. on Peaceful Uses of Atomic Energy 31, 190, 1958) to include a finite gravity force. We apply the method to a test case to demonstrate the method in general and our implementation in code in particular.

Effectiveness of pharmacovigilance training of general practitioners: a retrospective cohort study in the Netherlands comparing two methods.

PubMed

Gerritsen, Roald; Faddegon, Hans; Dijkers, Fred; van Grootheest, Kees; van Puijenbroek, Eugène

2011-09-01

Spontaneous reporting is a cornerstone of pharmacovigilance. Unfamiliarity with the reporting of suspected adverse drug reactions (ADRs) is a major factor leading to not reporting these events. Medical education may promote more effective reporting. Numerous changes have been implemented in medical education over the last decade, with a shift in training methods from those aimed predominantly at the transfer of knowledge towards those that are more practice based and skill oriented. It is conceivable that these changes have an impact on pharmacovigilance training in vocational training programmes. Therefore, this study compares the effectiveness of a skill-oriented, practice-based pharmacovigilance training method, with a traditional, lecture-based pharmacovigilance training method in the vocational training of general practitioners (GPs). The traditional, lecture-based method is common practice in the Netherlands. The purpose of this study was to establish whether the use of a practice-based, skill-oriented method in pharmacovigilance training during GP traineeship leads to an increase of reported ADRs after completion of this traineeship, compared with a lecture-based method. We also investigated whether the applied training method has an impact on the documentation level of the reports and on the number of unlabelled events reported. A retrospective cohort study. The number of ADR reports submitted to the Netherlands Pharmacovigilance Centre Lareb (between January 2006 and October 2010) after completion of GP vocational training was compared between the two groups. Documentation level of the reports and the number of labelled/unlabelled events reported were also compared. The practice-based cohort reported 32 times after completion of training (124 subjects, 6.8 reports per 1000 months of follow-up; total follow-up of 4704 months). The lecture-based cohort reported 12 times after training (135 subjects, 2.1 reports per 1000 months of follow-up; total follow-up of 5824 months) [odds ratio 2.9; 95% CI 1.4, 6.1]. Reports from GPs with practice-based training had a better documentation grade than those from GPs with lecture-based training, and more often concerned unlabelled events. The practice-based method resulted in significantly more and better-documented reports and more often concerned unlabelled events than the lecture-based method. This effect persisted and did not appear to diminish over time.

Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery

NASA Astrophysics Data System (ADS)

Jia, Xiaodong; Zhao, Ming; Di, Yuan; Li, Pin; Lee, Jay

2018-03-01

Sparsity is becoming a more and more important topic in the area of machine learning and signal processing recently. One big family of sparse measures in current literature is the generalized lp /lq norm, which is scale invariant and is widely regarded as normalized lp norm. However, the characteristics of the generalized lp /lq norm are still less discussed and its application to the condition monitoring of rotating devices has been still unexplored. In this study, we firstly discuss the characteristics of the generalized lp /lq norm for sparse optimization and then propose a method of sparse filtering with the generalized lp /lq norm for the purpose of impulsive signature enhancement. Further driven by the trend of industrial big data and the need of reducing maintenance cost for industrial equipment, the proposed sparse filter is customized for vibration signal processing and also implemented on bearing and gearbox for the purpose of condition monitoring. Based on the results from the industrial implementations in this paper, the proposed method has been found to be a promising tool for impulsive feature enhancement, and the superiority of the proposed method over previous methods is also demonstrated.

Uncertainty quantification for environmental models

USGS Publications Warehouse

Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming

2012-01-01

Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10]. There are also bootstrapping and cross-validation approaches.Sometimes analyses are conducted using surrogate models [12]. The availability of so many options can be confusing. Categorizing methods based on fundamental questions assists in communicating the essential results of uncertainty analyses to stakeholders. Such questions can focus on model adequacy (e.g., How well does the model reproduce observed system characteristics and dynamics?) and sensitivity analysis (e.g., What parameters can be estimated with available data? What observations are important to parameters and predictions? What parameters are important to predictions?), as well as on the uncertainty quantification (e.g., How accurate and precise are the predictions?). The methods can also be classified by the number of model runs required: few (10s to 1000s) or many (10,000s to 1,000,000s). Of the methods listed above, the most computationally frugal are generally those based on local derivatives; MCMC methods tend to be among the most computationally demanding. Surrogate models (emulators)do not necessarily produce computational frugality because many runs of the full model are generally needed to create a meaningful surrogate model. With this categorization, we can, in general, address all the fundamental questions mentioned above using either computationally frugal or demanding methods. Model development and analysis can thus be conducted consistently using either computation-ally frugal or demanding methods; alternatively, different fundamental questions can be addressed using methods that require different levels of effort. Based on this perspective, we pose the question: Can computationally frugal methods be useful companions to computationally demanding meth-ods? The reliability of computationally frugal methods generally depends on the model being reasonably linear, which usually means smooth nonlin-earities and the assumption of Gaussian errors; both tend to be more valid with more linear

The generalized Morse wavelet method to determine refractive index dispersion of dielectric films

NASA Astrophysics Data System (ADS)

Kocahan, Özlem; Özcan, Seçkin; Coşkun, Emre; Özder, Serhat

2017-04-01

The continuous wavelet transform (CWT) method is a useful tool for the determination of refractive index dispersion of dielectric films. Mother wavelet selection is an important factor for the accuracy of the results when using CWT. In this study, generalized Morse wavelet (GMW) was proposed as the mother wavelet because of having two degrees of freedom. The simulation studies, based on error calculations and Cauchy Coefficient comparisons, were presented and also the noisy signal was tested by CWT method with GMW. The experimental validity of this method was checked by D263 T schott glass having 100 μm thickness and the results were compared to those from the catalog value.

Equilibrium Solutions of the Logarithmic Hamiltonian Leapfrog for the N-body Problem

NASA Astrophysics Data System (ADS)

Minesaki, Yukitaka

2018-04-01

We prove that a second-order logarithmic Hamiltonian leapfrog for the classical general N-body problem (CGNBP) designed by Mikkola and Tanikawa and some higher-order logarithmic Hamiltonian methods based on symmetric multicompositions of the logarithmic algorithm exactly reproduce the orbits of elliptic relative equilibrium solutions in the original CGNBP. These methods are explicit symplectic methods. Before this proof, only some implicit discrete-time CGNBPs proposed by Minesaki had been analytically shown to trace the orbits of elliptic relative equilibrium solutions. The proof is therefore the first existence proof for explicit symplectic methods. Such logarithmic Hamiltonian methods with a variable time step can also precisely retain periodic orbits in the classical general three-body problem, which generic numerical methods with a constant time step cannot do.

Extended depth of field integral imaging using multi-focus fusion

NASA Astrophysics Data System (ADS)

Piao, Yongri; Zhang, Miao; Wang, Xiaohui; Li, Peihua

2018-03-01

In this paper, we propose a new method for depth of field extension in integral imaging by realizing the image fusion method on the multi-focus elemental images. In the proposed method, a camera is translated on a 2D grid to take multi-focus elemental images by sweeping the focus plane across the scene. Simply applying an image fusion method on the elemental images holding rich parallax information does not work effectively because registration accuracy of images is the prerequisite for image fusion. To solve this problem an elemental image generalization method is proposed. The aim of this generalization process is to geometrically align the objects in all elemental images so that the correct regions of multi-focus elemental images can be exacted. The all-in focus elemental images are then generated by fusing the generalized elemental images using the block based fusion method. The experimental results demonstrate that the depth of field of synthetic aperture integral imaging system has been extended by realizing the generation method combined with the image fusion on multi-focus elemental images in synthetic aperture integral imaging system.

General method of solving the Schroedinger equation of atoms and molecules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakatsuji, Hiroshi

2005-12-15

We propose a general method of solving the Schroedinger equation of atoms and molecules. We first construct the wave function having the exact structure, using the ICI (iterative configuration or complement interaction) method and then optimize the variables involved by the variational principle. Based on the scaled Schroedinger equation and related principles, we can avoid the singularity problem of atoms and molecules and formulate a general method of calculating the exact wave functions in an analytical expansion form. We choose initial function {psi}{sub 0} and scaling g function, and then the ICI method automatically generates the wave function that hasmore » the exact structure by using the Hamiltonian of the system. The Hamiltonian contains all the information of the system. The free ICI method provides a flexible and variationally favorable procedure of constructing the exact wave function. We explain the computational procedure of the analytical ICI method routinely performed in our laboratory. Simple examples are given using hydrogen atom for the nuclear singularity case, the Hooke's atom for the electron singularity case, and the helium atom for both cases.« less

Application of vector-valued rational approximations to the matrix eigenvalue problem and connections with Krylov subspace methods

NASA Technical Reports Server (NTRS)

Sidi, Avram

1992-01-01

Let F(z) be a vectored-valued function F: C approaches C sup N, which is analytic at z=0 and meromorphic in a neighborhood of z=0, and let its Maclaurin series be given. We use vector-valued rational approximation procedures for F(z) that are based on its Maclaurin series in conjunction with power iterations to develop bona fide generalizations of the power method for an arbitrary N X N matrix that may be diagonalizable or not. These generalizations can be used to obtain simultaneously several of the largest distinct eigenvalues and the corresponding invariant subspaces, and present a detailed convergence theory for them. In addition, it is shown that the generalized power methods of this work are equivalent to some Krylov subspace methods, among them the methods of Arnoldi and Lanczos. Thus, the theory provides a set of completely new results and constructions for these Krylov subspace methods. This theory suggests at the same time a new mode of usage for these Krylov subspace methods that were observed to possess computational advantages over their common mode of usage.

Koopmans' theorem in the Hartree-Fock method. General formulation

NASA Astrophysics Data System (ADS)

Plakhutin, Boris N.

2018-03-01

This work presents a general formulation of Koopmans' theorem (KT) in the Hartree-Fock (HF) method which is applicable to molecular and atomic systems with arbitrary orbital occupancies and total electronic spin including orbitally degenerate (OD) systems. The new formulation is based on the full set of variational conditions imposed upon the HF orbitals by the variational principle for the total energy and the conditions imposed by KT on the orbitals of an ionized electronic shell [B. N. Plakhutin and E. R. Davidson, J. Chem. Phys. 140, 014102 (2014)]. Based on these conditions, a general form of the restricted open-shell HF method is developed, whose eigenvalues (orbital energies) obey KT for the whole energy spectrum. Particular attention is paid to the treatment of OD systems, for which the new method gives a number of unexpected results. For example, the present method gives four different orbital energies for the triply degenerate atomic level 2p in the second row atoms B to F. Based on both KT conditions and a parallel treatment of atoms B to F within a limited configuration interaction approach, we prove that these four orbital energies, each of which is triply degenerate, are related via KT to the energies of different spin-dependent ionization and electron attachment processes (2p)N → (2p ) N ±1. A discussion is also presented of specific limitations of the validity of KT in the HF method which arise in OD systems. The practical applicability of the theory is verified by comparing KT estimates of the ionization potentials I2s and I2p for the second row open-shell atoms Li to F with the relevant experimental data.

VHR satellite multitemporal data to extract cultural landscape changes in the roman site of Grumentum

NASA Astrophysics Data System (ADS)

masini, nicola; Lasaponara, Rosa

2013-04-01

The papers deals with the use of VHR satellite multitemporal data set to extract cultural landscape changes in the roman site of Grumentum Grumentum is an ancient town, 50 km south of Potenza, located near the roman road of Via Herculea which connected the Venusia, in the north est of Basilicata, with Heraclea in the Ionian coast. The first settlement date back to the 6th century BC. It was resettled by the Romans in the 3rd century BC. Its urban fabric which evidences a long history from the Republican age to late Antiquity (III BC-V AD) is composed of the typical urban pattern of cardi and decumani. Its excavated ruins include a large amphitheatre, a theatre, the thermae, the Forum and some temples. There are many techniques nowadays available to capture and record differences in two or more images. In this paper we focus and apply the two main approaches which can be distinguished into : (i) unsupervised and (ii) supervised change detection methods. Unsupervised change detection methods are generally based on the transformation of the two multispectral images in to a single band or multiband image which are further analyzed to identify changes Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) a pixel-by-pixel comparison is performed, (iii). Identification of changes according to the magnitude an direction (positive /negative). Unsupervised change detection are generally based on the transformation of the two multispectral images into a single band or multiband image which are further analyzed to identify changes. Than the separation between changed and unchanged classes is obtained from the magnitude of the resulting spectral change vectors by means of empirical or theoretical well founded approaches Supervised change detection methods are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers. Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) supervised classification is performed on the single dates or on the map obtained as the difference of two dates, (iii). Identification of changes according to the magnitude an direction (positive /negative). Supervised change detection are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers, therefore these algorithms require a preliminary knowledge necessary: (i) to generate representative parameters for each class of interest; and (ii) to carry out the training stage Advantages and disadvantages of the supervised and unsupervised approaches are discuss. Finally results from the the satellite multitemporal dataset was also integrated with aerial photos from historical archive in order to expand the time window of the investigation and capture landscape changes occurred from the Agrarian Reform, in the 50s, up today.

A general approach to quantification of hydroxycinnamic acid derivatives and flavones, flavonols, and their glycosides by UV spectrophotometry

USDA-ARS?s Scientific Manuscript database

A general method was developed for the quantification of hydroxycinnamic acid derivatives and flavones, flavonols, and their glycosides based on the UV molar relative response factors (MRRF) of the standards. Each of these phenolic compounds contains a cinnamoyl structure and has a maximum absorban...

Protein Sorting Prediction.

PubMed

Nielsen, Henrik

2017-01-01

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Point cloud registration from local feature correspondences-Evaluation on challenging datasets.

PubMed

Petricek, Tomas; Svoboda, Tomas

2017-01-01

Registration of laser scans, or point clouds in general, is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. A coarse alignment of the point clouds is generally needed before applying local methods such as the Iterative Closest Point (ICP) algorithm. We propose a feature-based approach to point cloud registration and evaluate the proposed method and its individual components on challenging real-world datasets. For a moderate overlap between the laser scans, the method provides a superior registration accuracy compared to state-of-the-art methods including Generalized ICP, 3D Normal-Distribution Transform, Fast Point-Feature Histograms, and 4-Points Congruent Sets. Compared to the surface normals, the points as the underlying features yield higher performance in both keypoint detection and establishing local reference frames. Moreover, sign disambiguation of the basis vectors proves to be an important aspect in creating repeatable local reference frames. A novel method for sign disambiguation is proposed which yields highly repeatable reference frames.

Comparison of Threshold Detection Methods for the Generalized Pareto Distribution (GPD): Application to the NOAA-NCDC Daily Rainfall Dataset

NASA Astrophysics Data System (ADS)

Deidda, Roberto; Mamalakis, Antonis; Langousis, Andreas

2015-04-01

One of the most crucial issues in statistical hydrology is the estimation of extreme rainfall from data. To that extent, based on asymptotic arguments from Extreme Excess (EE) theory, several studies have focused on developing new, or improving existing methods to fit a Generalized Pareto Distribution (GPD) model to rainfall excesses above a properly selected threshold u. The latter is generally determined using various approaches that can be grouped into three basic classes: a) non-parametric methods that locate the changing point between extreme and non-extreme regions of the data, b) graphical methods where one studies the dependence of the GPD parameters (or related metrics) to the threshold level u, and c) Goodness of Fit (GoF) metrics that, for a certain level of significance, locate the lowest threshold u that a GPD model is applicable. In this work, we review representative methods for GPD threshold detection, discuss fundamental differences in their theoretical bases, and apply them to daily rainfall records from the NOAA-NCDC open-access database (http://www.ncdc.noaa.gov/oa/climate/ghcn-daily/). We find that non-parametric methods that locate the changing point between extreme and non-extreme regions of the data are generally not reliable, while graphical methods and GoF metrics that rely on limiting arguments for the upper distribution tail lead to unrealistically high thresholds u. The latter is expected, since one checks the validity of the limiting arguments rather than the applicability of a GPD distribution model. Better performance is demonstrated by graphical methods and GoF metrics that rely on GPD properties. Finally, we discuss the effects of data quantization (common in hydrologic applications) on the estimated thresholds. Acknowledgments: The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State.

Radio frequency electromagnetic field compliance assessment of multi-band and MIMO equipped radio base stations.

PubMed

Thors, Björn; Thielens, Arno; Fridén, Jonas; Colombi, Davide; Törnevik, Christer; Vermeeren, Günter; Martens, Luc; Joseph, Wout

2014-05-01

In this paper, different methods for practical numerical radio frequency exposure compliance assessments of radio base station products were investigated. Both multi-band base station antennas and antennas designed for multiple input multiple output (MIMO) transmission schemes were considered. For the multi-band case, various standardized assessment methods were evaluated in terms of resulting compliance distance with respect to the reference levels and basic restrictions of the International Commission on Non-Ionizing Radiation Protection. Both single frequency and multiple frequency (cumulative) compliance distances were determined using numerical simulations for a mobile communication base station antenna transmitting in four frequency bands between 800 and 2600 MHz. The assessments were conducted in terms of root-mean-squared electromagnetic fields, whole-body averaged specific absorption rate (SAR) and peak 10 g averaged SAR. In general, assessments based on peak field strengths were found to be less computationally intensive, but lead to larger compliance distances than spatial averaging of electromagnetic fields used in combination with localized SAR assessments. For adult exposure, the results indicated that even shorter compliance distances were obtained by using assessments based on localized and whole-body SAR. Numerical simulations, using base station products employing MIMO transmission schemes, were performed as well and were in agreement with reference measurements. The applicability of various field combination methods for correlated exposure was investigated, and best estimate methods were proposed. Our results showed that field combining methods generally considered as conservative could be used to efficiently assess compliance boundary dimensions of single- and dual-polarized multicolumn base station antennas with only minor increases in compliance distances. © 2014 Wiley Periodicals, Inc.

Unbiased nonorthogonal bases for tomographic reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sainz, Isabel; Klimov, Andrei B.; Roa, Luis

2010-05-15

We have developed a general method for constructing a set of nonorthogonal bases with equal separations between all different basis states in prime dimensions. The results are that the corresponding biorthogonal counterparts are pairwise unbiased with the components of the original bases. Using these bases, we derive an explicit expression for the optimal tomography in nonorthogonal bases. A special two-dimensional case is analyzed separately.

A [32P]-NAD+-based method to identify and quantitate long residence time enoyl-ACP reductase inhibitors

PubMed Central

Yu, Weixuan; Neckles, Carla; Chang, Andrew; Bommineni, Gopal Reddy; Spagnuolo, Lauren; Zhang, Zhuo; Liu, Nina; Lai, Christina; Truglio, James; Tonge, Peter J.

2015-01-01

The classical methods for quantifying drug-target residence time (tR) use loss or regain of enzyme activity in progress curve kinetic assays. However, such methods become imprecise at very long residence times, mitigating the use of alternative strategies. Using the NAD(P)H-dependent FabI enoyl-ACP reductase as a model system, we developed a Penefsky column-based method for direct measurement of tR, where the off-rate of the drug was determined with radiolabeled [adenylate-32P] NAD(P+) cofactor. Twenty-three FabI inhibitors were analyzed and a mathematical model was used to estimate limits to the tR values of each inhibitor based on percent drug-target complex recovery following gel filtration. In general, this method showed good agreement with the classical steady state kinetic methods for compounds with tR values of 10-100 min. In addition, we were able to identify seven long tR inhibitors (100-1500 min) and to accurately determine their tR values. The method was then used to measure tR as a function of temperature, an analysis not previously possible using the standard kinetic approach due to decreased NAD(P)H stability at elevated temperatures. In general, a 4-fold difference in tR was observed when the temperature was increased from 25 °C to 37 °C . PMID:25684450

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

PubMed

Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

2018-01-10

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

Audio-Visual Situational Awareness for General Aviation Pilots

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Lodha, Suresh K.; Clancy, Daniel (Technical Monitor)

2001-01-01

Weather is one of the major causes of general aviation accidents. Researchers are addressing this problem from various perspectives including improving meteorological forecasting techniques, collecting additional weather data automatically via on-board sensors and "flight" modems, and improving weather data dissemination and presentation. We approach the problem from the improved presentation perspective and propose weather visualization and interaction methods tailored for general aviation pilots. Our system, Aviation Weather Data Visualization Environment (AWE), utilizes information visualization techniques, a direct manipulation graphical interface, and a speech-based interface to improve a pilot's situational awareness of relevant weather data. The system design is based on a user study and feedback from pilots.

Photovoltaic semiconductor materials based on alloys of tin sulfide, and methods of production

DOEpatents

Lany, Stephan

2016-06-07

Photovoltaic thin-film materials comprising crystalline tin sulfide alloys of the general formula Sn.sub.1-x(R).sub.xS, where R is selected from magnesium, calcium and strontium, as well as methods of producing the same, are disclosed.

A Scenario-Based Parametric Analysis of Stable Marriage Approaches to the Army Officer Assignment Problem

DTIC Science & Technology

2017-03-23

solutions obtained through their proposed method to comparative instances of a generalized assignment problem with either ordinal cost components or... method flag: Designates the method by which the changed/ new assignment problem instance is solved. methodFlag = 0:SMAWarmstart Returns a matching...of randomized perturbations. We examine the contrasts between these methods in the context of assigning Army Officers among a set of identified

[Evaluation of PAE and AE for identifying generalized tracks using snakes in Hidalgo, Mexico].

PubMed

Montiel-Canales, Gustavo; Mayer-Goyenechea, Irene Goyenechea; Fernández Badillo, Leonardo; Castillo Cerón, Jesús M

2016-12-01

One of the most important concepts in Panbiogeography is the generalized track, which represents an ancestral biota fragmented by geological events that can be recovered through several methods, including Parsimony analysis of endemicity (PAE) and endemicity analysis (EA). PAE has been frequently used to identify generalized tracks, while EA is primarily designed to find areas of endemicity, but has been recently proposed for identifying generalized tracks as well. In this study we evaluated these methods to find generalized tracks using the distribution of the 84 snake species of Hidalgo. PAE found one generalized track from three individual tracks (Agkistrodon taylori, Crotalus totonacus and Pliocercus elapoides), supported by 89 % of Bootstrap, and EA identified two generalized tracks, with endemicity index values of 2.71-2.96 and 2.84-3.09, respectively. Those areas were transformed to generalized tracks. The first generalized track was retrieved from three individual tracks (Micrurus bernadi, Rhadinaea marcellae and R. quinquelineata), and the second was recovered from two individual tracks (Geophis mutitorques and Thamnophis sumichrasti). These generalized tracks can be considered a unique distribution pattern, because they resembled each other and agreed in shape. When comparing both methods, we noted that both are useful for identifying generalized tracks, and although they can be used independently, we suggest their complementary use. Nevertheless, to obtain accurate results, it is useful to consider theoretical bases of both methods, along with an appropriate choice of the size of the area. Results using small-grid size in EA are ideal for searching biogeographical patterns within geopolitical limits. Furthermore, they can be used for conservation proposals at state level where endemic species become irreplaceable, and where losing them would imply the extinction of unique lineages.

Use of machine learning methods to classify Universities based on the income structure

NASA Astrophysics Data System (ADS)

Terlyga, Alexandra; Balk, Igor

2017-10-01

In this paper we discuss use of machine learning methods such as self organizing maps, k-means and Ward’s clustering to perform classification of universities based on their income. This classification will allow us to quantitate classification of universities as teaching, research, entrepreneur, etc. which is important tool for government, corporations and general public alike in setting expectation and selecting universities to achieve different goals.

Uncertainty based pressure reconstruction from velocity measurement with generalized least squares

NASA Astrophysics Data System (ADS)

Zhang, Jiacheng; Scalo, Carlo; Vlachos, Pavlos

2017-11-01

A method using generalized least squares reconstruction of instantaneous pressure field from velocity measurement and velocity uncertainty is introduced and applied to both planar and volumetric flow data. Pressure gradients are computed on a staggered grid from flow acceleration. The variance-covariance matrix of the pressure gradients is evaluated from the velocity uncertainty by approximating the pressure gradient error to a linear combination of velocity errors. An overdetermined system of linear equations which relates the pressure and the computed pressure gradients is formulated and then solved using generalized least squares with the variance-covariance matrix of the pressure gradients. By comparing the reconstructed pressure field against other methods such as solving the pressure Poisson equation, the omni-directional integration, and the ordinary least squares reconstruction, generalized least squares method is found to be more robust to the noise in velocity measurement. The improvement on pressure result becomes more remarkable when the velocity measurement becomes less accurate and more heteroscedastic. The uncertainty of the reconstructed pressure field is also quantified and compared across the different methods.

A robust generalized fuzzy operator approach to film contrast correction in digital subtraction radiography.

PubMed

Leung, Chung-Chu

2006-03-01

Digital subtraction radiography requires close matching of the contrast in each pair of X-ray images to be subtracted. Previous studies have shown that nonparametric contrast/brightness correction methods using the cumulative density function (CDF) and its improvements, which are based on gray-level transformation associated with the pixel histogram, perform well in uniform contrast/brightness difference conditions. However, for radiographs with nonuniform contrast/ brightness, the CDF produces unsatisfactory results. In this paper, we propose a new approach in contrast correction based on the generalized fuzzy operator with least square method. The result shows that 50% of the contrast/brightness errors can be corrected using this approach when the contrast/brightness difference between a radiographic pair is 10 U. A comparison of our approach with that of CDF is presented, and this modified GFO method produces better contrast normalization results than the CDF approach.

Control theory based airfoil design using the Euler equations

NASA Technical Reports Server (NTRS)

Jameson, Antony; Reuther, James

1994-01-01

This paper describes the implementation of optimization techniques based on control theory for airfoil design. In our previous work it was shown that control theory could be employed to devise effective optimization procedures for two-dimensional profiles by using the potential flow equation with either a conformal mapping or a general coordinate system. The goal of our present work is to extend the development to treat the Euler equations in two-dimensions by procedures that can readily be generalized to treat complex shapes in three-dimensions. Therefore, we have developed methods which can address airfoil design through either an analytic mapping or an arbitrary grid perturbation method applied to a finite volume discretization of the Euler equations. Here the control law serves to provide computationally inexpensive gradient information to a standard numerical optimization method. Results are presented for both the inverse problem and drag minimization problem.

Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency

NASA Astrophysics Data System (ADS)

Liu, Changjiang; Cheng, Irene; Zhang, Yi; Basu, Anup

2017-06-01

This paper presents an improved multi-scale Retinex (MSR) based enhancement for ariel images under low visibility. For traditional multi-scale Retinex, three scales are commonly employed, which limits its application scenarios. We extend our research to a general purpose enhanced method, and design an MSR with more than three scales. Based on the mathematical analysis and deductions, an explicit multi-scale representation is proposed that balances image contrast and color consistency. In addition, a histogram truncation technique is introduced as a post-processing strategy to remap the multi-scale Retinex output to the dynamic range of the display. Analysis of experimental results and comparisons with existing algorithms demonstrate the effectiveness and generality of the proposed method. Results on image quality assessment proves the accuracy of the proposed method with respect to both objective and subjective criteria.

Variable context Markov chains for HIV protease cleavage site prediction.

PubMed

Oğul, Hasan

2009-06-01

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.

New-Sum: A Novel Online ABFT Scheme For General Iterative Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tao, Dingwen; Song, Shuaiwen; Krishnamoorthy, Sriram

Emerging high-performance computing platforms, with large component counts and lower power margins, are anticipated to be more susceptible to soft errors in both logic circuits and memory subsystems. We present an online algorithm-based fault tolerance (ABFT) approach to efficiently detect and recover soft errors for general iterative methods. We design a novel checksum-based encoding scheme for matrix-vector multiplication that is resilient to both arithmetic and memory errors. Our design decouples the checksum updating process from the actual computation, and allows adaptive checksum overhead control. Building on this new encoding mechanism, we propose two online ABFT designs that can effectively recovermore » from errors when combined with a checkpoint/rollback scheme.« less

Automatic classification of artifactual ICA-components for artifact removal in EEG signals.

PubMed

Winkler, Irene; Haufe, Stefan; Tangermann, Michael

2011-08-02

Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.

Critical review and hydrologic application of threshold detection methods for the generalized Pareto (GP) distribution

NASA Astrophysics Data System (ADS)

Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto

2016-04-01

Estimation of extreme rainfall from data constitutes one of the most important issues in statistical hydrology, as it is associated with the design of hydraulic structures and flood water management. To that extent, based on asymptotic arguments from Extreme Excess (EE) theory, several studies have focused on developing new, or improving existing methods to fit a generalized Pareto (GP) distribution model to rainfall excesses above a properly selected threshold u. The latter is generally determined using various approaches, such as non-parametric methods that are intended to locate the changing point between extreme and non-extreme regions of the data, graphical methods where one studies the dependence of GP distribution parameters (or related metrics) on the threshold level u, and Goodness of Fit (GoF) metrics that, for a certain level of significance, locate the lowest threshold u that a GP distribution model is applicable. In this work, we review representative methods for GP threshold detection, discuss fundamental differences in their theoretical bases, and apply them to 1714 daily rainfall records from the NOAA-NCDC open-access database, with more than 110 years of data. We find that non-parametric methods that are intended to locate the changing point between extreme and non-extreme regions of the data are generally not reliable, while methods that are based on asymptotic properties of the upper distribution tail lead to unrealistically high threshold and shape parameter estimates. The latter is justified by theoretical arguments, and it is especially the case in rainfall applications, where the shape parameter of the GP distribution is low; i.e. on the order of 0.1 ÷ 0.2. Better performance is demonstrated by graphical methods and GoF metrics that rely on pre-asymptotic properties of the GP distribution. For daily rainfall, we find that GP threshold estimates range between 2÷12 mm/d with a mean value of 6.5 mm/d, while the existence of quantization in the empirical records, as well as variations in their size, constitute the two most important factors that may significantly affect the accuracy of the obtained results. Acknowledgments The research project was implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and co-financed by the European Social Fund (ESF) and the Greek State. The work conducted by Roberto Deidda was funded under the Sardinian Regional Law 7/2007 (funding call 2013).

Reinforcing loose foundation stones in trait-based plant ecology.

PubMed

Shipley, Bill; De Bello, Francesco; Cornelissen, J Hans C; Laliberté, Etienne; Laughlin, Daniel C; Reich, Peter B

2016-04-01

The promise of "trait-based" plant ecology is one of generalized prediction across organizational and spatial scales, independent of taxonomy. This promise is a major reason for the increased popularity of this approach. Here, we argue that some important foundational assumptions of trait-based ecology have not received sufficient empirical evaluation. We identify three such assumptions and, where possible, suggest methods of improvement: (i) traits are functional to the degree that they determine individual fitness, (ii) intraspecific variation in functional traits can be largely ignored, and (iii) functional traits show general predictive relationships to measurable environmental gradients.

A flexible motif search technique based on generalized profiles.

PubMed

Bucher, P; Karplus, K; Moeri, N; Hofmann, K

1996-03-01

A flexible motif search technique is presented which has two major components: (1) a generalized profile syntax serving as a motif definition language; and (2) a motif search method specifically adapted to the problem of finding multiple instances of a motif in the same sequence. The new profile structure, which is the core of the generalized profile syntax, combines the functions of a variety of motif descriptors implemented in other methods, including regular expression-like patterns, weight matrices, previously used profiles, and certain types of hidden Markov models (HMMs). The relationship between generalized profiles and other biomolecular motif descriptors is analyzed in detail, with special attention to HMMs. Generalized profiles are shown to be equivalent to a particular class of HMMs, and conversion procedures in both directions are given. The conversion procedures provide an interpretation for local alignment in the framework of stochastic models, allowing for clear, simple significance tests. A mathematical statement of the motif search problem defines the new method exactly without linking it to a specific algorithmic solution. Part of the definition includes a new definition of disjointness of alignments.

Spherically-symmetric solutions in general relativity using a tetrad-based approach

NASA Astrophysics Data System (ADS)

Kim, Do Young; Lasenby, Anthony N.; Hobson, Michael P.

2018-03-01

We present a tetrad-based method for solving the Einstein field equations for spherically-symmetric systems and compare it with the widely-used Lemaître-Tolman-Bondi (LTB) model. In particular, we focus on the issues of gauge ambiguity and the use of comoving versus `physical' coordinate systems. We also clarify the correspondences between the two approaches, and illustrate their differences by applying them to the classic examples of the Schwarzschild and Friedmann-Lemaître-Robertson-Walker spacetimes. We demonstrate that the tetrad-based method does not suffer from the gauge freedoms inherent to the LTB model, naturally accommodates non-uniform pressure and has a more transparent physical interpretation. We further apply our tetrad-based method to a generalised form of `Swiss cheese' model, which consists of an interior spherical region surrounded by a spherical shell of vacuum that is embedded in an exterior background universe. In general, we allow the fluid in the interior and exterior regions to support pressure, and do not demand that the interior region be compensated. We pay particular attention to the form of the solution in the intervening vacuum region and illustrate the validity of Birkhoff's theorem at both the metric and tetrad level. We then reconsider critically the original theoretical arguments underlying the so-called Rh = ct cosmological model, which has recently received considerable attention. These considerations in turn illustrate the interesting behaviour of a number of `horizons' in general cosmological models.

Spectrophotometric and spectrofluorimetric methods for determination of certain biologically active phenolic drugs in their bulk powders and different pharmaceutical formulations.

PubMed

Omar, Mahmoud A; Badr El-Din, Kalid M; Salem, Hesham; Abdelmageed, Osama H

2018-03-05

Two simple and sensitive spectrophotometric and spectrofluorimetric methods for the determination of terbutaline sulfate, fenoterol hydrobromide, etilefrine hydrochloride, isoxsuprine hydrochloride, ethamsylate, doxycycline hyclate have been developed. Both methods were based on the oxidation of the cited drugs with cerium (IV) in acid medium. The spectrophotometric method was based on measurement of the absorbance difference (ΔA), which represents the excess cerium (IV), at 317nm for each drug. On the other hand, the spectrofluorimetric method was based on measurement of the fluorescent of the produced cerium (III) at emission wavelength 354nm (λ excitation =255nm) for the concentrations studied for each drug. For both methods, the variables affecting the reactions were carefully investigated and the conditions were optimized. Linear relationships were found between either ΔA or the fluorescent of the produced cerium (III) values and the concentration of the studied drugs in a general concentration range of 2.0-24.0μgmL -1 , 20.0-24.0ngmL -1 with good correlation coefficients in the following range 0.9990-0.9999, 0.9990-0.9993 for spectrophotometric and spectrofluorimetric methods respectively. The limits of detection and quantitation of spectrophotometric method were found in general concentration range 0.190-0.787 and 0.634-2.624μgmL -1 respectively. For spectrofluorimetric method, the limits of detection and quantitation were found in general concentration range 4.77-9.52 and 15.91-31.74ngmL -1 respectively. The stoichiometry of the reaction was determined, and the reactions pathways were postulated. The analytical performance of the methods, in terms of accuracy and precision, were statistically validated and the results obtained were satisfactory. The methods have been successfully applied to the determination of the cited drugs in their commercial pharmaceutical formulations. Statistical comparison of the results with the reference methods showed excellent agreement and proved that no significant difference in the accuracy and precision. Copyright © 2017 Elsevier B.V. All rights reserved.

The use of Lanczos's method to solve the large generalized symmetric definite eigenvalue problem

NASA Technical Reports Server (NTRS)

Jones, Mark T.; Patrick, Merrell L.

1989-01-01

The generalized eigenvalue problem, Kx = Lambda Mx, is of significant practical importance, especially in structural enginering where it arises as the vibration and buckling problem. A new algorithm, LANZ, based on Lanczos's method is developed. LANZ uses a technique called dynamic shifting to improve the efficiency and reliability of the Lanczos algorithm. A new algorithm for solving the tridiagonal matrices that arise when using Lanczos's method is described. A modification of Parlett and Scott's selective orthogonalization algorithm is proposed. Results from an implementation of LANZ on a Convex C-220 show it to be superior to a subspace iteration code.

Research and applications: Artificial intelligence

NASA Technical Reports Server (NTRS)

Raphael, B.; Fikes, R. E.; Chaitin, L. J.; Hart, P. E.; Duda, R. O.; Nilsson, N. J.

1971-01-01

A program of research in the field of artificial intelligence is presented. The research areas discussed include automatic theorem proving, representations of real-world environments, problem-solving methods, the design of a programming system for problem-solving research, techniques for general scene analysis based upon television data, and the problems of assembling an integrated robot system. Major accomplishments include the development of a new problem-solving system that uses both formal logical inference and informal heuristic methods, the development of a method of automatic learning by generalization, and the design of the overall structure of a new complete robot system. Eight appendices to the report contain extensive technical details of the work described.

Mean-Reverting Portfolio With Budget Constraint

NASA Astrophysics Data System (ADS)

Zhao, Ziping; Palomar, Daniel P.

2018-05-01

This paper considers the mean-reverting portfolio design problem arising from statistical arbitrage in the financial markets. We first propose a general problem formulation aimed at finding a portfolio of underlying component assets by optimizing a mean-reversion criterion characterizing the mean-reversion strength, taking into consideration the variance of the portfolio and an investment budget constraint. Then several specific problems are considered based on the general formulation, and efficient algorithms are proposed. Numerical results on both synthetic and market data show that our proposed mean-reverting portfolio design methods can generate consistent profits and outperform the traditional design methods and the benchmark methods in the literature.

Version Control in Project-Based Learning

ERIC Educational Resources Information Center

Milentijevic, Ivan; Ciric, Vladimir; Vojinovic, Oliver

2008-01-01

This paper deals with the development of a generalized model for version control systems application as a support in a range of project-based learning methods. The model is given as UML sequence diagram and described in detail. The proposed model encompasses a wide range of different project-based learning approaches by assigning a supervisory…

Concept Cartoons Supported Problem Based Learning Method in Middle School Science Classrooms

ERIC Educational Resources Information Center

Balim, Ali Günay; Inel-Ekici, Didem; Özcan, Erkan

2016-01-01

Problem based learning, in which events from daily life are presented as interesting scenarios, is one of the active learning approaches that encourages students to self-direct learning. Problem based learning, generally used in higher education, requires students to use high end thinking skills in learning environments. In order to use…

Development of an irrigation scheduling software based on model predicted crop water stress

USDA-ARS?s Scientific Manuscript database

Modern irrigation scheduling methods are generally based on sensor-monitored soil moisture regimes rather than crop water stress which is difficult to measure in real-time, but can be computed using agricultural system models. In this study, an irrigation scheduling software based on RZWQM2 model pr...

Group Decision Making Based on Heronian Aggregation Operators of Intuitionistic Fuzzy Numbers.

PubMed

Liu, Peide; Chen, Shyi-Ming

2017-09-01

Archimedean t -conorm and t -norm provide the general operational rules for intuitionistic fuzzy numbers (IFNs). The aggregation operators based on them can generalize most of the existing aggregation operators. At the same time, the Heronian mean (HM) has a significant advantage of considering interrelationships between the attributes. Therefore, it is very necessary to extend the HM based on IFNs and to construct intuitionistic fuzzy HM operators based on the Archimedean t -conorm and t -norm. In this paper, we first discuss intuitionistic fuzzy operational rules based on the Archimedean t -conorm and t -norm. Then, we propose the intuitionistic fuzzy Archimedean Heronian aggregation (IFAHA) operator and the intuitionistic fuzzy weight Archimedean Heronian aggregation (IFWAHA) operator. We also further discuss some properties and some special cases of these new operators. Moreover, we also propose a new multiple attribute group decision making (MAGDM) method based on the proposed IFAHA operator and the proposed IFWAHA operator. Finally, we use an illustrative example to show the MAGDM processes and to illustrate the effectiveness of the developed method.

Generalization Performance of Regularized Ranking With Multiscale Kernels.

PubMed

Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin

2016-05-01

The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

PubMed Central

2011-01-01

Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. PMID:21810266

An Assessment of Phylogenetic Tools for Analyzing the Interplay Between Interspecific Interactions and Phenotypic Evolution.

PubMed

Drury, J P; Grether, G F; Garland, T; Morlon, H

2018-05-01

Much ecological and evolutionary theory predicts that interspecific interactions often drive phenotypic diversification and that species phenotypes in turn influence species interactions. Several phylogenetic comparative methods have been developed to assess the importance of such processes in nature; however, the statistical properties of these methods have gone largely untested. Focusing mainly on scenarios of competition between closely-related species, we assess the performance of available comparative approaches for analyzing the interplay between interspecific interactions and species phenotypes. We find that many currently used statistical methods often fail to detect the impact of interspecific interactions on trait evolution, that sister-taxa analyses are particularly unreliable in general, and that recently developed process-based models have more satisfactory statistical properties. Methods for detecting predictors of species interactions are generally more reliable than methods for detecting character displacement. In weighing the strengths and weaknesses of different approaches, we hope to provide a clear guide for empiricists testing hypotheses about the reciprocal effect of interspecific interactions and species phenotypes and to inspire further development of process-based models.

7 CFR 770.3 - Eligibility requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... without adequate uncommitted funds, based on Generally Accepted Accounting Principles, or another financial accounting method acceptable to Secretary of Interior to acquire lands or interests therein within...

Developing a multimodal biometric authentication system using soft computing methods.

PubMed

Malcangi, Mario

2015-01-01

Robust personal authentication is becoming ever more important in computer-based applications. Among a variety of methods, biometric offers several advantages, mainly in embedded system applications. Hard and soft multi-biometric, combined with hard and soft computing methods, can be applied to improve the personal authentication process and to generalize the applicability. This chapter describes the embedded implementation of a multi-biometric (voiceprint and fingerprint) multimodal identification system based on hard computing methods (DSP) for feature extraction and matching, an artificial neural network (ANN) for soft feature pattern matching, and a fuzzy logic engine (FLE) for data fusion and decision.

Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology

PubMed Central

Gao, Fei; Li, Ye; Novak, Igor L.; Slepchenko, Boris M.

2016-01-01

Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium ‘sparks’ as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell. PMID:27959915

Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration

NASA Astrophysics Data System (ADS)

Chen, Guoxiong; Cheng, Qiuming

2016-02-01

Multi-resolution and scale-invariance have been increasingly recognized as two closely related intrinsic properties endowed in geofields such as geochemical and geophysical anomalies, and they are commonly investigated by using multiscale- and scaling-analysis methods. In this paper, the wavelet-based multiscale decomposition (WMD) method was proposed to investigate the multiscale natures of geochemical pattern from large scale to small scale. In the light of the wavelet transformation of fractal measures, we demonstrated that the wavelet approximation operator provides a generalization of box-counting method for scaling analysis of geochemical patterns. Specifically, the approximation coefficient acts as the generalized density-value in density-area fractal modeling of singular geochemical distributions. Accordingly, we presented a novel local singularity analysis (LSA) using the WMD algorithm which extends the conventional moving averaging to a kernel-based operator for implementing LSA. Finally, the novel LSA was validated using a case study dealing with geochemical data (Fe2O3) in stream sediments for mineral exploration in Inner Mongolia, China. In comparison with the LSA implemented using the moving averaging method the novel LSA using WMD identified improved weak geochemical anomalies associated with mineralization in covered area.

A partial least squares based spectrum normalization method for uncertainty reduction for laser-induced breakdown spectroscopy measurements

NASA Astrophysics Data System (ADS)

Li, Xiongwei; Wang, Zhe; Lui, Siu-Lung; Fu, Yangting; Li, Zheng; Liu, Jianming; Ni, Weidou

2013-10-01

A bottleneck of the wide commercial application of laser-induced breakdown spectroscopy (LIBS) technology is its relatively high measurement uncertainty. A partial least squares (PLS) based normalization method was proposed to improve pulse-to-pulse measurement precision for LIBS based on our previous spectrum standardization method. The proposed model utilized multi-line spectral information of the measured element and characterized the signal fluctuations due to the variation of plasma characteristic parameters (plasma temperature, electron number density, and total number density) for signal uncertainty reduction. The model was validated by the application of copper concentration prediction in 29 brass alloy samples. The results demonstrated an improvement on both measurement precision and accuracy over the generally applied normalization as well as our previously proposed simplified spectrum standardization method. The average relative standard deviation (RSD), average of the standard error (error bar), the coefficient of determination (R2), the root-mean-square error of prediction (RMSEP), and average value of the maximum relative error (MRE) were 1.80%, 0.23%, 0.992, 1.30%, and 5.23%, respectively, while those for the generally applied spectral area normalization were 3.72%, 0.71%, 0.973, 1.98%, and 14.92%, respectively.

A Monte-Carlo method which is not based on Markov chain algorithm, used to study electrostatic screening of ion potential

NASA Astrophysics Data System (ADS)

Šantić, Branko; Gracin, Davor

2017-12-01

A new simple Monte Carlo method is introduced for the study of electrostatic screening by surrounding ions. The proposed method is not based on the generally used Markov chain method for sample generation. Each sample is pristine and there is no correlation with other samples. As the main novelty, the pairs of ions are gradually added to a sample provided that the energy of each ion is within the boundaries determined by the temperature and the size of ions. The proposed method provides reliable results, as demonstrated by the screening of ion in plasma and in water.

The constraint method: A new finite element technique. [applied to static and dynamic loads on plates

NASA Technical Reports Server (NTRS)

Tsai, C.; Szabo, B. A.

1973-01-01

An approch to the finite element method which utilizes families of conforming finite elements based on complete polynomials is presented. Finite element approximations based on this method converge with respect to progressively reduced element sizes as well as with respect to progressively increasing orders of approximation. Numerical results of static and dynamic applications of plates are presented to demonstrate the efficiency of the method. Comparisons are made with plate elements in NASTRAN and the high-precision plate element developed by Cowper and his co-workers. Some considerations are given to implementation of the constraint method into general purpose computer programs such as NASTRAN.

The Analysis of Seawater: A Laboratory-Centered Learning Project in General Chemistry.

ERIC Educational Resources Information Center

Selco, Jodye I.; Roberts, Julian L., Jr.; Wacks, Daniel B.

2003-01-01

Describes a sea-water analysis project that introduces qualitative and quantitative analysis methods and laboratory methods such as gravimetric analysis, potentiometric titration, ion-selective electrodes, and the use of calibration curves. Uses a problem-based cooperative teaching approach. (Contains 24 references.) (YDS)

A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography.

PubMed

Prakash, Jaya; Yalavarthy, Phaneendra K

2013-03-01

Developing a computationally efficient automated method for the optimal choice of regularization parameter in diffuse optical tomography. The least-squares QR (LSQR)-type method that uses Lanczos bidiagonalization is known to be computationally efficient in performing the reconstruction procedure in diffuse optical tomography. The same is effectively deployed via an optimization procedure that uses the simplex method to find the optimal regularization parameter. The proposed LSQR-type method is compared with the traditional methods such as L-curve, generalized cross-validation (GCV), and recently proposed minimal residual method (MRM)-based choice of regularization parameter using numerical and experimental phantom data. The results indicate that the proposed LSQR-type and MRM-based methods performance in terms of reconstructed image quality is similar and superior compared to L-curve and GCV-based methods. The proposed method computational complexity is at least five times lower compared to MRM-based method, making it an optimal technique. The LSQR-type method was able to overcome the inherent limitation of computationally expensive nature of MRM-based automated way finding the optimal regularization parameter in diffuse optical tomographic imaging, making this method more suitable to be deployed in real-time.

Generalized Lagrange Jacobi Gauss-Lobatto (GLJGL) Collocation Method for Solving Linear and Nonlinear Fokker-Planck Equations

NASA Astrophysics Data System (ADS)

Parand, K.; Latifi, S.; Moayeri, M. M.; Delkhosh, M.

2018-05-01

In this study, we have constructed a new numerical approach for solving the time-dependent linear and nonlinear Fokker-Planck equations. In fact, we have discretized the time variable with Crank-Nicolson method and for the space variable, a numerical method based on Generalized Lagrange Jacobi Gauss-Lobatto (GLJGL) collocation method is applied. It leads to in solving the equation in a series of time steps and at each time step, the problem is reduced to a problem consisting of a system of algebraic equations that greatly simplifies the problem. One can observe that the proposed method is simple and accurate. Indeed, one of its merits is that it is derivative-free and by proposing a formula for derivative matrices, the difficulty aroused in calculation is overcome, along with that it does not need to calculate the General Lagrange basis and matrices; they have Kronecker property. Linear and nonlinear Fokker-Planck equations are given as examples and the results amply demonstrate that the presented method is very valid, effective, reliable and does not require any restrictive assumptions for nonlinear terms.

Angular Multigrid Preconditioner for Krylov-Based Solution Techniques Applied to the Sn Equations with Highly Forward-Peaked Scattering

NASA Astrophysics Data System (ADS)

Turcksin, Bruno; Ragusa, Jean C.; Morel, Jim E.

2012-01-01

It is well known that the diffusion synthetic acceleration (DSA) methods for the Sn equations become ineffective in the Fokker-Planck forward-peaked scattering limit. In response to this deficiency, Morel and Manteuffel (1991) developed an angular multigrid method for the 1-D Sn equations. This method is very effective, costing roughly twice as much as DSA per source iteration, and yielding a maximum spectral radius of approximately 0.6 in the Fokker-Planck limit. Pautz, Adams, and Morel (PAM) (1999) later generalized the angular multigrid to 2-D, but it was found that the method was unstable with sufficiently forward-peaked mappings between the angular grids. The method was stabilized via a filtering technique based on diffusion operators, but this filtering also degraded the effectiveness of the overall scheme. The spectral radius was not bounded away from unity in the Fokker-Planck limit, although the method remained more effective than DSA. The purpose of this article is to recast the multidimensional PAM angular multigrid method without the filtering as an Sn preconditioner and use it in conjunction with the Generalized Minimal RESidual (GMRES) Krylov method. The approach ensures stability and our computational results demonstrate that it is also significantly more efficient than an analogous DSA-preconditioned Krylov method.

Invited commentary: Lost in estimation--searching for alternatives to markov chains to fit complex Bayesian models.

PubMed

Molitor, John

2012-03-01

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

Construction of measurement uncertainty profiles for quantitative analysis of genetically modified organisms based on interlaboratory validation data.

PubMed

Macarthur, Roy; Feinberg, Max; Bertheau, Yves

2010-01-01

A method is presented for estimating the size of uncertainty associated with the measurement of products derived from genetically modified organisms (GMOs). The method is based on the uncertainty profile, which is an extension, for the estimation of uncertainty, of a recent graphical statistical tool called an accuracy profile that was developed for the validation of quantitative analytical methods. The application of uncertainty profiles as an aid to decision making and assessment of fitness for purpose is also presented. Results of the measurement of the quantity of GMOs in flour by PCR-based methods collected through a number of interlaboratory studies followed the log-normal distribution. Uncertainty profiles built using the results generally give an expected range for measurement results of 50-200% of reference concentrations for materials that contain at least 1% GMO. This range is consistent with European Network of GM Laboratories and the European Union (EU) Community Reference Laboratory validation criteria and can be used as a fitness for purpose criterion for measurement methods. The effect on the enforcement of EU labeling regulations is that, in general, an individual analytical result needs to be < 0.45% to demonstrate compliance, and > 1.8% to demonstrate noncompliance with a labeling threshold of 0.9%.

Promoting a structural view of biology for varied audiences: an overview of RCSB PDB resources and experiences.

PubMed

Dutta, Shuchismita; Zardecki, Christine; Goodsell, David S; Berman, Helen M

2010-10-01

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) supports scientific research and education worldwide by providing an essential resource of information on biomolecular structures. In addition to serving as a deposition, data-processing and distribution center for PDB data, the RCSB PDB offers resources and online materials that different audiences can use to customize their structural biology instruction. These include resources for general audiences that present macromolecular structure in the context of a biological theme, method-based materials for researchers who take a more traditional approach to the presentation of structural science, and materials that mix theme-based and method-based approaches for educators and students. Through these efforts the RCSB PDB aims to enable optimal use of structural data by researchers, educators and students designing and understanding experiments in biology, chemistry and medicine, and by general users making informed decisions about their life and health.

A support vector machine based control application to the experimental three-tank system.

PubMed

Iplikci, Serdar

2010-07-01

This paper presents a support vector machine (SVM) approach to generalized predictive control (GPC) of multiple-input multiple-output (MIMO) nonlinear systems. The possession of higher generalization potential and at the same time avoidance of getting stuck into the local minima have motivated us to employ SVM algorithms for modeling MIMO systems. Based on the SVM model, detailed and compact formulations for calculating predictions and gradient information, which are used in the computation of the optimal control action, are given in the paper. The proposed MIMO SVM-based GPC method has been verified on an experimental three-tank liquid level control system. Experimental results have shown that the proposed method can handle the control task successfully for different reference trajectories. Moreover, a detailed discussion on data gathering, model selection and effects of the control parameters have been given in this paper. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data

PubMed Central

Li, Jun; Tibshirani, Robert

2015-01-01

We discuss the identification of features that are associated with an outcome in RNA-Sequencing (RNA-Seq) and other sequencing-based comparative genomic experiments. RNA-Seq data takes the form of counts, so models based on the normal distribution are generally unsuitable. The problem is especially challenging because different sequencing experiments may generate quite different total numbers of reads, or ‘sequencing depths’. Existing methods for this problem are based on Poisson or negative binomial models: they are useful but can be heavily influenced by ‘outliers’ in the data. We introduce a simple, nonparametric method with resampling to account for the different sequencing depths. The new method is more robust than parametric methods. It can be applied to data with quantitative, survival, two-class or multiple-class outcomes. We compare our proposed method to Poisson and negative binomial-based methods in simulated and real data sets, and find that our method discovers more consistent patterns than competing methods. PMID:22127579

Wave propagation in anisotropic elastic materials and curvilinear coordinates using a summation-by-parts finite difference method

DOE PAGES

Petersson, N. Anders; Sjogreen, Bjorn

2015-07-20

We develop a fourth order accurate finite difference method for solving the three-dimensional elastic wave equation in general heterogeneous anisotropic materials on curvilinear grids. The proposed method is an extension of the method for isotropic materials, previously described in the paper by Sjögreen and Petersson (2012) [11]. The method we proposed discretizes the anisotropic elastic wave equation in second order formulation, using a node centered finite difference method that satisfies the principle of summation by parts. The summation by parts technique results in a provably stable numerical method that is energy conserving. Also, we generalize and evaluate the super-grid far-fieldmore » technique for truncating unbounded domains. Unlike the commonly used perfectly matched layers (PML), the super-grid technique is stable for general anisotropic material, because it is based on a coordinate stretching combined with an artificial dissipation. Moreover, the discretization satisfies an energy estimate, proving that the numerical approximation is stable. We demonstrate by numerical experiments that sufficiently wide super-grid layers result in very small artificial reflections. Applications of the proposed method are demonstrated by three-dimensional simulations of anisotropic wave propagation in crystals.« less

Methods for estimating flood frequency in Montana based on data through water year 1998

USGS Publications Warehouse

Parrett, Charles; Johnson, Dave R.

2004-01-01

Annual peak discharges having recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years (T-year floods) were determined for 660 gaged sites in Montana and in adjacent areas of Idaho, Wyoming, and Canada, based on data through water year 1998. The updated flood-frequency information was subsequently used in regression analyses, either ordinary or generalized least squares, to develop equations relating T-year floods to various basin and climatic characteristics, equations relating T-year floods to active-channel width, and equations relating T-year floods to bankfull width. The equations can be used to estimate flood frequency at ungaged sites. Montana was divided into eight regions, within which flood characteristics were considered to be reasonably homogeneous, and the three sets of regression equations were developed for each region. A measure of the overall reliability of the regression equations is the average standard error of prediction. The average standard errors of prediction for the equations based on basin and climatic characteristics ranged from 37.4 percent to 134.1 percent. Average standard errors of prediction for the equations based on active-channel width ranged from 57.2 percent to 141.3 percent. Average standard errors of prediction for the equations based on bankfull width ranged from 63.1 percent to 155.5 percent. In most regions, the equations based on basin and climatic characteristics generally had smaller average standard errors of prediction than equations based on active-channel or bankfull width. An exception was the Southeast Plains Region, where all equations based on active-channel width had smaller average standard errors of prediction than equations based on basin and climatic characteristics or bankfull width. Methods for weighting estimates derived from the basin- and climatic-characteristic equations and the channel-width equations also were developed. The weights were based on the cross correlation of residuals from the different methods and the average standard errors of prediction. When all three methods were combined, the average standard errors of prediction ranged from 37.4 percent to 120.2 percent. Weighting of estimates reduced the standard errors of prediction for all T-year flood estimates in four regions, reduced the standard errors of prediction for some T-year flood estimates in two regions, and provided no reduction in average standard error of prediction in two regions. A computer program for solving the regression equations, weighting estimates, and determining reliability of individual estimates was developed and placed on the USGS Montana District World Wide Web page. A new regression method, termed Region of Influence regression, also was tested. Test results indicated that the Region of Influence method was not as reliable as the regional equations based on generalized least squares regression. Two additional methods for estimating flood frequency at ungaged sites located on the same streams as gaged sites also are described. The first method, based on a drainage-area-ratio adjustment, is intended for use on streams where the ungaged site of interest is located near a gaged site. The second method, based on interpolation between gaged sites, is intended for use on streams that have two or more streamflow-gaging stations.

A practical material decomposition method for x-ray dual spectral computed tomography.

PubMed

Hu, Jingjing; Zhao, Xing

2016-03-17

X-ray dual spectral CT (DSCT) scans the measured object with two different x-ray spectra, and the acquired rawdata can be used to perform the material decomposition of the object. Direct calibration methods allow a faster material decomposition for DSCT and can be separated in two groups: image-based and rawdata-based. The image-based method is an approximative method, and beam hardening artifacts remain in the resulting material-selective images. The rawdata-based method generally obtains better image quality than the image-based method, but this method requires geometrically consistent rawdata. However, today's clinical dual energy CT scanners usually measure different rays for different energy spectra and acquire geometrically inconsistent rawdata sets, and thus cannot meet the requirement. This paper proposes a practical material decomposition method to perform rawdata-based material decomposition in the case of inconsistent measurement. This method first yields the desired consistent rawdata sets from the measured inconsistent rawdata sets, and then employs rawdata-based technique to perform material decomposition and reconstruct material-selective images. The proposed method was evaluated by use of simulated FORBILD thorax phantom rawdata and dental CT rawdata, and simulation results indicate that this method can produce highly quantitative DSCT images in the case of inconsistent DSCT measurements.

Parent Ratings of ADHD Symptoms: Generalized Partial Credit Model Analysis of Differential Item Functioning across Gender

ERIC Educational Resources Information Center

Gomez, Rapson

2012-01-01

Objective: Generalized partial credit model, which is based on item response theory (IRT), was used to test differential item functioning (DIF) for the "Diagnostic and Statistical Manual of Mental Disorders" (4th ed.), inattention (IA), and hyperactivity/impulsivity (HI) symptoms across boys and girls. Method: To accomplish this, parents completed…

Coping as a Predictor of Burnout and General Health in Therapists Working in ABA Schools

ERIC Educational Resources Information Center

Griffith, G. M.; Barbakou, A.; Hastings, R. P.

2014-01-01

Background: Little is known about the work-related well-being of applied behaviour analysis (ABA) therapists who work in school-based contexts and deliver ABA interventions to children with autism. Methods: A questionnaire on work-related stress (burnout), general distress, perceived supervisor support and coping was completed by 45 ABA therapists…

Generalized pseudopotential approach for electron-atom scattering.

NASA Technical Reports Server (NTRS)

Zarlingo, D. G.; Ishihara, T.; Poe, R. T.

1972-01-01

A generalized many-electron pseudopotential approach is presented for electron-neutral-atom scattering problems. A calculation based on this formulation is carried out for the singlet s-wave and p-wave electron-hydrogen phase shifts with excellent results. We compare the method with other approaches as well as discuss its applications for inelastic and rearrangement collision problems.

A Comparison between Linear IRT Observed-Score Equating and Levine Observed-Score Equating under the Generalized Kernel Equating Framework

ERIC Educational Resources Information Center

Chen, Haiwen

2012-01-01

In this article, linear item response theory (IRT) observed-score equating is compared under a generalized kernel equating framework with Levine observed-score equating for nonequivalent groups with anchor test design. Interestingly, these two equating methods are closely related despite being based on different methodologies. Specifically, when…

Addressing the Missing Instructional Data Problem: Using a Teacher Log to Document Tier 1 Instruction

ERIC Educational Resources Information Center

Kurz, Alexander; Elliott, Stephen N.; Roach, Andrew T.

2015-01-01

Response-to-intervention (RTI) systems posit that Tier 1 consists of high-quality general classroom instruction using evidence-based methods to address the needs of most students. However, data on the extent to which general education teachers provide such instruction are rarely collected. This missing instructional data problem may result in RTI…

Evaluation of Alternative Altitude Scaling Methods for Thermal Ice Protection System in NASA Icing Research Tunnel

NASA Technical Reports Server (NTRS)

Lee, Sam; Addy, Harold E. Jr.; Broeren, Andy P.; Orchard, David M.

2017-01-01

A test was conducted at NASA Icing Research Tunnel to evaluate altitude scaling methods for thermal ice protection system. Two new scaling methods based on Weber number were compared against a method based on Reynolds number. The results generally agreed with the previous set of tests conducted in NRCC Altitude Icing Wind Tunnel where the three methods of scaling were also tested and compared along with reference (altitude) icing conditions. In those tests, the Weber number-based scaling methods yielded results much closer to those observed at the reference icing conditions than the Reynolds number-based icing conditions. The test in the NASA IRT used a much larger, asymmetric airfoil with an ice protection system that more closely resembled designs used in commercial aircraft. Following the trends observed during the AIWT tests, the Weber number based scaling methods resulted in smaller runback ice than the Reynolds number based scaling, and the ice formed farther upstream. The results show that the new Weber number based scaling methods, particularly the Weber number with water loading scaling, continue to show promise for ice protection system development and evaluation in atmospheric icing tunnels.

48 CFR 215.404-71-1 - General.

Code of Federal Regulations, 2010 CFR

2010-10-01

... range provides values based on above normal or below normal conditions. In the price negotiation..., DEPARTMENT OF DEFENSE CONTRACTING METHODS AND CONTRACT TYPES CONTRACTING BY NEGOTIATION Contract Pricing 215... contracting officer assigns values to each profit factor; the value multiplied by the base results in the...

A characteristic based volume penalization method for general evolution problems applied to compressible viscous flows

NASA Astrophysics Data System (ADS)

Brown-Dymkoski, Eric; Kasimov, Nurlybek; Vasilyev, Oleg V.

2014-04-01

In order to introduce solid obstacles into flows, several different methods are used, including volume penalization methods which prescribe appropriate boundary conditions by applying local forcing to the constitutive equations. One well known method is Brinkman penalization, which models solid obstacles as porous media. While it has been adapted for compressible, incompressible, viscous and inviscid flows, it is limited in the types of boundary conditions that it imposes, as are most volume penalization methods. Typically, approaches are limited to Dirichlet boundary conditions. In this paper, Brinkman penalization is extended for generalized Neumann and Robin boundary conditions by introducing hyperbolic penalization terms with characteristics pointing inward on solid obstacles. This Characteristic-Based Volume Penalization (CBVP) method is a comprehensive approach to conditions on immersed boundaries, providing for homogeneous and inhomogeneous Dirichlet, Neumann, and Robin boundary conditions on hyperbolic and parabolic equations. This CBVP method can be used to impose boundary conditions for both integrated and non-integrated variables in a systematic manner that parallels the prescription of exact boundary conditions. Furthermore, the method does not depend upon a physical model, as with porous media approach for Brinkman penalization, and is therefore flexible for various physical regimes and general evolutionary equations. Here, the method is applied to scalar diffusion and to direct numerical simulation of compressible, viscous flows. With the Navier-Stokes equations, both homogeneous and inhomogeneous Neumann boundary conditions are demonstrated through external flow around an adiabatic and heated cylinder. Theoretical and numerical examination shows that the error from penalized Neumann and Robin boundary conditions can be rigorously controlled through an a priori penalization parameter η. The error on a transient boundary is found to converge as O(η), which is more favorable than the error convergence of the already established Dirichlet boundary condition.

Measurement of residual stresses by the moire method

NASA Astrophysics Data System (ADS)

Sciammarella, C. A.; Albertazzi, A., Jr.

Three different applications of the moire method to the determination of residual stresses and strains are presented. The three applications take advantage of the property of ratings to record the changes of the surface they are printed on. One of the applications deals with thermal residual stresses, another with contact residual stress and the third one is a generalization of the blind hole technique. This last application is based on a computer assisted moire technique and on the generalization of the quasi-heterodyne techniques of fringe pattern analysis.

Multi-loop Integrand Reduction with Computational Algebraic Geometry

NASA Astrophysics Data System (ADS)

Badger, Simon; Frellesvig, Hjalte; Zhang, Yang

2014-06-01

We discuss recent progress in multi-loop integrand reduction methods. Motivated by the possibility of an automated construction of multi-loop amplitudes via generalized unitarity cuts we describe a procedure to obtain a general parameterisation of any multi-loop integrand in a renormalizable gauge theory. The method relies on computational algebraic geometry techniques such as Gröbner bases and primary decomposition of ideals. We present some results for two and three loop amplitudes obtained with the help of the MACAULAY2 computer algebra system and the Mathematica package BASISDET.

Numerical realization of the variational method for generating self-trapped beams.

PubMed

Duque, Erick I; Lopez-Aguayo, Servando; Malomed, Boris A

2018-03-19

We introduce a numerical variational method based on the Rayleigh-Ritz optimization principle for predicting two-dimensional self-trapped beams in nonlinear media. This technique overcomes the limitation of the traditional variational approximation in performing analytical Lagrangian integration and differentiation. Approximate soliton solutions of a generalized nonlinear Schrödinger equation are obtained, demonstrating robustness of the beams of various types (fundamental, vortices, multipoles, azimuthons) in the course of their propagation. The algorithm offers possibilities to produce more sophisticated soliton profiles in general nonlinear models.

Self-learning Monte Carlo method and cumulative update in fermion systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Junwei; Shen, Huitao; Qi, Yang

2017-06-07

In this study, we develop the self-learning Monte Carlo (SLMC) method, a general-purpose numerical method recently introduced to simulate many-body systems, for studying interacting fermion systems. Our method uses a highly efficient update algorithm, which we design and dub “cumulative update”, to generate new candidate configurations in the Markov chain based on a self-learned bosonic effective model. From a general analysis and a numerical study of the double exchange model as an example, we find that the SLMC with cumulative update drastically reduces the computational cost of the simulation, while remaining statistically exact. Remarkably, its computational complexity is far lessmore » than the conventional algorithm with local updates.« less

Real-time simulation of biological soft tissues: a PGD approach.

PubMed

Niroomandi, S; González, D; Alfaro, I; Bordeu, F; Leygue, A; Cueto, E; Chinesta, F

2013-05-01

We introduce here a novel approach for the numerical simulation of nonlinear, hyperelastic soft tissues at kilohertz feedback rates necessary for haptic rendering. This approach is based upon the use of proper generalized decomposition techniques, a generalization of PODs. Proper generalized decomposition techniques can be considered as a means of a priori model order reduction and provides a physics-based meta-model without the need for prior computer experiments. The suggested strategy is thus composed of an offline phase, in which a general meta-model is computed, and an online evaluation phase in which the results are obtained at real time. Results are provided that show the potential of the proposed technique, together with some benchmark test that shows the accuracy of the method. Copyright © 2013 John Wiley & Sons, Ltd.

General optical discrete z transform: design and application.

PubMed

Ngo, Nam Quoc

2016-12-20

This paper presents a generalization of the discrete z transform algorithm. It is shown that the GOD-ZT algorithm is a generalization of several important conventional discrete transforms. Based on the GOD-ZT algorithm, a tunable general optical discrete z transform (GOD-ZT) processor is synthesized using the silica-based finite impulse response transversal filter. To demonstrate the effectiveness of the method, the design and simulation of a tunable optical discrete Fourier transform (ODFT) processor as a special case of the synthesized GOD-ZT processor is presented. It is also shown that the ODFT processor can function as a real-time optical spectrum analyzer. The tunable ODFT has an important potential application as a tunable optical demultiplexer at the receiver end of an optical orthogonal frequency-division multiplexing transmission system.

Multirate sampled-data yaw-damper and modal suppression system design

NASA Technical Reports Server (NTRS)

Berg, Martin C.; Mason, Gregory S.

1990-01-01

A multirate control law synthesized algorithm based on an infinite-time quadratic cost function, was developed along with a method for analyzing the robustness of multirate systems. A generalized multirate sampled-data control law structure (GMCLS) was introduced. A new infinite-time-based parameter optimization multirate sampled-data control law synthesis method and solution algorithm were developed. A singular-value-based method for determining gain and phase margins for multirate systems was also developed. The finite-time-based parameter optimization multirate sampled-data control law synthesis algorithm originally intended to be applied to the aircraft problem was instead demonstrated by application to a simpler problem involving the control of the tip position of a two-link robot arm. The GMCLS, the infinite-time-based parameter optimization multirate control law synthesis method and solution algorithm, and the singular-value based method for determining gain and phase margins were all demonstrated by application to the aircraft control problem originally proposed for this project.

Evaluation of a modified team based learning method for teaching general embryology to 1st year medical graduate students.

PubMed

Shankar, Nachiket; Roopa, R

2009-01-01

To encourage student participation in the learning process, the authors introduced a modified team based learning (TBL) method to cover two general embryology topics in the 1st year MBBS curriculum. The aim of this study was to evaluate students' perception of this method vis-à-vis the lecture method of teaching. A questionnaire was used to survey and evaluate the perceptions of 1st year MBBS students at the Department of Anatomy at our medical college in India. A total of eight classes were allotted to cover General Embryology. Six of these classes were conducted using the traditional didactic lecture method. Two topics were covered using the modified TBL method. Five teams of students were constituted, and each team was given handouts which contained basic factual material, four clinical case histories, and previous university exam questions from the topic. On the day of the session, these were discussed in the presence of the faculty facilitator. Students evaluated these sessions through a questionnaire. A majority of students felt that the modified TBL sessions were better at fulfilling learning objectives (46 students, 85%), enabled better understanding (43 students, 79%), were more interesting (43 students, 81%), ensured greater student participation (51 students, 94%) and involved greater effort on the part of students (53 students, 98%), as compared to traditional teaching methods. Most of the students (43 students, 79%) opined that more such sessions should be organized in the future. Responses from students show that the modified TBL classes could be utilized judiciously along with the traditional didactic lectures for teaching embryology.

Utility-preserving anonymization for health data publishing.

PubMed

Lee, Hyukki; Kim, Soohyung; Kim, Jong Wook; Chung, Yon Dohn

2017-07-11

Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information. A common practice for the privacy-preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k-anonymity. Among various anonymization techniques, generalization is the most commonly used in medical/health data processing. Generalization inevitably causes information loss, and thus, various methods have been proposed to reduce information loss. However, existing generalization-based data anonymization methods cannot avoid excessive information loss and preserve data utility. We propose a utility-preserving anonymization for privacy preserving data publishing (PPDP). To preserve data utility, the proposed method comprises three parts: (1) utility-preserving model, (2) counterfeit record insertion, (3) catalog of the counterfeit records. We also propose an anonymization algorithm using the proposed method. Our anonymization algorithm applies full-domain generalization algorithm. We evaluate our method in comparison with existence method on two aspects, information loss measured through various quality metrics and error rate of analysis result. With all different types of quality metrics, our proposed method show the lower information loss than the existing method. In the real-world EHRs analysis, analysis results show small portion of error between the anonymized data through the proposed method and original data. We propose a new utility-preserving anonymization method and an anonymization algorithm using the proposed method. Through experiments on various datasets, we show that the utility of EHRs anonymized by the proposed method is significantly better than those anonymized by previous approaches.

A General Accelerated Degradation Model Based on the Wiener Process.

PubMed

Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning

2016-12-06

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses.

A General Accelerated Degradation Model Based on the Wiener Process

PubMed Central

Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning

2016-01-01

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses. PMID:28774107

A Novel General Imaging Formation Algorithm for GNSS-Based Bistatic SAR.

PubMed

Zeng, Hong-Cheng; Wang, Peng-Bo; Chen, Jie; Liu, Wei; Ge, LinLin; Yang, Wei

2016-02-26

Global Navigation Satellite System (GNSS)-based bistatic Synthetic Aperture Radar (SAR) recently plays a more and more significant role in remote sensing applications for its low-cost and real-time global coverage capability. In this paper, a general imaging formation algorithm was proposed for accurately and efficiently focusing GNSS-based bistatic SAR data, which avoids the interpolation processing in traditional back projection algorithms (BPAs). A two-dimensional point target spectrum model was firstly presented, and the bulk range cell migration correction (RCMC) was consequently derived for reducing range cell migration (RCM) and coarse focusing. As the bulk RCMC seriously changes the range history of the radar signal, a modified and much more efficient hybrid correlation operation was introduced for compensating residual phase errors. Simulation results were presented based on a general geometric topology with non-parallel trajectories and unequal velocities for both transmitter and receiver platforms, showing a satisfactory performance by the proposed method.

A Novel General Imaging Formation Algorithm for GNSS-Based Bistatic SAR

PubMed Central

Zeng, Hong-Cheng; Wang, Peng-Bo; Chen, Jie; Liu, Wei; Ge, LinLin; Yang, Wei

2016-01-01

Global Navigation Satellite System (GNSS)-based bistatic Synthetic Aperture Radar (SAR) recently plays a more and more significant role in remote sensing applications for its low-cost and real-time global coverage capability. In this paper, a general imaging formation algorithm was proposed for accurately and efficiently focusing GNSS-based bistatic SAR data, which avoids the interpolation processing in traditional back projection algorithms (BPAs). A two-dimensional point target spectrum model was firstly presented, and the bulk range cell migration correction (RCMC) was consequently derived for reducing range cell migration (RCM) and coarse focusing. As the bulk RCMC seriously changes the range history of the radar signal, a modified and much more efficient hybrid correlation operation was introduced for compensating residual phase errors. Simulation results were presented based on a general geometric topology with non-parallel trajectories and unequal velocities for both transmitter and receiver platforms, showing a satisfactory performance by the proposed method. PMID:26927117

[Comparison of two algorithms for development of design space-overlapping method and probability-based method].

PubMed

Shao, Jing-Yuan; Qu, Hai-Bin; Gong, Xing-Chu

2018-05-01

In this work, two algorithms (overlapping method and the probability-based method) for design space calculation were compared by using the data collected from extraction process of Codonopsis Radix as an example. In the probability-based method, experimental error was simulated to calculate the probability of reaching the standard. The effects of several parameters on the calculated design space were studied, including simulation number, step length, and the acceptable probability threshold. For the extraction process of Codonopsis Radix, 10 000 times of simulation and 0.02 for the calculation step length can lead to a satisfactory design space. In general, the overlapping method is easy to understand, and can be realized by several kinds of commercial software without coding programs, but the reliability of the process evaluation indexes when operating in the design space is not indicated. Probability-based method is complex in calculation, but can provide the reliability to ensure that the process indexes can reach the standard within the acceptable probability threshold. In addition, there is no probability mutation in the edge of design space by probability-based method. Therefore, probability-based method is recommended for design space calculation. Copyright© by the Chinese Pharmaceutical Association.

Numerically pricing American options under the generalized mixed fractional Brownian motion model

NASA Astrophysics Data System (ADS)

Chen, Wenting; Yan, Bowen; Lian, Guanghua; Zhang, Ying

2016-06-01

In this paper, we introduce a robust numerical method, based on the upwind scheme, for the pricing of American puts under the generalized mixed fractional Brownian motion (GMFBM) model. By using portfolio analysis and applying the Wick-Itô formula, a partial differential equation (PDE) governing the prices of vanilla options under the GMFBM is successfully derived for the first time. Based on this, we formulate the pricing of American puts under the current model as a linear complementarity problem (LCP). Unlike the classical Black-Scholes (B-S) model or the generalized B-S model discussed in Cen and Le (2011), the newly obtained LCP under the GMFBM model is difficult to be solved accurately because of the numerical instability which results from the degeneration of the governing PDE as time approaches zero. To overcome this difficulty, a numerical approach based on the upwind scheme is adopted. It is shown that the coefficient matrix of the current method is an M-matrix, which ensures its stability in the maximum-norm sense. Remarkably, we have managed to provide a sharp theoretic error estimate for the current method, which is further verified numerically. The results of various numerical experiments also suggest that this new approach is quite accurate, and can be easily extended to price other types of financial derivatives with an American-style exercise feature under the GMFBM model.

A method for classification of transient events in EEG recordings: application to epilepsy diagnosis.

PubMed

Tzallas, A T; Karvelis, P S; Katsis, C D; Fotiadis, D I; Giannopoulos, S; Konitsiotis, S

2006-01-01

The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Element analysis: a wavelet-based method for analysing time-localized events in noisy time series.

PubMed

Lilly, Jonathan M

2017-04-01

A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized 'events'. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate. Finally, an expression for an event's 'region of influence' within the wavelet transform permits the formation of a criterion for rejecting spurious maxima due to numerical artefacts or other unsuitable events. Signals can then be reconstructed based on a small number of isolated points on the time/scale plane. This method, termed element analysis , is applied to the identification of long-lived eddy structures in ocean currents as observed by along-track measurements of sea surface elevation from satellite altimetry.

Risk Assessment and Hierarchical Risk Management of Enterprises in Chemical Industrial Parks Based on Catastrophe Theory

PubMed Central

Chen, Yu; Song, Guobao; Yang, Fenglin; Zhang, Shushen; Zhang, Yun; Liu, Zhenyu

2012-01-01

According to risk systems theory and the characteristics of the chemical industry, an index system was established for risk assessment of enterprises in chemical industrial parks (CIPs) based on the inherent risk of the source, effectiveness of the prevention and control mechanism, and vulnerability of the receptor. A comprehensive risk assessment method based on catastrophe theory was then proposed and used to analyze the risk levels of ten major chemical enterprises in the Songmu Island CIP, China. According to the principle of equal distribution function, the chemical enterprise risk level was divided into the following five levels: 1.0 (very safe), 0.8 (safe), 0.6 (generally recognized as safe, GRAS), 0.4 (unsafe), 0.2 (very unsafe). The results revealed five enterprises (50%) with an unsafe risk level, and another five enterprises (50%) at the generally recognized as safe risk level. This method solves the multi-objective evaluation and decision-making problem. Additionally, this method involves simple calculations and provides an effective technique for risk assessment and hierarchical risk management of enterprises in CIPs. PMID:23208298

Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gottwald, Fabian; Karsten, Sven; Ivanov, Sergei D., E-mail: sergei.ivanov@uni-rostock.de

2015-06-28

Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into a few important degrees of freedom which are treated most accurately and others which constitute a thermal bath. Particular attention in this respect attracts the linear generalized Langevin equation, which can be rigorously derived by means of a linear projection technique. Within this framework, a complicated interaction with the bath can be reduced to a single memory kernel. This memory kernel in turn is parametrized for a particular system studied,more » usually by means of time-domain methods based on explicit molecular dynamics data. Here, we discuss that this task is more naturally achieved in frequency domain and develop a Fourier-based parametrization method that outperforms its time-domain analogues. Very surprisingly, the widely used rigid bond method turns out to be inappropriate in general. Importantly, we show that the rigid bond approach leads to a systematic overestimation of relaxation times, unless the system under study consists of a harmonic bath bi-linearly coupled to the relevant degrees of freedom.« less

Clustering "N" Objects into "K" Groups under Optimal Scaling of Variables.

ERIC Educational Resources Information Center

van Buuren, Stef; Heiser, Willem J.

1989-01-01

A method based on homogeneity analysis (multiple correspondence analysis or multiple scaling) is proposed to reduce many categorical variables to one variable with "k" categories. The method is a generalization of the sum of squared distances cluster analysis problem to the case of mixed measurement level variables. (SLD)

A Survey of Model Evaluation Approaches with a Tutorial on Hierarchical Bayesian Methods

ERIC Educational Resources Information Center

Shiffrin, Richard M.; Lee, Michael D.; Kim, Woojae; Wagenmakers, Eric-Jan

2008-01-01

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues…

Pediatricians' beliefs and prescribing patterns of adolescent contraception: a provider survey.

PubMed

Swanson, K J; Gossett, D R; Fournier, M

2013-12-01

Teen pregnancy and sexually transmitted infection (STI) rates continue to be significant public health problems in the United States. While general pediatricians are in a unique position to improve these issues by addressing contraception with their adolescent patients, there are no data describing their current prescribing patterns. This study sought to elucidate the beliefs and prescribing patterns of general pediatricians and pediatrics residents and to distinguish whether these were affected by practice setting, level of training, or gender. General pediatricians and pediatrics residents affiliated with Lurie Children's Hospital in Chicago, IL, were asked to complete a survey regarding adolescent contraception. Questions were related to obtaining information about contraception, contraceptive counseling, knowledge of contraceptive methods, prescribing patterns of contraceptives, and concerns about individual contraceptive methods. 120 physicians of an eligible 411 physicians participated in this study (29%). 79% of participants had prescribed at least 1 contraceptive method. The most commonly prescribed method was oral contraceptive pills at 72%. We noted few differences in prescribing patterns based on above criteria. Numerous misconceptions existed among participants, including a high rate of concern about infertility with IUD use (29% among physicians who prescribed at least 1 method of contraception). General pediatricians can improve their rates of prescribing contraception to adolescents, and could utilize more of the approved methods. One way to do so may be to implement educational interventions among general pediatricians. Copyright © 2013 North American Society for Pediatric and Adolescent Gynecology. Published by Elsevier Inc. All rights reserved.

Photochemically Initiated Single Polymer Immobilization

PubMed Central

2015-01-01

This Concept article surveys methods for attaching single polymer molecules on solid substrates. A general approach to single polymer immobilization based on the photochemistry of perfluorophenylazides is elaborated. PMID:17444538

In-service teachers' perceptions of project-based learning.

PubMed

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.

Review of Hull Structural Monitoring Systems for Navy Ships

DTIC Science & Technology

2013-05-01

generally based on the same basic form of S-N curve, different correction methods are used by the various classification societies. ii. Methods for...Likewise there are a number of different methods employed for temperature compensation and these vary depending on the type of gauge, although typically...Analysis, Inc.[30] Figure 8. Examples of different methods of temperature compensation of fibre-optic strain sensors. It is noted in NATO

The role of feedback in improving the effectiveness of workplace based assessments: a systematic review.

PubMed

Saedon, Habiba; Salleh, Shizalia; Balakrishnan, Arun; Imray, Christopher H E; Saedon, Mahmud

2012-05-02

With recent emphasis placed on workplace based assessment (WBA) as a method of formative performance assessment, there is limited evidence in the current literature regarding the role of feedback in improving the effectiveness of WBA. The aim of this systematic review was to elucidate the impact of feedback on the effectiveness of WBA in postgraduate medical training. Searches were conducted using the following bibliographic databases to identify original published studies related to WBA and the role of feedback: Medline (1950-December 2010), Embase (1980-December 2010) and Journals@Ovid (English language only, 1996-December 2010). Studies which attempted to evaluate the role of feedback in WBA involving postgraduate doctors were included. 15 identified studies met the inclusion criteria and minimum quality threshold. They were heterogeneous in methodological design. 7 studies focused on multi source feedback, 3 studies were based on mini-clinical evaluation exercise, 2 looked at procedural based assessment, one study looked at workplace based assessments in general and 2 studies looked at a combination of 3 to 6 workplace based assessments. 7 studies originated from the United Kingdom. Others were from Canada, the United States and New Zealand. Study populations were doctors in various grades of training from a wide range of specialties including general practice, general medicine, general surgery, dermatology, paediatrics and anaesthetics. All studies were prospective in design, and non-comparative descriptive or observational studies using a variety of methods including questionnaires, one to one interviews and focus groups. The evidence base contains few high quality conclusive studies and more studies are required to provide further evidence for the effect of feedback from workplace based assessment on subsequent performance. There is, however, good evidence that if well implemented, feedback from workplace based assessments, particularly multisource feedback, leads to a perceived positive effect on practice.

A fuzzy case based reasoning tool for model based approach to rocket engine health monitoring

NASA Technical Reports Server (NTRS)

Krovvidy, Srinivas; Nolan, Adam; Hu, Yong-Lin; Wee, William G.

1992-01-01

In this system we develop a fuzzy case based reasoner that can build a case representation for several past anomalies detected, and we develop case retrieval methods that can be used to index a relevant case when a new problem (case) is presented using fuzzy sets. The choice of fuzzy sets is justified by the uncertain data. The new problem can be solved using knowledge of the model along with the old cases. This system can then be used to generalize the knowledge from previous cases and use this generalization to refine the existing model definition. This in turn can help to detect failures using the model based algorithms.

The scenario-based generalization of radiation therapy margins.

PubMed

Fredriksson, Albin; Bokrantz, Rasmus

2016-03-07

We give a scenario-based treatment plan optimization formulation that is equivalent to planning with geometric margins if the scenario doses are calculated using the static dose cloud approximation. If the scenario doses are instead calculated more accurately, then our formulation provides a novel robust planning method that overcomes many of the difficulties associated with previous scenario-based robust planning methods. In particular, our method protects only against uncertainties that can occur in practice, it gives a sharp dose fall-off outside high dose regions, and it avoids underdosage of the target in 'easy' scenarios. The method shares the benefits of the previous scenario-based robust planning methods over geometric margins for applications where the static dose cloud approximation is inaccurate, such as irradiation with few fields and irradiation with ion beams. These properties are demonstrated on a suite of phantom cases planned for treatment with scanned proton beams subject to systematic setup uncertainty.

Alternative methods to evaluate trial level surrogacy.

PubMed

Abrahantes, Josè Cortiñas; Shkedy, Ziv; Molenberghs, Geert

2008-01-01

The evaluation and validation of surrogate endpoints have been extensively studied in the last decade. Prentice [1] and Freedman, Graubard and Schatzkin [2] laid the foundations for the evaluation of surrogate endpoints in randomized clinical trials. Later, Buyse et al. [5] proposed a meta-analytic methodology, producing different methods for different settings, which was further studied by Alonso and Molenberghs [9], in their unifying approach based on information theory. In this article, we focus our attention on the trial-level surrogacy and propose alternative procedures to evaluate such surrogacy measure, which do not pre-specify the type of association. A promising correction based on cross-validation is investigated. As well as the construction of confidence intervals for this measure. In order to avoid making assumption about the type of relationship between the treatment effects and its distribution, a collection of alternative methods, based on regression trees, bagging, random forests, and support vector machines, combined with bootstrap-based confidence interval and, should one wish, in conjunction with a cross-validation based correction, will be proposed and applied. We apply the various strategies to data from three clinical studies: in opthalmology, in advanced colorectal cancer, and in schizophrenia. The results obtained for the three case studies are compared; they indicate that using random forest or bagging models produces larger estimated values for the surrogacy measure, which are in general stabler and the confidence interval narrower than linear regression and support vector regression. For the advanced colorectal cancer studies, we even found the trial-level surrogacy is considerably different from what has been reported. In general the alternative methods are more computationally demanding, and specially the calculation of the confidence intervals, require more computational time that the delta-method counterpart. First, more flexible modeling techniques can be used, allowing for other type of association. Second, when no cross-validation-based correction is applied, overly optimistic trial-level surrogacy estimates will be found, thus cross-validation is highly recommendable. Third, the use of the delta method to calculate confidence intervals is not recommendable since it makes assumptions valid only in very large samples. It may also produce range-violating limits. We therefore recommend alternatives: bootstrap methods in general. Also, the information-theoretic approach produces comparable results with the bagging and random forest approaches, when cross-validation correction is applied. It is also important to observe that, even for the case in which the linear model might be a good option too, bagging methods perform well too, and their confidence intervals were more narrow.

Performance of Underprepared Students in Traditional versus Animation-Based Flipped-Classroom Settings

ERIC Educational Resources Information Center

Gregorius, R. Ma.

2017-01-01

Student performance in a flipped classroom with an animation-based content knowledge development system for the bottom third of the incoming first year college students was compared to that in a traditional lecture-based teaching method. 52% of these students withdrew from the traditionally taught General Chemistry course, compared to 22% in a…

Rational-operator-based depth-from-defocus approach to scene reconstruction.

PubMed

Li, Ang; Staunton, Richard; Tjahjadi, Tardi

2013-09-01

This paper presents a rational-operator-based approach to depth from defocus (DfD) for the reconstruction of three-dimensional scenes from two-dimensional images, which enables fast DfD computation that is independent of scene textures. Two variants of the approach, one using the Gaussian rational operators (ROs) that are based on the Gaussian point spread function (PSF) and the second based on the generalized Gaussian PSF, are considered. A novel DfD correction method is also presented to further improve the performance of the approach. Experimental results are considered for real scenes and show that both approaches outperform existing RO-based methods.

An efficient direct method for image registration of flat objects

NASA Astrophysics Data System (ADS)

Nikolaev, Dmitry; Tihonkih, Dmitrii; Makovetskii, Artyom; Voronin, Sergei

2017-09-01

Image alignment of rigid surfaces is a rapidly developing area of research and has many practical applications. Alignment methods can be roughly divided into two types: feature-based methods and direct methods. Known SURF and SIFT algorithms are examples of the feature-based methods. Direct methods refer to those that exploit the pixel intensities without resorting to image features and image-based deformations are general direct method to align images of deformable objects in 3D space. Nevertheless, it is not good for the registration of images of 3D rigid objects since the underlying structure cannot be directly evaluated. In the article, we propose a model that is suitable for image alignment of rigid flat objects under various illumination models. The brightness consistency assumptions used for reconstruction of optimal geometrical transformation. Computer simulation results are provided to illustrate the performance of the proposed algorithm for computing of an accordance between pixels of two images.

Conditional Monte Carlo randomization tests for regression models.

PubMed

Parhat, Parwen; Rosenberger, William F; Diao, Guoqing

2014-08-15

We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.

Controllers, observers, and applications thereof

NASA Technical Reports Server (NTRS)

Gao, Zhiqiang (Inventor); Zhou, Wankun (Inventor); Miklosovic, Robert (Inventor); Radke, Aaron (Inventor); Zheng, Qing (Inventor)

2011-01-01

Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed.

Exploring super-Gaussianity toward robust information-theoretical time delay estimation.

PubMed

Petsatodis, Theodoros; Talantzis, Fotios; Boukis, Christos; Tan, Zheng-Hua; Prasad, Ramjee

2013-03-01

Time delay estimation (TDE) is a fundamental component of speaker localization and tracking algorithms. Most of the existing systems are based on the generalized cross-correlation method assuming gaussianity of the source. It has been shown that the distribution of speech, captured with far-field microphones, is highly varying, depending on the noise and reverberation conditions. Thus the performance of TDE is expected to fluctuate depending on the underlying assumption for the speech distribution, being also subject to multi-path reflections and competitive background noise. This paper investigates the effect upon TDE when modeling the source signal with different speech-based distributions. An information theoretical TDE method indirectly encapsulating higher order statistics (HOS) formed the basis of this work. The underlying assumption of Gaussian distributed source has been replaced by that of generalized Gaussian distribution that allows evaluating the problem under a larger set of speech-shaped distributions, ranging from Gaussian to Laplacian and Gamma. Closed forms of the univariate and multivariate entropy expressions of the generalized Gaussian distribution are derived to evaluate the TDE. The results indicate that TDE based on the specific criterion is independent of the underlying assumption for the distribution of the source, for the same covariance matrix.

Generalized Bootstrap Method for Assessment of Uncertainty in Semivariogram Inference

USGS Publications Warehouse

Olea, R.A.; Pardo-Iguzquiza, E.

2011-01-01

The semivariogram and its related function, the covariance, play a central role in classical geostatistics for modeling the average continuity of spatially correlated attributes. Whereas all methods are formulated in terms of the true semivariogram, in practice what can be used are estimated semivariograms and models based on samples. A generalized form of the bootstrap method to properly model spatially correlated data is used to advance knowledge about the reliability of empirical semivariograms and semivariogram models based on a single sample. Among several methods available to generate spatially correlated resamples, we selected a method based on the LU decomposition and used several examples to illustrate the approach. The first one is a synthetic, isotropic, exhaustive sample following a normal distribution, the second example is also a synthetic but following a non-Gaussian random field, and a third empirical sample consists of actual raingauge measurements. Results show wider confidence intervals than those found previously by others with inadequate application of the bootstrap. Also, even for the Gaussian example, distributions for estimated semivariogram values and model parameters are positively skewed. In this sense, bootstrap percentile confidence intervals, which are not centered around the empirical semivariogram and do not require distributional assumptions for its construction, provide an achieved coverage similar to the nominal coverage. The latter cannot be achieved by symmetrical confidence intervals based on the standard error, regardless if the standard error is estimated from a parametric equation or from bootstrap. ?? 2010 International Association for Mathematical Geosciences.

Finger Vein Recognition Based on a Personalized Best Bit Map

PubMed Central

Yang, Gongping; Xi, Xiaoming; Yin, Yilong

2012-01-01

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition. PMID:22438735

Finger vein recognition based on a personalized best bit map.

PubMed

Yang, Gongping; Xi, Xiaoming; Yin, Yilong

2012-01-01

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition.

Detection of Parent-of-Origin Effects Using General Pedigree Data

PubMed Central

Zhou, Ji-Yuan; Ding, Jie; Fung, Wing K.; Lin, Shili

2010-01-01

Genomic imprinting is an important epigenetic factor in complex traits study, which has generally been examined by testing for parent-of-origin effects of alleles. For a diallelic marker locus, the parental-asymmetry test (PAT) based on case-parents trios and its extensions to incomplete nuclear families (1-PAT and C-PAT) are simple and powerful for detecting parent-of-origin effects. However, these methods are suitable only for nuclear families and thus are not amenable to general pedigree data. Use of data from extended pedigrees, if available, may lead to more powerful methods than randomly selecting one two-generation nuclear family from each pedigree. In this study, we extend PAT to accommodate general pedigree data by proposing the pedigree PAT (PPAT) statistic, which uses all informative family trios from pedigrees. To fully utilize pedigrees with some missing genotypes, we further develop the Monte Carlo (MC) PPAT (MCPPAT) statistic based on MC sampling and estimation. Extensive simulations were carried out to evaluate the performance of the proposed methods. Under the assumption that the pedigrees and their associated affection patterns are randomly drawn from a population of pedigrees with at least one affected offspring, we demonstrated that MCPPAT is a valid test for parent-of-origin effects in the presence of association. Further, MCPPAT is much more powerful compared to PAT for trios or even PPAT for all informative family trios from the same pedigrees if there is missing data. Application of the proposed methods to a rheumatoid arthritis dataset further demonstrates the advantage of MCPPAT. PMID:19676055

Micromechanics-Based Progressive Failure Analysis of Composite Laminates Using Different Constituent Failure Theories

NASA Technical Reports Server (NTRS)

Moncada, Albert M.; Chattopadhyay, Aditi; Bednarcyk, Brett A.; Arnold, Steven M.

2008-01-01

Predicting failure in a composite can be done with ply level mechanisms and/or micro level mechanisms. This paper uses the Generalized Method of Cells and High-Fidelity Generalized Method of Cells micromechanics theories, coupled with classical lamination theory, as implemented within NASA's Micromechanics Analysis Code with Generalized Method of Cells. The code is able to implement different failure theories on the level of both the fiber and the matrix constituents within a laminate. A comparison is made among maximum stress, maximum strain, Tsai-Hill, and Tsai-Wu failure theories. To verify the failure theories the Worldwide Failure Exercise (WWFE) experiments have been used. The WWFE is a comprehensive study that covers a wide range of polymer matrix composite laminates. The numerical results indicate good correlation with the experimental results for most of the composite layups, but also point to the need for more accurate resin damage progression models.

Identifying multiple influential spreaders based on generalized closeness centrality

NASA Astrophysics Data System (ADS)

Liu, Huan-Li; Ma, Chuang; Xiang, Bing-Bing; Tang, Ming; Zhang, Hai-Feng

2018-02-01

To maximize the spreading influence of multiple spreaders in complex networks, one important fact cannot be ignored: the multiple spreaders should be dispersively distributed in networks, which can effectively reduce the redundance of information spreading. For this purpose, we define a generalized closeness centrality (GCC) index by generalizing the closeness centrality index to a set of nodes. The problem converts to how to identify multiple spreaders such that an objective function has the minimal value. By comparing with the K-means clustering algorithm, we find that the optimization problem is very similar to the problem of minimizing the objective function in the K-means method. Therefore, how to find multiple nodes with the highest GCC value can be approximately solved by the K-means method. Two typical transmission dynamics-epidemic spreading process and rumor spreading process are implemented in real networks to verify the good performance of our proposed method.

[Characteristics of perioperative period in Xenon-based combined general anaesthesia in neurosurgery].

PubMed

Viatkin, A A; Petrosian, L G; Mizikov, V M; Vasil'ev, S A

2013-01-01

Neuroprotection could be the aim to use Xenon for general anesthesia. However the experience of Xenon anesthesia in neurosurgery is quite limited. The appraisal of Xenon based anesthesia was accomplished in 12 patients during various brain surgery. Xe in concentration 65% was used to maintenance of anesthesia, other medication was avoided. As a resuIt there were 8 cases of arterial hypertension and 2 cases of superficial hypnotic state. Excitation (n = 3), hyperdynamic reaction (n = 8), PONV (n = 8) were detected in early postoperative period. An analysis of this study suggests a conclusion that studied method of Xenon-based anesthesia is inexpedient for neurosurgery.

Bayesian inference based on dual generalized order statistics from the exponentiated Weibull model

NASA Astrophysics Data System (ADS)

Al Sobhi, Mashail M.

2015-02-01

Bayesian estimation for the two parameters and the reliability function of the exponentiated Weibull model are obtained based on dual generalized order statistics (DGOS). Also, Bayesian prediction bounds for future DGOS from exponentiated Weibull model are obtained. The symmetric and asymmetric loss functions are considered for Bayesian computations. The Markov chain Monte Carlo (MCMC) methods are used for computing the Bayes estimates and prediction bounds. The results have been specialized to the lower record values. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

Man-systems evaluation of moving base vehicle simulation motion cues. [human acceleration perception involving visual feedback

NASA Technical Reports Server (NTRS)

Kirkpatrick, M.; Brye, R. G.

1974-01-01

A motion cue investigation program is reported that deals with human factor aspects of high fidelity vehicle simulation. General data on non-visual motion thresholds and specific threshold values are established for use as washout parameters in vehicle simulation. A general purpose similator is used to test the contradictory cue hypothesis that acceleration sensitivity is reduced during a vehicle control task involving visual feedback. The simulator provides varying acceleration levels. The method of forced choice is based on the theory of signal detect ability.

Should There Be Separate Parent and Teacher-Based Categories of ODD? Evidence from a General Population

ERIC Educational Resources Information Center

Munkvold, Linda; Lundervold, Astri; Lie, Stein Atle; Manger, Terje

2009-01-01

Objective: To examine the occurrence of oppositional defiant disorder (ODD) symptoms in a general population of boys and girls, as reported by parents and teachers, and to investigate differences in prevalence estimates, depending on how parents' and teachers' ratings were combined. Method: Data were collected from 7007 children (aged 7-9) who…

Summary of noise reduction characteristics of typical general aviation materials

NASA Technical Reports Server (NTRS)

Roskam, J.; Grosveld, F.; Van Aken, J.

1979-01-01

The paper presents the results of a large number of systematic tests to determine noise reduction characteristics of general aviation materials. Effects of material type (metallic and composite), thickness, panel stiffening, vibration damping materials, sound absorption materials and pressurization on noise reduction are included. Several promising methods for reducing cabin interior noise in light airplanes are discussed based on the results.

Cartographic generalization of urban street networks based on gravitational field theory

NASA Astrophysics Data System (ADS)

Liu, Gang; Li, Yongshu; Li, Zheng; Guo, Jiawei

2014-05-01

The automatic generalization of urban street networks is a constant and important aspect of geographical information science. Previous studies show that the dual graph for street-street relationships more accurately reflects the overall morphological properties and importance of streets than do other methods. In this study, we construct a dual graph to represent street-street relationship and propose an approach to generalize street networks based on gravitational field theory. We retain the global structural properties and topological connectivity of an original street network and borrow from gravitational field theory to define the gravitational force between nodes. The concept of multi-order neighbors is introduced and the gravitational force is taken as the measure of the importance contribution between nodes. The importance of a node is defined as the result of the interaction between a given node and its multi-order neighbors. Degree distribution is used to evaluate the level of maintaining the global structure and topological characteristics of a street network and to illustrate the efficiency of the suggested method. Experimental results indicate that the proposed approach can be used in generalizing street networks and retaining their density characteristics, connectivity and global structure.

Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on binary outcomes

PubMed Central

2018-01-01

Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures. The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (eg, dose or quantity of medication, income, or years of education). We used Monte Carlo simulations to examine the performance of different methods of using the GPS to estimate the effect of continuous exposures on binary outcomes. We examined covariate adjustment using the GPS and weighting using weights based on the inverse of the GPS. We examined both the use of ordinary least squares to estimate the propensity function and the use of the covariate balancing propensity score algorithm. The use of methods based on the GPS was compared with the use of G‐computation. All methods resulted in essentially unbiased estimation of the population dose‐response function. However, GPS‐based weighting tended to result in estimates that displayed greater variability and had higher mean squared error when the magnitude of confounding was strong. Of the methods based on the GPS, covariate adjustment using the GPS tended to result in estimates with lower variability and mean squared error when the magnitude of confounding was strong. We illustrate the application of these methods by estimating the effect of average neighborhood income on the probability of death within 1 year of hospitalization for an acute myocardial infarction. PMID:29508424

Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on binary outcomes.

PubMed

Austin, Peter C

2018-05-20

Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures. The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (eg, dose or quantity of medication, income, or years of education). We used Monte Carlo simulations to examine the performance of different methods of using the GPS to estimate the effect of continuous exposures on binary outcomes. We examined covariate adjustment using the GPS and weighting using weights based on the inverse of the GPS. We examined both the use of ordinary least squares to estimate the propensity function and the use of the covariate balancing propensity score algorithm. The use of methods based on the GPS was compared with the use of G-computation. All methods resulted in essentially unbiased estimation of the population dose-response function. However, GPS-based weighting tended to result in estimates that displayed greater variability and had higher mean squared error when the magnitude of confounding was strong. Of the methods based on the GPS, covariate adjustment using the GPS tended to result in estimates with lower variability and mean squared error when the magnitude of confounding was strong. We illustrate the application of these methods by estimating the effect of average neighborhood income on the probability of death within 1 year of hospitalization for an acute myocardial infarction. © 2018 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

PubMed Central

Lee, Seunggeun; Teslovich, Tanya M.; Boehnke, Michael; Lin, Xihong

2013-01-01

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels. PMID:23768515

A simple method for assessing occupational exposure via the one-way random effects model.

PubMed

Krishnamoorthy, K; Mathew, Thomas; Peng, Jie

2016-11-01

A one-way random effects model is postulated for the log-transformed shift-long personal exposure measurements, where the random effect in the model represents an effect due to the worker. Simple closed-form confidence intervals are proposed for the relevant parameters of interest using the method of variance estimates recovery (MOVER). The performance of the confidence bounds is evaluated and compared with those based on the generalized confidence interval approach. Comparison studies indicate that the proposed MOVER confidence bounds are better than the generalized confidence bounds for the overall mean exposure and an upper percentile of the exposure distribution. The proposed methods are illustrated using a few examples involving industrial hygiene data.

QMR: A Quasi-Minimal Residual method for non-Hermitian linear systems

NASA Technical Reports Server (NTRS)

Freund, Roland W.; Nachtigal, Noel M.

1990-01-01

The biconjugate gradient (BCG) method is the natural generalization of the classical conjugate gradient algorithm for Hermitian positive definite matrices to general non-Hermitian linear systems. Unfortunately, the original BCG algorithm is susceptible to possible breakdowns and numerical instabilities. A novel BCG like approach is presented called the quasi-minimal residual (QMR) method, which overcomes the problems of BCG. An implementation of QMR based on a look-ahead version of the nonsymmetric Lanczos algorithm is proposed. It is shown how BCG iterates can be recovered stably from the QMR process. Some further properties of the QMR approach are given and an error bound is presented. Finally, numerical experiments are reported.

Automatic Extraction of Planetary Image Features

NASA Technical Reports Server (NTRS)

Troglio, G.; LeMoigne, J.; Moser, G.; Serpico, S. B.; Benediktsson, J. A.

2009-01-01

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration.

Defect inspection and printability study for 14 nm node and beyond photomask

NASA Astrophysics Data System (ADS)

Seki, Kazunori; Yonetani, Masashi; Badger, Karen; Dechene, Dan J.; Akima, Shinji

2016-10-01

Two different mask inspection techniques are developed and compared for 14 nm node and beyond photomasks, High resolution and Litho-based inspection. High resolution inspection is the general inspection method in which a 19x nm wavelength laser is used with the High NA inspection optics. Litho-based inspection is a new inspection technology. This inspection uses the wafer lithography information, and as such, this method has automatic defect classification capability which is based on wafer printability. Both High resolution and Litho-based inspection methods are compared using 14 nm and 7 nm node programmed defect and production design masks. The defect sensitivity and mask inspectability is compared, in addition to comparing the defect classification and throughput. Additionally, the Cost / Infrastructure comparison is analyzed and the impact of each inspection method is discussed.

24 CFR 35.1305 - Definitions and other general requirements.

Code of Federal Regulations, 2011 CFR

2011-04-01

... Housing and Urban Development LEAD-BASED PAINT POISONING PREVENTION IN CERTAIN RESIDENTIAL STRUCTURES Methods and Standards for Lead-Paint Hazard Evaluation and Hazard Reduction Activities § 35.1305...

24 CFR 35.1305 - Definitions and other general requirements.

Code of Federal Regulations, 2012 CFR

2012-04-01

... Housing and Urban Development LEAD-BASED PAINT POISONING PREVENTION IN CERTAIN RESIDENTIAL STRUCTURES Methods and Standards for Lead-Paint Hazard Evaluation and Hazard Reduction Activities § 35.1305...

24 CFR 35.1305 - Definitions and other general requirements.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Housing and Urban Development LEAD-BASED PAINT POISONING PREVENTION IN CERTAIN RESIDENTIAL STRUCTURES Methods and Standards for Lead-Paint Hazard Evaluation and Hazard Reduction Activities § 35.1305...

24 CFR 35.1305 - Definitions and other general requirements.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Housing and Urban Development LEAD-BASED PAINT POISONING PREVENTION IN CERTAIN RESIDENTIAL STRUCTURES Methods and Standards for Lead-Paint Hazard Evaluation and Hazard Reduction Activities § 35.1305...

24 CFR 35.1305 - Definitions and other general requirements.

Code of Federal Regulations, 2013 CFR

2013-04-01

... Housing and Urban Development LEAD-BASED PAINT POISONING PREVENTION IN CERTAIN RESIDENTIAL STRUCTURES Methods and Standards for Lead-Paint Hazard Evaluation and Hazard Reduction Activities § 35.1305...

Quick multitemporal approach to get cloudless improved multispectral imagery for large geographical areas

NASA Astrophysics Data System (ADS)

Colaninno, Nicola; Marambio Castillo, Alejandro; Roca Cladera, Josep

2017-10-01

The demand for remotely sensed data is growing increasingly, due to the possibility of managing information about huge geographic areas, in digital format, at different time periods, and suitable for analysis in GIS platforms. However, primary satellite information is not such immediate as desirable. Beside geometric and atmospheric limitations, clouds, cloud shadows, and haze generally contaminate optical images. In terms of land cover, such a contamination is intended as missing information and should be replaced. Generally, image reconstruction is classified according to three main approaches, i.e. in-painting-based, multispectral-based, and multitemporal-based methods. This work relies on a multitemporal-based approach to retrieve uncontaminated pixels for an image scene. We explore an automatic method for quickly getting daytime cloudless and shadow-free image at moderate spatial resolution for large geographical areas. The process expects two main steps: a multitemporal effect adjustment to avoid significant seasonal variations, and a data reconstruction phase, based on automatic selection of uncontaminated pixels from an image stack. The result is a composite image based on middle values of the stack, over a year. The assumption is that, for specific purposes, land cover changes at a coarse scale are not significant over relatively short time periods. Because it is largely recognized that satellite imagery along tropical areas are generally strongly affected by clouds, the methodology is tested for the case study of the Dominican Republic at the year 2015; while Landsat 8 imagery are employed to test the approach.

Regional frequency analysis of extreme rainfalls using partial L moments method

NASA Astrophysics Data System (ADS)

Zakaria, Zahrahtul Amani; Shabri, Ani

2013-07-01

An approach based on regional frequency analysis using L moments and LH moments are revisited in this study. Subsequently, an alternative regional frequency analysis using the partial L moments (PL moments) method is employed, and a new relationship for homogeneity analysis is developed. The results were then compared with those obtained using the method of L moments and LH moments of order two. The Selangor catchment, consisting of 37 sites and located on the west coast of Peninsular Malaysia, is chosen as a case study. PL moments for the generalized extreme value (GEV), generalized logistic (GLO), and generalized Pareto distributions were derived and used to develop the regional frequency analysis procedure. PL moment ratio diagram and Z test were employed in determining the best-fit distribution. Comparison between the three approaches showed that GLO and GEV distributions were identified as the suitable distributions for representing the statistical properties of extreme rainfall in Selangor. Monte Carlo simulation used for performance evaluation shows that the method of PL moments would outperform L and LH moments methods for estimation of large return period events.

An Effective Measured Data Preprocessing Method in Electrical Impedance Tomography

PubMed Central

Yu, Chenglong; Yue, Shihong; Wang, Jianpei; Wang, Huaxiang

2014-01-01

As an advanced process detection technology, electrical impedance tomography (EIT) has widely been paid attention to and studied in the industrial fields. But the EIT techniques are greatly limited to the low spatial resolutions. This problem may result from the incorrect preprocessing of measuring data and lack of general criterion to evaluate different preprocessing processes. In this paper, an EIT data preprocessing method is proposed by all rooting measured data and evaluated by two constructed indexes based on all rooted EIT measured data. By finding the optimums of the two indexes, the proposed method can be applied to improve the EIT imaging spatial resolutions. In terms of a theoretical model, the optimal rooting times of the two indexes range in [0.23, 0.33] and in [0.22, 0.35], respectively. Moreover, these factors that affect the correctness of the proposed method are generally analyzed. The measuring data preprocessing is necessary and helpful for any imaging process. Thus, the proposed method can be generally and widely used in any imaging process. Experimental results validate the two proposed indexes. PMID:25165735

Fast wavelet based algorithms for linear evolution equations

NASA Technical Reports Server (NTRS)

Engquist, Bjorn; Osher, Stanley; Zhong, Sifen

1992-01-01

A class was devised of fast wavelet based algorithms for linear evolution equations whose coefficients are time independent. The method draws on the work of Beylkin, Coifman, and Rokhlin which they applied to general Calderon-Zygmund type integral operators. A modification of their idea is applied to linear hyperbolic and parabolic equations, with spatially varying coefficients. A significant speedup over standard methods is obtained when applied to hyperbolic equations in one space dimension and parabolic equations in multidimensions.

Accurate and computationally efficient prediction of thermochemical properties of biomolecules using the generalized connectivity-based hierarchy.

PubMed

Sengupta, Arkajyoti; Ramabhadran, Raghunath O; Raghavachari, Krishnan

2014-08-14

In this study we have used the connectivity-based hierarchy (CBH) method to derive accurate heats of formation of a range of biomolecules, 18 amino acids and 10 barbituric acid/uracil derivatives. The hierarchy is based on the connectivity of the different atoms in a large molecule. It results in error-cancellation reaction schemes that are automated, general, and can be readily used for a broad range of organic molecules and biomolecules. Herein, we first locate stable conformational and tautomeric forms of these biomolecules using an accurate level of theory (viz. CCSD(T)/6-311++G(3df,2p)). Subsequently, the heats of formation of the amino acids are evaluated using the CBH-1 and CBH-2 schemes and routinely employed density functionals or wave function-based methods. The calculated heats of formation obtained herein using modest levels of theory and are in very good agreement with those obtained using more expensive W1-F12 and W2-F12 methods on amino acids and G3 results on barbituric acid derivatives. Overall, the present study (a) highlights the small effect of including multiple conformers in determining the heats of formation of biomolecules and (b) in concurrence with previous CBH studies, proves that use of the more effective error-cancelling isoatomic scheme (CBH-2) results in more accurate heats of formation with modestly sized basis sets along with common density functionals or wave function-based methods.

3 CFR 13520 - Executive Order 13520 of November 20, 2009. Reducing Improper Payments

Code of Federal Regulations, 2010 CFR

2010-01-01

... General and the Director of OMB, shall publish on the Internet information about improper payments under... Internet-based method to collect from the public information concerning suspected incidents of waste, fraud... Federal Government. (d) Agencies shall place a prominently displayed link to Internet-based resources for...

Model-based multi-fringe interferometry using Zernike polynomials

NASA Astrophysics Data System (ADS)

Gu, Wei; Song, Weihong; Wu, Gaofeng; Quan, Haiyang; Wu, Yongqian; Zhao, Wenchuan

2018-06-01

In this paper, a general phase retrieval method is proposed, which is based on one single interferogram with a small amount of fringes (either tilt or power). Zernike polynomials are used to characterize the phase to be measured; the phase distribution is reconstructed by a non-linear least squares method. Experiments show that the proposed method can obtain satisfactory results compared to the standard phase-shifting interferometry technique. Additionally, the retrace errors of proposed method can be neglected because of the few fringes; it does not need any auxiliary phase shifting facilities (low cost) and it is easy to implement without the process of phase unwrapping.

Managing corneal foreign bodies in office-based general practice.

PubMed

Fraenkel, Alison; Lee, Lawrence R; Lee, Graham A

2017-03-01

Patients with a corneal foreign body may first present to their general practitioner (GP). Safe and efficacious management of these presentations avoids sight-threatening and eye-threatening complications. Removal of a simple, superficial foreign body without a slit lamp is within The Royal Australian College of General Practitioners' (RACGP's) curriculum and scope of practice. Knowing the rele-vant procedural skills and indications for referral is equally important. The objective of this article is to provide an evidence-based and expert-based guide to the management of corneal foreign bodies in the GP's office. History is key to identifying patient characteristics and mechanisms of ocular injury that are red flags for referral. Examination tech-niques and methods of superficial foreign body removal without a slit lamp are outlined, as well as the procedural threshold for referral to an ophthalmologist.

An improved peak frequency shift method for Q estimation based on generalized seismic wavelet function

NASA Astrophysics Data System (ADS)

Wang, Qian; Gao, Jinghuai

2018-02-01

As a powerful tool for hydrocarbon detection and reservoir characterization, the quality factor, Q, provides useful information in seismic data processing and interpretation. In this paper, we propose a novel method for Q estimation. The generalized seismic wavelet (GSW) function was introduced to fit the amplitude spectrum of seismic waveforms with two parameters: fractional value and reference frequency. Then we derive an analytical relation between the GSW function and the Q factor of the medium. When a seismic wave propagates through a viscoelastic medium, the GSW function can be employed to fit the amplitude spectrum of the source and attenuated wavelets, then the fractional values and reference frequencies can be evaluated numerically from the discrete Fourier spectrum. After calculating the peak frequency based on the obtained fractional value and reference frequency, the relationship between the GSW function and the Q factor can be built by the conventional peak frequency shift method. Synthetic tests indicate that our method can achieve higher accuracy and be more robust to random noise compared with existing methods. Furthermore, the proposed method is applicable to different types of source wavelet. Field data application also demonstrates the effectiveness of our method in seismic attenuation and the potential in the reservoir characteristic.

Thermal Process Development

DTIC Science & Technology

1983-04-08

constraints and legislation, the methods used for disposing of military lethal agents such as GB, VX, and HD, have changed from land and sea burial to...uction is the most generally accepted method of destroying toxic organic materials for all cases where the toxicity is associated with the totality of...8217., preferred method can be based on estimates or determinations of the required incineration conditions and an appraisal of the requirement for

Free boundary skin current MHD (magnetohydrodynamic) equilibria

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reusch, M.F.

1988-02-01

Function theoretic methods in the complex plane are used to develop simple parametric hodograph formulae which generate sharp boundary equilibria of arbitrary shape. The related method of Gorenflo and Merkel is discussed. A numerical technique for the construction of solutions, based on one of the methods is presented. A study is made of the bifurcations of an equilibrium of general form. 28 refs., 9 figs.

A community resource benchmarking predictions of peptide binding to MHC-I molecules.

PubMed

Peters, Bjoern; Bui, Huynh-Hoa; Frankild, Sune; Nielson, Morten; Lundegaard, Claus; Kostem, Emrah; Basch, Derek; Lamberth, Kasper; Harndahl, Mikkel; Fleri, Ward; Wilson, Stephen S; Sidney, John; Lund, Ole; Buus, Soren; Sette, Alessandro

2006-06-09

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

Support vector machines for nuclear reactor state estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zavaljevski, N.; Gross, K. C.

2000-02-14

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformedmore » into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.« less

Quantum mechanical generalized phase-shift approach to atom-surface scattering: a Feshbach projection approach to dealing with closed channel effects.

PubMed

Maji, Kaushik; Kouri, Donald J

2011-03-28

We have developed a new method for solving quantum dynamical scattering problems, using the time-independent Schrödinger equation (TISE), based on a novel method to generalize a "one-way" quantum mechanical wave equation, impose correct boundary conditions, and eliminate exponentially growing closed channel solutions. The approach is readily parallelized to achieve approximate N(2) scaling, where N is the number of coupled equations. The full two-way nature of the TISE is included while propagating the wave function in the scattering variable and the full S-matrix is obtained. The new algorithm is based on a "Modified Cayley" operator splitting approach, generalizing earlier work where the method was applied to the time-dependent Schrödinger equation. All scattering variable propagation approaches to solving the TISE involve solving a Helmholtz-type equation, and for more than one degree of freedom, these are notoriously ill-behaved, due to the unavoidable presence of exponentially growing contributions to the numerical solution. Traditionally, the method used to eliminate exponential growth has posed a major obstacle to the full parallelization of such propagation algorithms. We stabilize by using the Feshbach projection operator technique to remove all the nonphysical exponentially growing closed channels, while retaining all of the propagating open channel components, as well as exponentially decaying closed channel components.

Making a Difference in Learners' Lives: Results of a Study Based on Adult Literacy Programs

ERIC Educational Resources Information Center

Terry, Marion

2006-01-01

Using qualitative methods, the author explores how adult learners' lives are affected by participating in adult literacy programs in Manitoba, Canada. After a description of research methods, changes in six major areas of learners' lives are discussed: general attitudes toward life, levels of interpersonal awareness, self-esteem, academic…

Aeroelastic analysis of a troposkien-type wind turbine blade

NASA Technical Reports Server (NTRS)

Nitzsche, F.

1981-01-01

The linear aeroelastic equations for one curved blade of a vertical axis wind turbine in state vector form are presented. The method is based on a simple integrating matrix scheme together with the transfer matrix idea. The method is proposed as a convenient way of solving the associated eigenvalue problem for general support conditions.

Highly accurate symplectic element based on two variational principles

NASA Astrophysics Data System (ADS)

Qing, Guanghui; Tian, Jia

2018-02-01

For the stability requirement of numerical resultants, the mathematical theory of classical mixed methods are relatively complex. However, generalized mixed methods are automatically stable, and their building process is simple and straightforward. In this paper, based on the seminal idea of the generalized mixed methods, a simple, stable, and highly accurate 8-node noncompatible symplectic element (NCSE8) was developed by the combination of the modified Hellinger-Reissner mixed variational principle and the minimum energy principle. To ensure the accuracy of in-plane stress results, a simultaneous equation approach was also suggested. Numerical experimentation shows that the accuracy of stress results of NCSE8 are nearly the same as that of displacement methods, and they are in good agreement with the exact solutions when the mesh is relatively fine. NCSE8 has advantages of the clearing concept, easy calculation by a finite element computer program, higher accuracy and wide applicability for various linear elasticity compressible and nearly incompressible material problems. It is possible that NCSE8 becomes even more advantageous for the fracture problems due to its better accuracy of stresses.

Pilot Test of a Novel Method for Assessing Community Response to Low-Amplitude Sonic Booms

NASA Technical Reports Server (NTRS)

Fidell, Sanford; Horonjeff, Richard D.; Harris, Michael

2012-01-01

A pilot test of a novel method for assessing residents annoyance to sonic booms was performed. During a two-week period, residents of the base housing area at Edwards Air Force Base provided data on their reactions to sonic booms using Smartphone-based interviews. Noise measurements were conducted at the same time. The report presents information about data collection methods and about test participants reactions to low-amplitude sonic booms. The latter information should not be viewed as definitive for several reasons. It may not be reliably generalized to the wider U.S. residential population (because it was not derived from a representative random sample) and the sample itself was not large.

Ray Tracing and Modal Methods for Modeling Radio Propagation in Tunnels With Rough Walls

PubMed Central

Zhou, Chenming

2017-01-01

At the ultrahigh frequencies common to portable radios, tunnels such as mine entries are often modeled by hollow dielectric waveguides. The roughness condition of the tunnel walls has an influence on radio propagation, and therefore should be taken into account when an accurate power prediction is needed. This paper investigates how wall roughness affects radio propagation in tunnels, and presents a unified ray tracing and modal method for modeling radio propagation in tunnels with rough walls. First, general analytical formulas for modeling the influence of the wall roughness are derived, based on the modal method and the ray tracing method, respectively. Second, the equivalence of the ray tracing and modal methods in the presence of wall roughnesses is mathematically proved, by showing that the ray tracing-based analytical formula can converge to the modal-based formula through the Poisson summation formula. The derivation and findings are verified by simulation results based on ray tracing and modal methods. PMID:28935995

Photometric redshift estimation via deep learning. Generalized and pre-classification-less, image based, fully probabilistic redshifts

NASA Astrophysics Data System (ADS)

D'Isanto, A.; Polsterer, K. L.

2018-01-01

Context. The need to analyze the available large synoptic multi-band surveys drives the development of new data-analysis methods. Photometric redshift estimation is one field of application where such new methods improved the results, substantially. Up to now, the vast majority of applied redshift estimation methods have utilized photometric features. Aims: We aim to develop a method to derive probabilistic photometric redshift directly from multi-band imaging data, rendering pre-classification of objects and feature extraction obsolete. Methods: A modified version of a deep convolutional network was combined with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) were applied as performance criteria. We have adopted a feature based random forest and a plain mixture density network to compare performances on experiments with data from SDSS (DR9). Results: We show that the proposed method is able to predict redshift PDFs independently from the type of source, for example galaxies, quasars or stars. Thereby the prediction performance is better than both presented reference methods and is comparable to results from the literature. Conclusions: The presented method is extremely general and allows us to solve of any kind of probabilistic regression problems based on imaging data, for example estimating metallicity or star formation rate of galaxies. This kind of methodology is tremendously important for the next generation of surveys.

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

Methods to recognize work-related cancer in workplaces, the general population, and by experts in the clinic, a Norwegian experience

PubMed Central

2011-01-01

Background In most countries, the numbers of work-related cancer identified are much lower than are the estimated total burden of cancer caused by exposure at work. Therefore, there is a great need to use all available practical as well as epidemiological methods for identification as well as to develop new methods of recognizing cases of work-related cancers. Methods Primarily based on practical experiences from Norway, methods to identify cases of possible work-related cancers in the general population and at workplaces as well as methods to recognize more specific cases after referral to specialized clinics are reviewed in this publication. Results Countries applying a number of the available methods to detect work-related cancer reach a reporting rate of 60 such cases per million, while other countries that do not employ such methods hardly identify any cases. As most subjects previously exposed to cancer causing agents and substances at work are gradually recruited out of work, methods should be versatile for identification of cases in the general population, as well as at work. Conclusions Even in countries using a number of the available methods for identification, only a limited fraction of the real number of work-related cancer are notified to the labour inspectorate. Clinicians should be familiar with the methods and do the best to identify work-related cancer to serve prevention. PMID:21899752

Within-brain classification for brain tumor segmentation.

PubMed

Havaei, Mohammad; Larochelle, Hugo; Poulin, Philippe; Jodoin, Pierre-Marc

2016-05-01

In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem. This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction. We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain. As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.

Sensor placement in nuclear reactors based on the generalized empirical interpolation method

NASA Astrophysics Data System (ADS)

Argaud, J.-P.; Bouriquet, B.; de Caso, F.; Gong, H.; Maday, Y.; Mula, O.

2018-06-01

In this paper, we apply the so-called generalized empirical interpolation method (GEIM) to address the problem of sensor placement in nuclear reactors. This task is challenging due to the accumulation of a number of difficulties like the complexity of the underlying physics and the constraints in the admissible sensor locations and their number. As a result, the placement, still today, strongly relies on the know-how and experience of engineers from different areas of expertise. The present methodology contributes to making this process become more systematic and, in turn, simplify and accelerate the procedure.

Random search optimization based on genetic algorithm and discriminant function

NASA Technical Reports Server (NTRS)

Kiciman, M. O.; Akgul, M.; Erarslanoglu, G.

1990-01-01

The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.

Ubiquitous Creation of Bas-Relief Surfaces with Depth-of-Field Effects Using Smartphones.

PubMed

Sohn, Bong-Soo

2017-03-11

This paper describes a new method to automatically generate digital bas-reliefs with depth-of-field effects from general scenes. Most previous methods for bas-relief generation take input in the form of 3D models. However, obtaining 3D models of real scenes or objects is often difficult, inaccurate, and time-consuming. From this motivation, we developed a method that takes as input a set of photographs that can be quickly and ubiquitously captured by ordinary smartphone cameras. A depth map is computed from the input photographs. The value range of the depth map is compressed and used as a base map representing the overall shape of the bas-relief. However, the resulting base map contains little information on details of the scene. Thus, we construct a detail map using pixel values of the input image to express the details. The base and detail maps are blended to generate a new depth map that reflects both overall depth and scene detail information. This map is selectively blurred to simulate the depth-of-field effects. The final depth map is converted to a bas-relief surface mesh. Experimental results show that our method generates a realistic bas-relief surface of general scenes with no expensive manual processing.

Ubiquitous Creation of Bas-Relief Surfaces with Depth-of-Field Effects Using Smartphones

PubMed Central

Sohn, Bong-Soo

2017-01-01

This paper describes a new method to automatically generate digital bas-reliefs with depth-of-field effects from general scenes. Most previous methods for bas-relief generation take input in the form of 3D models. However, obtaining 3D models of real scenes or objects is often difficult, inaccurate, and time-consuming. From this motivation, we developed a method that takes as input a set of photographs that can be quickly and ubiquitously captured by ordinary smartphone cameras. A depth map is computed from the input photographs. The value range of the depth map is compressed and used as a base map representing the overall shape of the bas-relief. However, the resulting base map contains little information on details of the scene. Thus, we construct a detail map using pixel values of the input image to express the details. The base and detail maps are blended to generate a new depth map that reflects both overall depth and scene detail information. This map is selectively blurred to simulate the depth-of-field effects. The final depth map is converted to a bas-relief surface mesh. Experimental results show that our method generates a realistic bas-relief surface of general scenes with no expensive manual processing. PMID:28287487

Accelerating EPI distortion correction by utilizing a modern GPU-based parallel computation.

PubMed

Yang, Yao-Hao; Huang, Teng-Yi; Wang, Fu-Nien; Chuang, Tzu-Chao; Chen, Nan-Kuei

2013-04-01

The combination of phase demodulation and field mapping is a practical method to correct echo planar imaging (EPI) geometric distortion. However, since phase dispersion accumulates in each phase-encoding step, the calculation complexity of phase modulation is Ny-fold higher than conventional image reconstructions. Thus, correcting EPI images via phase demodulation is generally a time-consuming task. Parallel computing by employing general-purpose calculations on graphics processing units (GPU) can accelerate scientific computing if the algorithm is parallelized. This study proposes a method that incorporates the GPU-based technique into phase demodulation calculations to reduce computation time. The proposed parallel algorithm was applied to a PROPELLER-EPI diffusion tensor data set. The GPU-based phase demodulation method reduced the EPI distortion correctly, and accelerated the computation. The total reconstruction time of the 16-slice PROPELLER-EPI diffusion tensor images with matrix size of 128 × 128 was reduced from 1,754 seconds to 101 seconds by utilizing the parallelized 4-GPU program. GPU computing is a promising method to accelerate EPI geometric correction. The resulting reduction in computation time of phase demodulation should accelerate postprocessing for studies performed with EPI, and should effectuate the PROPELLER-EPI technique for clinical practice. Copyright © 2011 by the American Society of Neuroimaging.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

A method based on IHS cylindrical transform model for quality assessment of image fusion

NASA Astrophysics Data System (ADS)

Zhu, Xiaokun; Jia, Yonghong

2005-10-01

Image fusion technique has been widely applied to remote sensing image analysis and processing, and methods for quality assessment of image fusion in remote sensing have also become the research issues at home and abroad. Traditional assessment methods combine calculation of quantitative indexes and visual interpretation to compare fused images quantificationally and qualitatively. However, in the existing assessment methods, there are two defects: on one hand, most imdexes lack the theoretic support to compare different fusion methods. On the hand, there is not a uniform preference for most of the quantitative assessment indexes when they are applied to estimate the fusion effects. That is, the spatial resolution and spectral feature could not be analyzed synchronously by these indexes and there is not a general method to unify the spatial and spectral feature assessment. So in this paper, on the basis of the approximate general model of four traditional fusion methods, including Intensity Hue Saturation(IHS) triangle transform fusion, High Pass Filter(HPF) fusion, Principal Component Analysis(PCA) fusion, Wavelet Transform(WT) fusion, a correlation coefficient assessment method based on IHS cylindrical transform is proposed. By experiments, this method can not only get the evaluation results of spatial and spectral features on the basis of uniform preference, but also can acquire the comparison between fusion image sources and fused images, and acquire differences among fusion methods. Compared with the traditional assessment methods, the new methods is more intuitionistic, and in accord with subjective estimation.

Rank-based permutation approaches for non-parametric factorial designs.

PubMed

Umlauft, Maria; Konietschke, Frank; Pauly, Markus

2017-11-01

Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal-Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability. © 2017 The British Psychological Society.

Evaluation of lung and chest wall mechanics during anaesthesia using the PEEP-step method.

PubMed

Persson, P; Stenqvist, O; Lundin, S

2018-04-01

Postoperative pulmonary complications are common. Between patients there are differences in lung and chest wall mechanics. Individualised mechanical ventilation based on measurement of transpulmonary pressures would be a step forward. A previously described method evaluates lung and chest wall mechanics from a change of ΔPEEP and calculation of change in end-expiratory lung volume (ΔEELV). The aim of the present study was to validate this PEEP-step method (PSM) during general anaesthesia by comparing it with the conventional method using oesophageal pressure (PES) measurements. In 24 lung healthy subjects (BMI 18.5-32), three different sizes of PEEP steps were performed during general anaesthesia and ΔEELVs were calculated. Transpulmonary driving pressure (ΔPL) for a tidal volume equal to each ΔEELV was measured using PES measurements and compared to ΔPEEP with limits of agreement and intraclass correlation coefficients (ICC). ΔPL calculated with both methods was compared with a Bland-Altman plot. Mean differences between ΔPEEP and ΔPL were <0.15 cm H 2 O, 95% limits of agreements -2.1 to 2.0 cm H 2 O, ICC 0.6-0.83. Mean differences between ΔPL calculated by both methods were <0.2 cm H 2 O. Ratio of lung elastance and respiratory system elastance was 0.5-0.95. The large variation in mechanical properties among the lung healthy patients stresses the need for individualised ventilator settings based on measurements of lung and chest wall mechanics. The agreement between ΔPLs measured by the two methods during general anaesthesia suggests the use of the non-invasive PSM in this patient population. NCT 02830516. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Estimating survival of radio-tagged birds

USGS Publications Warehouse

Bunck, C.M.; Pollock, K.H.; Lebreton, J.-D.; North, P.M.

1993-01-01

Parametric and nonparametric methods for estimating survival of radio-tagged birds are described. The general assumptions of these methods are reviewed. An estimate based on the assumption of constant survival throughout the period is emphasized in the overview of parametric methods. Two nonparametric methods, the Kaplan-Meier estimate of the survival funcrion and the log rank test, are explained in detail The link between these nonparametric methods and traditional capture-recapture models is discussed aloag with considerations in designing studies that use telemetry techniques to estimate survival.

A Correlation-Based Transition Model using Local Variables. Part 2; Test Cases and Industrial Applications

NASA Technical Reports Server (NTRS)

Langtry, R. B.; Menter, F. R.; Likki, S. R.; Suzen, Y. B.; Huang, P. G.; Volker, S.

2006-01-01

A new correlation-based transition model has been developed, which is built strictly on local variables. As a result, the transition model is compatible with modern computational fluid dynamics (CFD) methods using unstructured grids and massive parallel execution. The model is based on two transport equations, one for the intermittency and one for the transition onset criteria in terms of momentum thickness Reynolds number. The proposed transport equations do not attempt to model the physics of the transition process (unlike, e.g., turbulence models), but form a framework for the implementation of correlation-based models into general-purpose CFD methods.

Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

NASA Technical Reports Server (NTRS)

Leong, Harrison Monfook

1988-01-01

General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

Inertia Estimation of Spacecraft Based on Modified Law of Conservation of Angular Momentum

NASA Astrophysics Data System (ADS)

Kim, Dong Hoon; Choi, Dae-Gyun; Oh, Hwa-Suk

2010-12-01

In general, the information of inertia properties is required to control a spacecraft. The inertia properties are changed by some activities such as consumption of propellant, deployment of solar panel, sloshing, etc. Extensive estimation methods have been investigated to obtain the precise inertia properties. The gyro-based attitude data including noise and bias needs to be compensated for improvement of attitude control accuracy. A modified estimation method based on the law of conservation of angular momentum is suggested to avoid inconvenience like filtering process for noiseeffect compensation. The conventional method is modified and beforehand estimated moment of inertia is applied to improve estimation efficiency of product of inertia. The performance of the suggested method has been verified for the case of STSAT-3, Korea Science Technology Satellite.

Beta-function B-spline smoothing on triangulations

NASA Astrophysics Data System (ADS)

Dechevsky, Lubomir T.; Zanaty, Peter

2013-03-01

In this work we investigate a novel family of Ck-smooth rational basis functions on triangulations for fitting, smoothing, and denoising geometric data. The introduced basis function is closely related to a recently introduced general method introduced in utilizing generalized expo-rational B-splines, which provides Ck-smooth convex resolutions of unity on very general disjoint partitions and overlapping covers of multidimensional domains with complex geometry. One of the major advantages of this new triangular construction is its locality with respect to the star-1 neighborhood of the vertex on which the said base is providing Hermite interpolation. This locality of the basis functions can be in turn utilized in adaptive methods, where, for instance a local refinement of the underlying triangular mesh affects only the refined domain, whereas, in other method one needs to investigate what changes are occurring outside of the refined domain. Both the triangular and the general smooth constructions have the potential to become a new versatile tool of Computer Aided Geometric Design (CAGD), Finite and Boundary Element Analysis (FEA/BEA) and Iso-geometric Analysis (IGA).

Scalable parallel elastic-plastic finite element analysis using a quasi-Newton method with a balancing domain decomposition preconditioner

NASA Astrophysics Data System (ADS)

Yusa, Yasunori; Okada, Hiroshi; Yamada, Tomonori; Yoshimura, Shinobu

2018-04-01

A domain decomposition method for large-scale elastic-plastic problems is proposed. The proposed method is based on a quasi-Newton method in conjunction with a balancing domain decomposition preconditioner. The use of a quasi-Newton method overcomes two problems associated with the conventional domain decomposition method based on the Newton-Raphson method: (1) avoidance of a double-loop iteration algorithm, which generally has large computational complexity, and (2) consideration of the local concentration of nonlinear deformation, which is observed in elastic-plastic problems with stress concentration. Moreover, the application of a balancing domain decomposition preconditioner ensures scalability. Using the conventional and proposed domain decomposition methods, several numerical tests, including weak scaling tests, were performed. The convergence performance of the proposed method is comparable to that of the conventional method. In particular, in elastic-plastic analysis, the proposed method exhibits better convergence performance than the conventional method.

Simple Test Functions in Meshless Local Petrov-Galerkin Methods

NASA Technical Reports Server (NTRS)

Raju, Ivatury S.

2016-01-01

Two meshless local Petrov-Galerkin (MLPG) methods based on two different trial functions but that use a simple linear test function were developed for beam and column problems. These methods used generalized moving least squares (GMLS) and radial basis (RB) interpolation functions as trial functions. These two methods were tested on various patch test problems. Both methods passed the patch tests successfully. Then the methods were applied to various beam vibration problems and problems involving Euler and Beck's columns. Both methods yielded accurate solutions for all problems studied. The simple linear test function offers considerable savings in computing efforts as the domain integrals involved in the weak form are avoided. The two methods based on this simple linear test function method produced accurate results for frequencies and buckling loads. Of the two methods studied, the method with radial basis trial functions is very attractive as the method is simple, accurate, and robust.

Multivariate assessment of event-related potentials with the t-CWT method.

PubMed

Bostanov, Vladimir

2015-11-05

Event-related brain potentials (ERPs) are usually assessed with univariate statistical tests although they are essentially multivariate objects. Brain-computer interface applications are a notable exception to this practice, because they are based on multivariate classification of single-trial ERPs. Multivariate ERP assessment can be facilitated by feature extraction methods. One such method is t-CWT, a mathematical-statistical algorithm based on the continuous wavelet transform (CWT) and Student's t-test. This article begins with a geometric primer on some basic concepts of multivariate statistics as applied to ERP assessment in general and to the t-CWT method in particular. Further, it presents for the first time a detailed, step-by-step, formal mathematical description of the t-CWT algorithm. A new multivariate outlier rejection procedure based on principal component analysis in the frequency domain is presented as an important pre-processing step. The MATLAB and GNU Octave implementation of t-CWT is also made publicly available for the first time as free and open source code. The method is demonstrated on some example ERP data obtained in a passive oddball paradigm. Finally, some conceptually novel applications of the multivariate approach in general and of the t-CWT method in particular are suggested and discussed. Hopefully, the publication of both the t-CWT source code and its underlying mathematical algorithm along with a didactic geometric introduction to some basic concepts of multivariate statistics would make t-CWT more accessible to both users and developers in the field of neuroscience research.

Cannabis cultivation in Quebec: between space-time hotspots and coldspots.

PubMed

Chadillon-Farinacci, Véronique; Apparicio, Philippe; Morselli, Carlo

2015-03-01

Cannabis cultivation has become increasingly localized, whether soil-based or hydroponic growing methods are used. Characteristics of a given location, such as its climate and the equipment it requires may influence general accessibility or attract different types of offenders based on potential profits. The location of crops, especially hydroponic crops, suggests a certain proximity to the consumer market via semi-urban and urban environments, while making it possible to avoid detection. This article examines the cannabis market through its cultivation. The stability of temporal and spatial clusters of cannabis cultivation, hotspots, and coldspots between 2001 and 2009 in the province of Quebec, Canada, are addressed. Studying the geography of crime is not a new endeavor, but coldspots are rarely documented in drug market research. Using arrests and general population data, as well as Kulldorff's scan statistics, results show that the temporal distribution of cannabis cultivation is highly seasonal for soil-based methods. Hydroponic production shows adaptation to its soil-based counterpart. Stable patterns are found for both spatial distributions. Hotspots for soil-based cultivation are found near several urban centers and the Ontario border. For hydroponic cannabis cultivation, a new hotspot suggests the emergence of an American demand for Quebec-grown cannabis between 2007 and 2009. Curiously, the region surrounding Montreal, the largest urban center in Quebec, is a recurrent and stable coldspot for both methods of cultivation. For all periods, spatial clusters are stronger for soil-based methods than in the hydroponic context. Temporal differences and spatial similarities between soil-based cultivation and hydroponic cultivation are discussed. The role of the metropolis is also addressed. Copyright © 2015 Elsevier B.V. All rights reserved.

Framework for event-based semidistributed modeling that unifies the SCS-CN method, VIC, PDM, and TOPMODEL

NASA Astrophysics Data System (ADS)

Bartlett, M. S.; Parolari, A. J.; McDonnell, J. J.; Porporato, A.

2016-09-01

Hydrologists and engineers may choose from a range of semidistributed rainfall-runoff models such as VIC, PDM, and TOPMODEL, all of which predict runoff from a distribution of watershed properties. However, these models are not easily compared to event-based data and are missing ready-to-use analytical expressions that are analogous to the SCS-CN method. The SCS-CN method is an event-based model that describes the runoff response with a rainfall-runoff curve that is a function of the cumulative storm rainfall and antecedent wetness condition. Here we develop an event-based probabilistic storage framework and distill semidistributed models into analytical, event-based expressions for describing the rainfall-runoff response. The event-based versions called VICx, PDMx, and TOPMODELx also are extended with a spatial description of the runoff concept of "prethreshold" and "threshold-excess" runoff, which occur, respectively, before and after infiltration exceeds a storage capacity threshold. For total storm rainfall and antecedent wetness conditions, the resulting ready-to-use analytical expressions define the source areas (fraction of the watershed) that produce runoff by each mechanism. They also define the probability density function (PDF) representing the spatial variability of runoff depths that are cumulative values for the storm duration, and the average unit area runoff, which describes the so-called runoff curve. These new event-based semidistributed models and the traditional SCS-CN method are unified by the same general expression for the runoff curve. Since the general runoff curve may incorporate different model distributions, it may ease the way for relating such distributions to land use, climate, topography, ecology, geology, and other characteristics.

Patient-based surveying: a cost-effective approach for reaching large markets.

PubMed

Byer, S

1995-01-01

Member-based surveying is an important tool for managed care companies to discern newer and better ways in which to keep their current members satisfied, develop products that will attract new members, and to gauge changes of course in health consumer opinion. This article discusses a consumer friendly and cost-effective method to survey members and the general public that has produced a very positive response for a modest investment. The response rate will likely improve over time as the method gains broader acceptance.

Quantum tomography for measuring experimentally the matrix elements of an arbitrary quantum operation.

PubMed

D'Ariano, G M; Lo Presti, P

2001-05-07

Quantum operations describe any state change allowed in quantum mechanics, including the evolution of an open system or the state change due to a measurement. We present a general method based on quantum tomography for measuring experimentally the matrix elements of an arbitrary quantum operation. As input the method needs only a single entangled state. The feasibility of the technique for the electromagnetic field is shown, and the experimental setup is illustrated based on homodyne tomography of a twin beam.

A connectionist model for dynamic control

NASA Technical Reports Server (NTRS)

Whitfield, Kevin C.; Goodall, Sharon M.; Reggia, James A.

1989-01-01

The application of a connectionist modeling method known as competition-based spreading activation to a camera tracking task is described. The potential is explored for automation of control and planning applications using connectionist technology. The emphasis is on applications suitable for use in the NASA Space Station and in related space activities. The results are quite general and could be applicable to control systems in general.

Detection of coupling delay: A problem not yet solved

NASA Astrophysics Data System (ADS)

Coufal, David; Jakubík, Jozef; Jajcay, Nikola; Hlinka, Jaroslav; Krakovská, Anna; Paluš, Milan

2017-08-01

Nonparametric detection of coupling delay in unidirectionally and bidirectionally coupled nonlinear dynamical systems is examined. Both continuous and discrete-time systems are considered. Two methods of detection are assessed—the method based on conditional mutual information—the CMI method (also known as the transfer entropy method) and the method of convergent cross mapping—the CCM method. Computer simulations show that neither method is generally reliable in the detection of coupling delays. For continuous-time chaotic systems, the CMI method appears to be more sensitive and applicable in a broader range of coupling parameters than the CCM method. In the case of tested discrete-time dynamical systems, the CCM method has been found to be more sensitive, while the CMI method required much stronger coupling strength in order to bring correct results. However, when studied systems contain a strong oscillatory component in their dynamics, results of both methods become ambiguous. The presented study suggests that results of the tested algorithms should be interpreted with utmost care and the nonparametric detection of coupling delay, in general, is a problem not yet solved.

Comparison of outlier identification methods in hospital surgical quality improvement programs.

PubMed

Bilimoria, Karl Y; Cohen, Mark E; Merkow, Ryan P; Wang, Xue; Bentrem, David J; Ingraham, Angela M; Richards, Karen; Hall, Bruce L; Ko, Clifford Y

2010-10-01

Surgeons and hospitals are being increasingly assessed by third parties regarding surgical quality and outcomes, and much of this information is reported publicly. Our objective was to compare various methods used to classify hospitals as outliers in established surgical quality assessment programs by applying each approach to a single data set. Using American College of Surgeons National Surgical Quality Improvement Program data (7/2008-6/2009), hospital risk-adjusted 30-day morbidity and mortality were assessed for general surgery at 231 hospitals (cases = 217,630) and for colorectal surgery at 109 hospitals (cases = 17,251). The number of outliers (poor performers) identified using different methods and criteria were compared. The overall morbidity was 10.3% for general surgery and 25.3% for colorectal surgery. The mortality was 1.6% for general surgery and 4.0% for colorectal surgery. Programs used different methods (logistic regression, hierarchical modeling, partitioning) and criteria (P < 0.01, P < 0.05, P < 0.10) to identify outliers. Depending on outlier identification methods and criteria employed, when each approach was applied to this single dataset, the number of outliers ranged from 7 to 57 hospitals for general surgery morbidity, 1 to 57 hospitals for general surgery mortality, 4 to 27 hospitals for colorectal morbidity, and 0 to 27 hospitals for colorectal mortality. There was considerable variation in the number of outliers identified using different detection approaches. Quality programs seem to be utilizing outlier identification methods contrary to what might be expected, thus they should justify their methodology based on the intent of the program (i.e., quality improvement vs. reimbursement). Surgeons and hospitals should be aware of variability in methods used to assess their performance as these outlier designations will likely have referral and reimbursement consequences.

Applications to car bodies - Generalized layout design of three-dimensional shells

NASA Technical Reports Server (NTRS)

Fukushima, Junichi; Suzuki, Katsuyuki; Kikuchi, Noboru

1993-01-01

We shall describe applications of the homogenization method, formulated in Part 1, to design layout of car bodies represented by three-dimensional shell structures based on a multi-loading optimization.

General hybrid projective complete dislocated synchronization with non-derivative and derivative coupling based on parameter identification in several chaotic and hyperchaotic systems

NASA Astrophysics Data System (ADS)

Sun, Jun-Wei; Shen, Yi; Zhang, Guo-Dong; Wang, Yan-Feng; Cui, Guang-Zhao

2013-04-01

According to the Lyapunov stability theorem, a new general hybrid projective complete dislocated synchronization scheme with non-derivative and derivative coupling based on parameter identification is proposed under the framework of drive-response systems. Every state variable of the response system equals the summation of the hybrid drive systems in the previous hybrid synchronization. However, every state variable of the drive system equals the summation of the hybrid response systems while evolving with time in our method. Complete synchronization, hybrid dislocated synchronization, projective synchronization, non-derivative and derivative coupling, and parameter identification are included as its special item. The Lorenz chaotic system, Rössler chaotic system, memristor chaotic oscillator system, and hyperchaotic Lü system are discussed to show the effectiveness of the proposed methods.

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Annealed importance sampling with constant cooling rate

NASA Astrophysics Data System (ADS)

Giovannelli, Edoardo; Cardini, Gianni; Gellini, Cristina; Pietraperzia, Giangaetano; Chelli, Riccardo

2015-02-01

Annealed importance sampling is a simulation method devised by Neal [Stat. Comput. 11, 125 (2001)] to assign weights to configurations generated by simulated annealing trajectories. In particular, the equilibrium average of a generic physical quantity can be computed by a weighted average exploiting weights and estimates of this quantity associated to the final configurations of the annealed trajectories. Here, we review annealed importance sampling from the perspective of nonequilibrium path-ensemble averages [G. E. Crooks, Phys. Rev. E 61, 2361 (2000)]. The equivalence of Neal's and Crooks' treatments highlights the generality of the method, which goes beyond the mere thermal-based protocols. Furthermore, we show that a temperature schedule based on a constant cooling rate outperforms stepwise cooling schedules and that, for a given elapsed computer time, performances of annealed importance sampling are, in general, improved by increasing the number of intermediate temperatures.

Development of WAIS-III General Ability Index Minus WMS-III memory discrepancy scores.

PubMed

Lange, Rael T; Chelune, Gordon J; Tulsky, David S

2006-09-01

Analysis of the discrepancy between intellectual functioning and memory ability has received some support as a useful means for evaluating memory impairment. In recent additions to Wechlser scale interpretation, the WAIS-III General Ability Index (GAI) and the WMS-III Delayed Memory Index (DMI) were developed. The purpose of this investigation is to develop base rate data for GAI-IMI, GAI-GMI, and GAI-DMI discrepancy scores using data from the WAIS-III/WMS-III standardization sample (weighted N = 1250). Base rate tables were developed using the predicted-difference method and two simple-difference methods (i.e., stratified and non-stratified). These tables provide valuable data for clinical reference purposes to determine the frequency of GAI-IMI, GAI-GMI, and GAI-DMI discrepancy scores in the WAIS-III/WMS-III standardization sample.

Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation.

PubMed

Kang, Sung-Won; Lee, Woo-Jin; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Kim, Tae-Il; Yi, Won-Jin

2015-03-01

We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method.

Survey of in-situ and remote sensing methods for soil moisture determination

NASA Technical Reports Server (NTRS)

Schmugge, T. J.; Jackson, T. J.; Mckim, H. L.

1981-01-01

General methods for determining the moisture content in the surface layers of the soil based on in situ or point measurements, soil water models and remote sensing observations are surveyed. In situ methods described include gravimetric techniques, nuclear techniques based on neutron scattering or gamma-ray attenuation, electromagnetic techniques, tensiometric techniques and hygrometric techniques. Soil water models based on column mass balance treat soil moisture contents as a result of meteorological inputs (precipitation, runoff, subsurface flow) and demands (evaporation, transpiration, percolation). The remote sensing approaches are based on measurements of the diurnal range of surface temperature and the crop canopy temperature in the thermal infrared, measurements of the radar backscattering coefficient in the microwave region, and measurements of microwave emission or brightness temperature. Advantages and disadvantages of the various methods are pointed out, and it is concluded that a successful monitoring system must incorporate all of the approaches considered.

Progress in multirate digital control system design

NASA Technical Reports Server (NTRS)

Berg, Martin C.; Mason, Gregory S.

1991-01-01

A new methodology for multirate sampled-data control design based on a new generalized control law structure, two new parameter-optimization-based control law synthesis methods, and a new singular-value-based robustness analysis method are described. The control law structure can represent multirate sampled-data control laws of arbitrary structure and dynamic order, with arbitrarily prescribed sampling rates for all sensors and update rates for all processor states and actuators. The two control law synthesis methods employ numerical optimization to determine values for the control law parameters. The robustness analysis method is based on the multivariable Nyquist criterion applied to the loop transfer function for the sampling period equal to the period of repetition of the system's complete sampling/update schedule. The complete methodology is demonstrated by application to the design of a combination yaw damper and modal suppression system for a commercial aircraft.

A quasiparticle-based multi-reference coupled-cluster method.

PubMed

Rolik, Zoltán; Kállay, Mihály

2014-10-07

The purpose of this paper is to introduce a quasiparticle-based multi-reference coupled-cluster (MRCC) approach. The quasiparticles are introduced via a unitary transformation which allows us to represent a complete active space reference function and other elements of an orthonormal multi-reference (MR) basis in a determinant-like form. The quasiparticle creation and annihilation operators satisfy the fermion anti-commutation relations. On the basis of these quasiparticles, a generalization of the normal-ordered operator products for the MR case can be introduced as an alternative to the approach of Mukherjee and Kutzelnigg [Recent Prog. Many-Body Theor. 4, 127 (1995); Mukherjee and Kutzelnigg, J. Chem. Phys. 107, 432 (1997)]. Based on the new normal ordering any quasiparticle-based theory can be formulated using the well-known diagram techniques. Beyond the general quasiparticle framework we also present a possible realization of the unitary transformation. The suggested transformation has an exponential form where the parameters, holding exclusively active indices, are defined in a form similar to the wave operator of the unitary coupled-cluster approach. The definition of our quasiparticle-based MRCC approach strictly follows the form of the single-reference coupled-cluster method and retains several of its beneficial properties. Test results for small systems are presented using a pilot implementation of the new approach and compared to those obtained by other MR methods.

Target attribute-based false alarm rejection in small infrared target detection

NASA Astrophysics Data System (ADS)

Kim, Sungho

2012-11-01

Infrared search and track is an important research area in military applications. Although there are a lot of works on small infrared target detection methods, we cannot apply them in real field due to high false alarm rate caused by clutters. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. Eight kinds of target features are extracted and analyzed statistically. Learning-based classifiers such as SVM and Adaboost are developed and compared with conventional classifiers for real infrared images. In addition, the generalization capability is also inspected for various infrared clutters.

System and method for knowledge based matching of users in a network

DOEpatents

Verspoor, Cornelia Maria [Santa Fe, NM; Sims, Benjamin Hayden [Los Alamos, NM; Ambrosiano, John Joseph [Los Alamos, NM; Cleland, Timothy James [Los Alamos, NM

2011-04-26

A knowledge-based system and methods to matchmaking and social network extension are disclosed. The system is configured to allow users to specify knowledge profiles, which are collections of concepts that indicate a certain topic or area of interest selected from an. The system utilizes the knowledge model as the semantic space within which to compare similarities in user interests. The knowledge model is hierarchical so that indications of interest in specific concepts automatically imply interest in more general concept. Similarity measures between profiles may then be calculated based on suitable distance formulas within this space.

Application of perturbation theory to lattice calculations based on method of cyclic characteristics

NASA Astrophysics Data System (ADS)

Assawaroongruengchot, Monchai

Perturbation theory is a technique used for the estimation of changes in performance functionals, such as linear reaction rate ratio and eigenvalue affected by small variations in reactor core compositions. Here the algorithm of perturbation theory is developed for the multigroup integral neutron transport problems in 2D fuel assemblies with isotropic scattering. The integral transport equation is used in the perturbative formulation because it represents the interconnecting neutronic systems of the lattice assemblies via the tracking lines. When the integral neutron transport equation is used in the formulation, one needs to solve the resulting integral transport equations for the flux importance and generalized flux importance functions. The relationship between the generalized flux importance and generalized source importance functions is defined in order to transform the generalized flux importance transport equations into the integro-differential equations for the generalized adjoints. Next we develop the adjoint and generalized adjoint transport solution algorithms based on the method of cyclic characteristics (MOCC) in DRAGON code. In the MOCC method, the adjoint characteristics equations associated with a cyclic tracking line are formulated in such a way that a closed form for the adjoint angular function can be obtained. The MOCC method then requires only one cycle of scanning over the cyclic tracking lines in each spatial iteration. We also show that the source importance function by CP method is mathematically equivalent to the adjoint function by MOCC method. In order to speed up the MOCC solution algorithm, a group-reduction and group-splitting techniques based on the structure of the adjoint scattering matrix are implemented. A combined forward flux/adjoint function iteration scheme, based on the group-splitting technique and the common use of a large number of variables storing tracking-line data and exponential values, is proposed to reduce the computing time when both direct and adjoint solutions are required. A problem that arises for the generalized adjoint problem is that the direct use of the negative external generalized adjoint sources in the adjoint solution algorithm results in negative generalized adjoint functions. A coupled flux biasing/decontamination scheme is applied to make the generalized adjoint functions positive using the adjoint functions in such a way that it can be used for the multigroup rebalance technique. Next we consider the application of the perturbation theory to the reactor problems. Since the coolant void reactivity (CVR) is a important factor in reactor safety analysis, we have decided to select this parameter for optimization studies. We consider the optimization and adjoint sensitivity techniques for the adjustments of CVR at beginning of burnup cycle (BOC) and k eff at end of burnup cycle (EOC) for a 2D Advanced CANDU Reactor (ACR) lattice. The sensitivity coefficients are evaluated using the perturbation theory based on the integral transport equations. Three sets of parameters for CVR-BOC and keff-EOC adjustments are studied: (1) Dysprosium density in the central pin with Uranium enrichment in the outer fuel rings, (2) Dysprosium density and Uranium enrichment both in the central pin, and (3) the same parameters as in the first case but the objective is to obtain a negative checkerboard CVR at beginning of cycle (CBCVR-BOC). To approximate the sensitivity coefficient at EOC, we perform constant-power burnup/depletion calculations for 600 full power days (FPD) using a slightly perturbed nuclear library and the unperturbed neutron fluxes to estimate the variation of nuclide densities at EOC. Sensitivity analyses of CVR and eigenvalue are included in the study. In addition the optimization and adjoint sensitivity techniques are applied to the CBCVR-BOC and keff-EOC adjustment of the ACR lattices with Gadolinium in the central pin. Finally we apply these techniques to the CVR-BOC, CVR-EOC and keff-EOC adjustment of a CANDU lattice of which the burnup period is extended from 300 to 450 FPDs. The cases with the central pin containing either Dysprosium or Gadolinium in the natural Uranium are considered in our study. (Abstract shortened by UMI.)

CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data.

PubMed

duVerle, David A; Yotsukura, Sohiya; Nomura, Seitaro; Aburatani, Hiroyuki; Tsuda, Koji

2016-09-13

Single-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths. Here we introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. With cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/ .

Diagnostic layer integration in FPGA-based pipeline measurement systems for HEP experiments

NASA Astrophysics Data System (ADS)

Pozniak, Krzysztof T.

2007-08-01

Integrated triggering and data acquisition systems for high energy physics experiments may be considered as fast, multichannel, synchronous, distributed, pipeline measurement systems. A considerable extension of functional, technological and monitoring demands, which has recently been imposed on them, forced a common usage of large field-programmable gate array (FPGA), digital signal processing-enhanced matrices and fast optical transmission for their realization. This paper discusses modelling, design, realization and testing of pipeline measurement systems. A distribution of synchronous data stream flows is considered in the network. A general functional structure of a single network node is presented. A suggested, novel block structure of the node model facilitates full implementation in the FPGA chip, circuit standardization and parametrization, as well as integration of functional and diagnostic layers. A general method for pipeline system design was derived. This method is based on a unified model of the synchronous data network node. A few examples of practically realized, FPGA-based, pipeline measurement systems were presented. The described systems were applied in ZEUS and CMS.

Support-vector-based emergent self-organising approach for emotional understanding

NASA Astrophysics Data System (ADS)

Nguwi, Yok-Yen; Cho, Siu-Yeung

2010-12-01

This study discusses the computational analysis of general emotion understanding from questionnaires methodology. The questionnaires method approaches the subject by investigating the real experience that accompanied the emotions, whereas the other laboratory approaches are generally associated with exaggerated elements. We adopted a connectionist model called support-vector-based emergent self-organising map (SVESOM) to analyse the emotion profiling from the questionnaires method. The SVESOM first identifies the important variables by giving discriminative features with high ranking. The classifier then performs the classification based on the selected features. Experimental results show that the top rank features are in line with the work of Scherer and Wallbott [(1994), 'Evidence for Universality and Cultural Variation of Differential Emotion Response Patterning', Journal of Personality and Social Psychology, 66, 310-328], which approached the emotions physiologically. While the performance measures show that using the full features for classifications can degrade the performance, the selected features provide superior results in terms of accuracy and generalisation.

Promoting a structural view of biology for varied audiences: an overview of RCSB PDB resources and experiences

PubMed Central

Dutta, Shuchismita; Zardecki, Christine; Goodsell, David S.; Berman, Helen M.

2010-01-01

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) supports scientific research and education worldwide by providing an essential resource of information on biomolecular structures. In addition to serving as a deposition, data-processing and distribution center for PDB data, the RCSB PDB offers resources and online materials that different audiences can use to customize their structural biology instruction. These include resources for general audiences that present macromolecular structure in the context of a biological theme, method-based materials for researchers who take a more traditional approach to the presentation of structural science, and materials that mix theme-based and method-based approaches for educators and students. Through these efforts the RCSB PDB aims to enable optimal use of structural data by researchers, educators and students designing and understanding experiments in biology, chemistry and medicine, and by general users making informed decisions about their life and health. PMID:20877496

Teaching Psychosomatic Medicine Using Problem-Based Learning and Role-Playing

ERIC Educational Resources Information Center

Heru, Alison M.

2011-01-01

Objective: Problem-based learning (PBL) has been implemented in medical education world-wide. Despite its popularity, it has not been generally considered useful for residency programs. The author presents a model for the implementation of PBL in residency programs. Method: The author presents a description of a PBL curriculum for teaching…

Economics: A Discriminant Analysis of Students' Perceptions of Web-Based Learning.

ERIC Educational Resources Information Center

Usip, Ebenge E.; Bee, Richard H.

1998-01-01

Users and nonusers of Web-based instruction (WBI) in an undergraduate statistics classes at Youngstown State University were surveyed. Users concluded that distance learning via the Web was a good method of obtaining general information and useful tool in improving their academic performance. Nonusers thought the university should provide…

A Comparison of Web-Based and Paper-and-Pencil Homework on Student Performance in College Algebra

ERIC Educational Resources Information Center

Hauk, Shandy; Powers, Robert A.; Segalla, Angelo

2015-01-01

College algebra fulfills general education requirements at many colleges in the United States. The study reported here investigated differences in mathematics achievement between undergraduates in college algebra classes using one of two homework methods: "WeBWorK," an open-source system for web-based homework, or traditional…

The Tax Base And The Tax Bill. Tax Implications of Development: A Workbook.

ERIC Educational Resources Information Center

Brighton, Deb; Northup, Jim

The property tax base in Vermont's towns are overburdened as property taxes are usually the only funding method available to finance schools, police departments, highway work, recreation programs, and government in general. Attempting to offer their citizens a balanced program of services without exorbitant taxes, local officials are striving to…

General Constraints on Sampling Wildlife on FIA Plots

Treesearch

Larissa L. Bailey; John R. Sauer; James D. Nichols; Paul H. Geissler

2005-01-01

This paper reviews the constraints to sampling wildlife populations at FIA points. Wildlife sampling programs must have well-defined goals and provide information adequate to meet those goals. Investigators should choose a State variable based on information needs and the spatial sampling scale. We discuss estimation-based methods for three State variables: species...

The Applicability of the Generalized Method of Cells for Analyzing Discontinuously Reinforced Composites

NASA Technical Reports Server (NTRS)

Pahr, D. H.; Arnold, S. M.

2001-01-01

The paper begins with a short overview of the recent work done in the field of discontinuous reinforced composites, focusing on the different parameters which influence the material behavior of discontinuous reinforced composites, as well as the various analysis approaches undertaken. Based on this overview it became evident, that in order to investigate the enumerated effects in an efficient and comprehensive manner, an alternative approach to the computationally intensive finite-element based micromechanics approach is required. Therefore, an investigation is conducted to demonstrate the utility of utilizing the generalized method of cells (GMC), a semi-analytical micromechanics-based approach, to simulate the elastic and elastoplastic material behavior of aligned short fiber composites. The results are compared with (1) simulations using other micromechanical based mean field models and finite element (FE) unit cell models found in the literature given elastic material behavior, as well as (2) finite element unit cell and a new semianalytical elastoplastic shear lag model in the inelastic range. GMC is shown to definitely have a window of applicability when simulating discontinuously reinforced composite material behavior.

The Applicability of the Generalized Method of Cells for Analyzing Discontinuously Reinforced Composites

NASA Technical Reports Server (NTRS)

Pahr, D. H.; Arnold, S. M.

2001-01-01

The paper begins with a short overview of the recent work done in the field of discontinuous reinforced composites, focusing on the different parameters which influence the material behavior of discontinuous reinforced composites, as well as the various analysis approaches undertaken. Based on this overview it became evident that in order to investigate the enumerated effects in an efficient and comprehensive manner, an alternative approach to the computationally intensive finite-element based micromechanics approach is required. Therefore, an investigation is conducted to demonstrate the utility of utilizing the generalized method of cells (GMC), a semi-analytical micromechanics-based approach, to simulate the elastic and elastoplastic material behavior of aligned short fiber composites. The results are compared with simulations using other micromechanical based mean field models and finite element (FE) unit cell models found in the literature given elastic material behavior, as well as finite element unit cell and a new semianalytical elastoplastic shear lag model in the inelastic range. GMC is shown to definitely have a window of applicability when simulating discontinuously reinforced composite material behavior.

A general framework of noise suppression in material decomposition for dual-energy CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petrongolo, Michael; Dong, Xue; Zhu, Lei, E-mail: leizhu@gatech.edu

Purpose: As a general problem of dual-energy CT (DECT), noise amplification in material decomposition severely reduces the signal-to-noise ratio on the decomposed images compared to that on the original CT images. In this work, the authors propose a general framework of noise suppression in material decomposition for DECT. The method is based on an iterative algorithm recently developed in their group for image-domain decomposition of DECT, with an extension to include nonlinear decomposition models. The generalized framework of iterative DECT decomposition enables beam-hardening correction with simultaneous noise suppression, which improves the clinical benefits of DECT. Methods: The authors propose tomore » suppress noise on the decomposed images of DECT using convex optimization, which is formulated in the form of least-squares estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance–covariance matrix of the decomposed images as the penalty weight in the least-squares term. Analytical formulas are derived to compute the variance–covariance matrix for decomposed images with general-form numerical or analytical decomposition. As a demonstration, the authors implement the proposed algorithm on phantom data using an empirical polynomial function of decomposition measured on a calibration scan. The polynomial coefficients are determined from the projection data acquired on a wedge phantom, and the signal decomposition is performed in the projection domain. Results: On the Catphan{sup ®}600 phantom, the proposed noise suppression method reduces the average noise standard deviation of basis material images by one to two orders of magnitude, with a superior performance on spatial resolution as shown in comparisons of line-pair images and modulation transfer function measurements. On the synthesized monoenergetic CT images, the noise standard deviation is reduced by a factor of 2–3. By using nonlinear decomposition on projections, the authors’ method effectively suppresses the streaking artifacts of beam hardening and obtains more uniform images than their previous approach based on a linear model. Similar performance of noise suppression is observed in the results of an anthropomorphic head phantom and a pediatric chest phantom generated by the proposed method. With beam-hardening correction enabled by their approach, the image spatial nonuniformity on the head phantom is reduced from around 10% on the original CT images to 4.9% on the synthesized monoenergetic CT image. On the pediatric chest phantom, their method suppresses image noise standard deviation by a factor of around 7.5, and compared with linear decomposition, it reduces the estimation error of electron densities from 33.3% to 8.6%. Conclusions: The authors propose a general framework of noise suppression in material decomposition for DECT. Phantom studies have shown the proposed method improves the image uniformity and the accuracy of electron density measurements by effective beam-hardening correction and reduces noise level without noticeable resolution loss.« less

Effect of the Jigsaw-Based Cooperative Learning Method on Student Performance in the General Certificate of Education Advanced-Level Psychology: An Exploratory Brunei Case Study

ERIC Educational Resources Information Center

Azmin, Nur Hafizah

2016-01-01

The mixed-methods study investigated the effect of the jigsaw cooperative learning method on student performance in psychology and their views towards it. Experimental data were obtained via pre-and-post tests and an open-ended questionnaire from 16 conveniently selected students at one Sixth Form College in Brunei. Moreover, the participants…

Algorithms for Solvents and Spectral Factors of Matrix Polynomials

DTIC Science & Technology

1981-01-01

spectral factors of matrix polynomials LEANG S. SHIEHt, YIH T. TSAYt and NORMAN P. COLEMANt A generalized Newton method , based on the contracted gradient...of a matrix poly- nomial, is derived for solving the right (left) solvents and spectral factors of matrix polynomials. Two methods of selecting initial...estimates for rapid convergence of the newly developed numerical method are proposed. Also, new algorithms for solving complete sets of the right

Selecting a restoration technique to minimize OCR error.

PubMed

Cannon, M; Fugate, M; Hush, D R; Scovel, C

2003-01-01

This paper introduces a learning problem related to the task of converting printed documents to ASCII text files. The goal of the learning procedure is to produce a function that maps documents to restoration techniques in such a way that on average the restored documents have minimum optical character recognition error. We derive a general form for the optimal function and use it to motivate the development of a nonparametric method based on nearest neighbors. We also develop a direct method of solution based on empirical error minimization for which we prove a finite sample bound on estimation error that is independent of distribution. We show that this empirical error minimization problem is an extension of the empirical optimization problem for traditional M-class classification with general loss function and prove computational hardness for this problem. We then derive a simple iterative algorithm called generalized multiclass ratchet (GMR) and prove that it produces an optimal function asymptotically (with probability 1). To obtain the GMR algorithm we introduce a new data map that extends Kesler's construction for the multiclass problem and then apply an algorithm called Ratchet to this mapped data, where Ratchet is a modification of the Pocket algorithm . Finally, we apply these methods to a collection of documents and report on the experimental results.

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

Online learning control using adaptive critic designs with sparse kernel machines.

PubMed

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

Model-based economic evaluation in Alzheimer's disease: a review of the methods available to model Alzheimer's disease progression.

PubMed

Green, Colin; Shearer, James; Ritchie, Craig W; Zajicek, John P

2011-01-01

To consider the methods available to model Alzheimer's disease (AD) progression over time to inform on the structure and development of model-based evaluations, and the future direction of modelling methods in AD. A systematic search of the health care literature was undertaken to identify methods to model disease progression in AD. Modelling methods are presented in a descriptive review. The literature search identified 42 studies presenting methods or applications of methods to model AD progression over time. The review identified 10 general modelling frameworks available to empirically model the progression of AD as part of a model-based evaluation. Seven of these general models are statistical models predicting progression of AD using a measure of cognitive function. The main concerns with models are on model structure, around the limited characterization of disease progression, and on the use of a limited number of health states to capture events related to disease progression over time. None of the available models have been able to present a comprehensive model of the natural history of AD. Although helpful, there are serious limitations in the methods available to model progression of AD over time. Advances are needed to better model the progression of AD and the effects of the disease on peoples' lives. Recent evidence supports the need for a multivariable approach to the modelling of AD progression, and indicates that a latent variable analytic approach to characterising AD progression is a promising avenue for advances in the statistical development of modelling methods. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

PubMed

Liu, Wensong; Yang, Jie; Zhao, Jinqi; Shi, Hongtao; Yang, Le

2018-02-12

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by R j test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

Similarity based false-positive reduction for breast cancer using radiographic and pathologic imaging features

NASA Astrophysics Data System (ADS)

Pai, Akshay; Samala, Ravi K.; Zhang, Jianying; Qian, Wei

2010-03-01

Mammography reading by radiologists and breast tissue image interpretation by pathologists often leads to high False Positive (FP) Rates. Similarly, current Computer Aided Diagnosis (CADx) methods tend to concentrate more on sensitivity, thus increasing the FP rates. A novel method is introduced here which employs similarity based method to decrease the FP rate in the diagnosis of microcalcifications. This method employs the Principal Component Analysis (PCA) and the similarity metrics in order to achieve the proposed goal. The training and testing set is divided into generalized (Normal and Abnormal) and more specific (Abnormal, Normal, Benign) classes. The performance of this method as a standalone classification system is evaluated in both the cases (general and specific). In another approach the probability of each case belonging to a particular class is calculated. If the probabilities are too close to classify, the augmented CADx system can be instructed to have a detailed analysis of such cases. In case of normal cases with high probability, no further processing is necessary, thus reducing the computation time. Hence, this novel method can be employed in cascade with CADx to reduce the FP rate and also avoid unnecessary computational time. Using this methodology, a false positive rate of 8% and 11% is achieved for mammography and cellular images respectively.

Artificial intelligence in radiology.

PubMed

Hosny, Ahmed; Parmar, Chintan; Quackenbush, John; Schwartz, Lawrence H; Aerts, Hugo J W L

2018-05-17

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

Domain decomposition methods for systems of conservation laws: Spectral collocation approximations

NASA Technical Reports Server (NTRS)

Quarteroni, Alfio

1989-01-01

Hyperbolic systems of conversation laws are considered which are discretized in space by spectral collocation methods and advanced in time by finite difference schemes. At any time-level a domain deposition method based on an iteration by subdomain procedure was introduced yielding at each step a sequence of independent subproblems (one for each subdomain) that can be solved simultaneously. The method is set for a general nonlinear problem in several space variables. The convergence analysis, however, is carried out only for a linear one-dimensional system with continuous solutions. A precise form of the error reduction factor at each iteration is derived. Although the method is applied here to the case of spectral collocation approximation only, the idea is fairly general and can be used in a different context as well. For instance, its application to space discretization by finite differences is straight forward.

Reply to comment by Fred L. Ogden et al. on "Beyond the SCS-CN method: A theoretical framework for spatially lumped rainfall-runoff response"

NASA Astrophysics Data System (ADS)

Bartlett, M. S.; Parolari, A. J.; McDonnell, J. J.; Porporato, A.

2017-07-01

Though Ogden et al. list several shortcomings of the original SCS-CN method, fit for purpose is a key consideration in hydrological modelling, as shown by the adoption of SCS-CN method in many design standards. The theoretical framework of Bartlett et al. [2016a] reveals a family of semidistributed models, of which the SCS-CN method is just one member. Other members include event-based versions of the Variable Infiltration Capacity (VIC) model and TOPMODEL. This general model allows us to move beyond the limitations of the original SCS-CN method under different rainfall-runoff mechanisms and distributions for soil and rainfall variability. Future research should link this general model approach to different hydrogeographic settings, in line with the call for action proposed by Ogden et al.

General field and office procedures for indirect discharge measurements

USGS Publications Warehouse

Benson, M.A.; Dalrymple, Tate

2001-04-01

The discharge of streams is usually measured by the current-meter method. During flood periods, however, it is frequently impossible or impractical to measure the discharges by this method when they occur. Consequently, many peak discharges must be determined after the passage of the flood by indirect methods, such as slope-area, contracted-opening, flow-over-dam, and flow-through-culvert, rather than by direct current-meter measurement. Indirect methods of determining peak discharge are based on hydraulic equations which relate the discharge to the water-surface profile and the geometry of the channel. A field survey is made after the flood to determine the location and elevation of high-water marks and the characteristics of the channel. Detailed descriptions of the general procedures used in collecting the field data and in computing the discharge are given in this report. Each of the methods requires special procedures described in subsequent chapters.

Diagonally Implicit Runge-Kutta Methods for Ordinary Differential Equations. A Review

NASA Technical Reports Server (NTRS)

Kennedy, Christopher A.; Carpenter, Mark H.

2016-01-01

A review of diagonally implicit Runge-Kutta (DIRK) methods applied to rst-order ordinary di erential equations (ODEs) is undertaken. The goal of this review is to summarize the characteristics, assess the potential, and then design several nearly optimal, general purpose, DIRK-type methods. Over 20 important aspects of DIRKtype methods are reviewed. A design study is then conducted on DIRK-type methods having from two to seven implicit stages. From this, 15 schemes are selected for general purpose application. Testing of the 15 chosen methods is done on three singular perturbation problems. Based on the review of method characteristics, these methods focus on having a stage order of two, sti accuracy, L-stability, high quality embedded and dense-output methods, small magnitudes of the algebraic stability matrix eigenvalues, small values of aii, and small or vanishing values of the internal stability function for large eigenvalues of the Jacobian. Among the 15 new methods, ESDIRK4(3)6L[2]SA is recommended as a good default method for solving sti problems at moderate error tolerances.

Image encryption using random sequence generated from generalized information domain

NASA Astrophysics Data System (ADS)

Xia-Yan, Zhang; Guo-Ji, Zhang; Xuan, Li; Ya-Zhou, Ren; Jie-Hua, Wu

2016-05-01

A novel image encryption method based on the random sequence generated from the generalized information domain and permutation-diffusion architecture is proposed. The random sequence is generated by reconstruction from the generalized information file and discrete trajectory extraction from the data stream. The trajectory address sequence is used to generate a P-box to shuffle the plain image while random sequences are treated as keystreams. A new factor called drift factor is employed to accelerate and enhance the performance of the random sequence generator. An initial value is introduced to make the encryption method an approximately one-time pad. Experimental results show that the random sequences pass the NIST statistical test with a high ratio and extensive analysis demonstrates that the new encryption scheme has superior security.

Alternative strategy for converting an inverting glycoside hydrolase into a glycosynthase.

PubMed

Honda, Yuji; Fushinobu, Shinya; Hidaka, Masafumi; Wakagi, Takayoshi; Shoun, Hirofumi; Taniguchi, Hajime; Kitaoka, Motomitsu

2008-04-01

The tyrosine residue Y198 is known to support a nucleophilic water molecule with the general base residue, D263, in the reducing-end xylose-releasing exo-oligoxylanase (Rex). A mutation in the tyrosine residue changing it into phenylalanine caused a drastic decrease in the hydrolytic activity and a small increase in the F(-) releasing activity from alpha-xylobiosyl fluoride in the presence of xylose. In contrast, mutations at D263 resulted in the decreased F(-) releasing activity. As a result of the high F(-) releasing activity and low hydrolytic activity, Y198F of Rex accumulates a large amount of product during the glycosynthase reaction. We propose a novel method for producing a glycosynthase from an inverting glycoside hydrolase by mutating a residue that holds the nucleophilic water molecule with the general base residue while keeping the general base residue intact.

Effective domain-dependent reuse in medical knowledge bases.

PubMed

Dojat, M; Pachet, F

1995-12-01

Knowledge reuse is now a critical issue for most developers of medical knowledge-based systems. As a rule, reuse is addressed from an ambitious, knowledge-engineering perspective that focuses on reusable general purpose knowledge modules, concepts, and methods. However, such a general goal fails to take into account the specific aspects of medical practice. From the point of view of the knowledge engineer, whose goal is to capture the specific features and intricacies of a given domain, this approach addresses the wrong level of generality. In this paper, we adopt a more pragmatic viewpoint, introducing the less ambitious goal of "domain-dependent limited reuse" and suggesting effective means of achieving it in practice. In a knowledge representation framework combining objects and production rules, we propose three mechanisms emerging from the combination of object-oriented programming and rule-based programming. We show these mechanisms contribute to achieve limited reuse and to introduce useful limited variations in medical expertise.

An overview of very high level software design methods

NASA Technical Reports Server (NTRS)

Asdjodi, Maryam; Hooper, James W.

1988-01-01

Very High Level design methods emphasize automatic transfer of requirements to formal design specifications, and/or may concentrate on automatic transformation of formal design specifications that include some semantic information of the system into machine executable form. Very high level design methods range from general domain independent methods to approaches implementable for specific applications or domains. Applying AI techniques, abstract programming methods, domain heuristics, software engineering tools, library-based programming and other methods different approaches for higher level software design are being developed. Though one finds that a given approach does not always fall exactly in any specific class, this paper provides a classification for very high level design methods including examples for each class. These methods are analyzed and compared based on their basic approaches, strengths and feasibility for future expansion toward automatic development of software systems.

Model of load distribution for earth observation satellite

NASA Astrophysics Data System (ADS)

Tu, Shumin; Du, Min; Li, Wei

2017-03-01

For the system of multiple types of EOS (Earth Observing Satellites), it is a vital issue to assure that each type of payloads carried by the group of EOS can be used efficiently and reasonably for in astronautics fields. Currently, most of researches on configuration of satellite and payloads focus on the scheduling for launched satellites. However, the assignments of payloads for un-launched satellites are bit researched, which are the same crucial as the scheduling of tasks. Moreover, the current models of satellite resources scheduling lack of more general characteristics. Referring the idea about roles-based access control (RBAC) of information system, this paper brings forward a model based on role-mining of RBAC to improve the generality and foresight of the method of assignments of satellite-payload. By this way, the assignment of satellite-payload can be mapped onto the problem of role-mining. A novel method will be introduced, based on the idea of biclique-combination in graph theory and evolutionary algorithm in intelligence computing, to address the role-mining problem of satellite-payload assignments. The simulation experiments are performed to verify the novel method. Finally, the work of this paper is concluded.

Element analysis: a wavelet-based method for analysing time-localized events in noisy time series

PubMed Central

2017-01-01

A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized ‘events’. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate. Finally, an expression for an event’s ‘region of influence’ within the wavelet transform permits the formation of a criterion for rejecting spurious maxima due to numerical artefacts or other unsuitable events. Signals can then be reconstructed based on a small number of isolated points on the time/scale plane. This method, termed element analysis, is applied to the identification of long-lived eddy structures in ocean currents as observed by along-track measurements of sea surface elevation from satellite altimetry. PMID:28484325

Accurate finite difference methods for time-harmonic wave propagation

NASA Technical Reports Server (NTRS)

Harari, Isaac; Turkel, Eli

1994-01-01

Finite difference methods for solving problems of time-harmonic acoustics are developed and analyzed. Multidimensional inhomogeneous problems with variable, possibly discontinuous, coefficients are considered, accounting for the effects of employing nonuniform grids. A weighted-average representation is less sensitive to transition in wave resolution (due to variable wave numbers or nonuniform grids) than the standard pointwise representation. Further enhancement in method performance is obtained by basing the stencils on generalizations of Pade approximation, or generalized definitions of the derivative, reducing spurious dispersion, anisotropy and reflection, and by improving the representation of source terms. The resulting schemes have fourth-order accurate local truncation error on uniform grids and third order in the nonuniform case. Guidelines for discretization pertaining to grid orientation and resolution are presented.

Time-lapse joint AVO inversion using generalized linear method based on exact Zoeppritz equations

NASA Astrophysics Data System (ADS)

Zhi, Longxiao; Gu, Hanming

2018-03-01

The conventional method of time-lapse AVO (Amplitude Versus Offset) inversion is mainly based on the approximate expression of Zoeppritz equations. Though the approximate expression is concise and convenient to use, it has certain limitations. For example, its application condition is that the difference of elastic parameters between the upper medium and lower medium is little and the incident angle is small. In addition, the inversion of density is not stable. Therefore, we develop the method of time-lapse joint AVO inversion based on exact Zoeppritz equations. In this method, we apply exact Zoeppritz equations to calculate the reflection coefficient of PP wave. And in the construction of objective function for inversion, we use Taylor series expansion to linearize the inversion problem. Through the joint AVO inversion of seismic data in baseline survey and monitor survey, we can obtain the P-wave velocity, S-wave velocity, density in baseline survey and their time-lapse changes simultaneously. We can also estimate the oil saturation change according to inversion results. Compared with the time-lapse difference inversion, the joint inversion doesn't need certain assumptions and can estimate more parameters simultaneously. It has a better applicability. Meanwhile, by using the generalized linear method, the inversion is easily implemented and its calculation cost is small. We use the theoretical model to generate synthetic seismic records to test and analyze the influence of random noise. The results can prove the availability and anti-noise-interference ability of our method. We also apply the inversion to actual field data and prove the feasibility of our method in actual situation.

General MoM Solutions for Large Arrays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fasenfest, B; Capolino, F; Wilton, D R

2003-07-22

This paper focuses on a numerical procedure that addresses the difficulties of dealing with large, finite arrays while preserving the generality and robustness of full-wave methods. We present a fast method based on approximating interactions between sufficiently separated array elements via a relatively coarse interpolation of the Green's function on a uniform grid commensurate with the array's periodicity. The interaction between the basis and testing functions is reduced to a three-stage process. The first stage is a projection of standard (e.g., RWG) subdomain bases onto a set of interpolation functions that interpolate the Green's function on the array face. Thismore » projection, which is used in a matrix/vector product for each array cell in an iterative solution process, need only be carried out once for a single cell and results in a low-rank matrix. An intermediate stage matrix/vector product computation involving the uniformly sampled Green's function is of convolutional form in the lateral (transverse) directions so that a 2D FFT may be used. The final stage is a third matrix/vector product computation involving a matrix resulting from projecting testing functions onto the Green's function interpolation functions; the low-rank matrix is either identical to (using Galerkin's method) or similar to that for the bases projection. An effective MoM solution scheme is developed for large arrays using a modification of the AIM (Adaptive Integral Method) method. The method permits the analysis of arrays with arbitrary contours and nonplanar elements. Both fill and solve times within the MoM method are improved with respect to more standard MoM solvers.« less

Efficiency of reactant site sampling in network-free simulation of rule-based models for biochemical systems

PubMed Central

Yang, Jin; Hlavacek, William S.

2011-01-01

Rule-based models, which are typically formulated to represent cell signaling systems, can now be simulated via various network-free simulation methods. In a network-free method, reaction rates are calculated for rules that characterize molecular interactions, and these rule rates, which each correspond to the cumulative rate of all reactions implied by a rule, are used to perform a stochastic simulation of reaction kinetics. Network-free methods, which can be viewed as generalizations of Gillespie’s method, are so named because these methods do not require that a list of individual reactions implied by a set of rules be explicitly generated, which is a requirement of other methods for simulating rule-based models. This requirement is impractical for rule sets that imply large reaction networks (i.e., long lists of individual reactions), as reaction network generation is expensive. Here, we compare the network-free simulation methods implemented in RuleMonkey and NFsim, general-purpose software tools for simulating rule-based models encoded in the BioNetGen language. The method implemented in NFsim uses rejection sampling to correct overestimates of rule rates, which introduces null events (i.e., time steps that do not change the state of the system being simulated). The method implemented in RuleMonkey uses iterative updates to track rule rates exactly, which avoids null events. To ensure a fair comparison of the two methods, we developed implementations of the rejection and rejection-free methods specific to a particular class of kinetic models for multivalent ligand-receptor interactions. These implementations were written with the intention of making them as much alike as possible, minimizing the contribution of irrelevant coding differences to efficiency differences. Simulation results show that performance of the rejection method is equal to or better than that of the rejection-free method over wide parameter ranges. However, when parameter values are such that ligand-induced aggregation of receptors yields a large connected receptor cluster, the rejection-free method is more efficient. PMID:21832806

Stats on the Cheap: Using Free and Inexpensive Internet Resources to Enhance the Teaching of Statistics and Research Methods

ERIC Educational Resources Information Center

Hartnett, Jessica L.

2013-01-01

The present article describes four free or inexpensive Internet-based activities that can be used to supplement statistics/research methods/general psychology classes. Each activity and subsequent homework assessment is described, as well as homework performance outcome and student opinion data for each activity. (Contains 1 table.)

Effects of an Interteaching Probe on Learning and Generalization of American Psychological Association (APA) Style

ERIC Educational Resources Information Center

Slezak, Jonathan M.; Faas, Caitlin

2017-01-01

This study implemented the components of interteaching as a probe to teach American Psychological Association (APA) Style to undergraduate university students in a psychology research methods and statistics course. The interteaching method was compared to the traditional lecture-based approach between two sections of the course with the same…

A New Approach for Laboratory Exercise of Pathophysiology in China Based on Student-Centered Learning

ERIC Educational Resources Information Center

Chen, Jian; Zhou, Junhai; Sun, Li; Wu, Qiuhui; Lu, Huiling; Tian, Jing

2015-01-01

Student-centered learning is generally defined as any instructional method that purportedly engages students in active learning and critical thinking. The student-centered method of teaching moves the focus from teaching to learning, from the teachers' conveying course concepts via lecture to the understanding of concepts by students. The…

Influences of sample interference and interference controls on quantification of enterococci fecal indicator bacteria in surface water samples by the qPCR method

EPA Science Inventory

A quantitative polymerase chain reaction (qPCR) method for the detection of entercocci fecal indicator bacteria has been shown to be generally applicable for the analysis of temperate fresh (Great Lakes) and marine coastal waters and for providing risk-based determinations of wat...

Fire Behavior (FB)

Treesearch

Robert E. Keane

2006-01-01

The Fire Behavior (FB) method is used to describe the behavior of the fire and the ambient weather and fuel conditions that influence the fire behavior. Fire behavior methods are not plot based and are collected by fire event and time-date. In general, the fire behavior data are used to interpret the fire effects documented in the plot-level sampling. Unlike the other...

Robust current control-based generalized predictive control with sliding mode disturbance compensation for PMSM drives.

PubMed

Liu, Xudong; Zhang, Chenghui; Li, Ke; Zhang, Qi

2017-11-01

This paper addresses the current control of permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and disturbances. A generalized predictive current control method combined with sliding mode disturbance compensation is proposed to satisfy the requirement of fast response and strong robustness. Firstly, according to the generalized predictive control (GPC) theory based on the continuous time model, a predictive current control method is presented without considering the disturbance, which is convenient to be realized in the digital controller. In fact, it's difficult to derive the exact motor model and parameters in the practical system. Thus, a sliding mode disturbance compensation controller is studied to improve the adaptiveness and robustness of the control system. The designed controller attempts to combine the merits of both predictive control and sliding mode control, meanwhile, the controller parameters are easy to be adjusted. Lastly, the proposed controller is tested on an interior PMSM by simulation and experiment, and the results indicate that it has good performance in both current tracking and disturbance rejection. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A comparison of the finite difference and finite element methods for heat transfer calculations

NASA Technical Reports Server (NTRS)

Emery, A. F.; Mortazavi, H. R.

1982-01-01

The finite difference method and finite element method for heat transfer calculations are compared by describing their bases and their application to some common heat transfer problems. In general it is noted that neither method is clearly superior, and in many instances, the choice is quite arbitrary and depends more upon the codes available and upon the personal preference of the analyst than upon any well defined advantages of one method. Classes of problems for which one method or the other is better suited are defined.

Macromolecule mapping of the brain using ultrashort-TE acquisition and reference-based metabolite removal.

PubMed

Lam, Fan; Li, Yudu; Clifford, Bryan; Liang, Zhi-Pei

2018-05-01

To develop a practical method for mapping macromolecule distribution in the brain using ultrashort-TE MRSI data. An FID-based chemical shift imaging acquisition without metabolite-nulling pulses was used to acquire ultrashort-TE MRSI data that capture the macromolecule signals with high signal-to-noise-ratio (SNR) efficiency. To remove the metabolite signals from the ultrashort-TE data, single voxel spectroscopy data were obtained to determine a set of high-quality metabolite reference spectra. These spectra were then incorporated into a generalized series (GS) model to represent general metabolite spatiospectral distributions. A time-segmented algorithm was developed to back-extrapolate the GS model-based metabolite distribution from truncated FIDs and remove it from the MRSI data. Numerical simulations and in vivo experiments have been performed to evaluate the proposed method. Simulation results demonstrate accurate metabolite signal extrapolation by the proposed method given a high-quality reference. For in vivo experiments, the proposed method is able to produce spatiospectral distributions of macromolecules in the brain with high SNR from data acquired in about 10 minutes. We further demonstrate that the high-dimensional macromolecule spatiospectral distribution resides in a low-dimensional subspace. This finding provides a new opportunity to use subspace models for quantification and accelerated macromolecule mapping. Robustness of the proposed method is also demonstrated using multiple data sets from the same and different subjects. The proposed method is able to obtain macromolecule distributions in the brain from ultrashort-TE acquisitions. It can also be used for acquiring training data to determine a low-dimensional subspace to represent the macromolecule signals for subspace-based MRSI. Magn Reson Med 79:2460-2469, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

NASA Astrophysics Data System (ADS)

Zhou, Xiran; Liu, Jun; Liu, Shuguang; Cao, Lei; Zhou, Qiming; Huang, Huawen

2014-02-01

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity-hue-saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.