An Improved Cluster Richness Estimator
Rozo, Eduardo; Rykoff, Eli S.; Koester, Benjamin P.; McKay, Timothy; Hao, Jiangang; Evrard, August; Wechsler, Risa H.; Hansen, Sarah; Sheldon, Erin; Johnston, David; Becker, Matthew R.; Annis, James T.; Bleem, Lindsey; Scranton, Ryan; /Pittsburgh U.
2009-08-03
Minimizing the scatter between cluster mass and accessible observables is an important goal for cluster cosmology. In this work, we introduce a new matched filter richness estimator, and test its performance using the maxBCG cluster catalog. Our new estimator significantly reduces the variance in the L{sub X}-richness relation, from {sigma}{sub lnL{sub X}}{sup 2} = (0.86 {+-} 0.02){sup 2} to {sigma}{sub lnL{sub X}}{sup 2} = (0.69 {+-} 0.02){sup 2}. Relative to the maxBCG richness estimate, it also removes the strong redshift dependence of the richness scaling relations, and is significantly more robust to photometric and redshift errors. These improvements are largely due to our more sophisticated treatment of galaxy color data. We also demonstrate the scatter in the L{sub X}-richness relation depends on the aperture used to estimate cluster richness, and introduce a novel approach for optimizing said aperture which can be easily generalized to other mass tracers.
Improving lensing cluster mass estimate with flexion
NASA Astrophysics Data System (ADS)
Cardone, V. F.; Vicinanza, M.; Er, X.; Maoli, R.; Scaramella, R.
2016-11-01
Gravitational lensing has long been considered as a valuable tool to determine the total mass of galaxy clusters. The shear profile, as inferred from the statistics of ellipticity of background galaxies, allows us to probe the cluster intermediate and outer regions, thus determining the virial mass estimate. However, the mass sheet degeneracy and the need for a large number of background galaxies motivate the search for alternative tracers which can break the degeneracy among model parameters and hence improve the accuracy of the mass estimate. Lensing flexion, i.e. the third derivative of the lensing potential, has been suggested as a good answer to the above quest since it probes the details of the mass profile. We investigate here whether this is indeed the case considering jointly using weak lensing, magnification and flexion. We use a Fisher matrix analysis to forecast the relative improvement in the mass accuracy for different assumptions on the shear and flexion signal-to- noise (S/N) ratio also varying the cluster mass, redshift, and ellipticity. It turns out that the error on the cluster mass may be reduced up to a factor of ˜2 for reasonable values of the flexion S/N ratio. As a general result, we get that the improvement in mass accuracy is larger for more flattened haloes, but it extracting general trends is difficult because of the many parameters at play. We nevertheless find that flexion is as efficient as magnification to increase the accuracy in both mass and concentration determination.
NASA Astrophysics Data System (ADS)
Samui, Saumyadip; Samui Pal, Shanoli
2017-02-01
We present an improved photometric redshift estimator code, CuBANz, that is publicly available at https://goo.gl/fpk90V. It uses the back propagation neural network along with clustering of the training set, which makes it more efficient than existing neural network codes. In CuBANz, the training set is divided into several self learning clusters with galaxies having similar photometric properties and spectroscopic redshifts within a given span. The clustering algorithm uses the color information (i.e. u - g , g - r etc.) rather than the apparent magnitudes at various photometric bands as the photometric redshift is more sensitive to the flux differences between different bands rather than the actual values. Separate neural networks are trained for each cluster using all possible colors, magnitudes and uncertainties in the measurements. For a galaxy with unknown redshift, we identify the closest possible clusters having similar photometric properties and use those clusters to get the photometric redshifts using the particular networks that were trained using those cluster members. For galaxies that do not match with any training cluster, the photometric redshifts are obtained from a separate network that uses entire training set. This clustering method enables us to determine the redshifts more accurately. SDSS Stripe 82 catalog has been used here for the demonstration of the code. For the clustered sources with redshift range zspec < 0.7, the residual error (<(zspec -zphot) 2 > 1 / 2) in the training/testing phase is as low as 0.03 compared to the existing ANNz code that provides residual error on the same test data set of 0.05. Further, we provide a much better estimate of the uncertainty of the derived photometric redshift.
2013-01-01
optimization problem (2)–(3) is convex and can 1We adopt the convention that yii = 1 for any node i that belongs to a cluster. 2We assume aii = 1 for all i. 3The...relaxations: The formulation (2)–(3) is not the only way to relax the non - convex ML estimator. Instead of the nuclear norm regularizer, a hard constraint ...presented a convex optimization formulation, essentially a convexification of the maximum likelihood estimator. Our theoretic analysis shows that this
Geist, David R. ); Jones, Julia; Murray, Christopher J. ); Dauble, Dennis D. )
1999-12-01
We improved our predictions of fall chinook salmon (Oncorhynchus tshawytscha) habitat use by analyzing spawning habitat at the spatial scale of redd clusters. Spatial point pattern analyses indicated that redd clusters in the Hanford Reach, Columbia River, were consistent in their location from 1994 to 1995. Redd densities were 16.1 and 8.9 redds?ha-1 in 1994 and 1995, respectively, and individual redds within clusters were usually less than 30 m apart. Pattern analysis also showed strong evidence that redds were uniformly distributed within the clusters where inter-redd distances ranged from 2 to 5 m. Redd clusters were found to occur predominantly where water velocity was between 1.4 to 2 m?s-1, water depth was 2 to 4 m, and lateral slope of the riverbed was less than 4%. This habitat use represented a narrower range of use than previously reported for adult fall chinook salmon. Logistic regression analysis determined that water velocity and lateral slope were the most significant predictors of redd cluster location over a range of river discharges. Over-estimates of available spawning habitat lead to non-achievable goals for protecting and restoring critical salmonid habitat. Better predictions of spawning habitat may be possible if cluster-specific characteristics are used.
A nonparametric clustering technique which estimates the number of clusters
NASA Technical Reports Server (NTRS)
Ramey, D. B.
1983-01-01
In applications of cluster analysis, one usually needs to determine the number of clusters, K, and the assignment of observations to each cluster. A clustering technique based on recursive application of a multivariate test of bimodality which automatically estimates both K and the cluster assignments is presented.
Horney, Jennifer; Zotti, Marianne E.; Williams, Amy; Hsia, Jason
2015-01-01
Introduction and Background Women of reproductive age, in particular women who are pregnant or fewer than 6 months postpartum, are uniquely vulnerable to the effects of natural disasters, which may create stressors for caregivers, limit access to prenatal/postpartum care, or interrupt contraception. Traditional approaches (e.g., newborn records, community surveys) to survey women of reproductive age about unmet needs may not be practical after disasters. Finding pregnant or postpartum women is especially challenging because fewer than 5% of women of reproductive age are pregnant or postpartum at any time. Methods From 2009 to 2011, we conducted three pilots of a sampling strategy that aimed to increase the proportion of pregnant and postpartum women of reproductive age who were included in postdisaster reproductive health assessments in Johnston County, North Carolina, after tornadoes, Cobb/Douglas Counties, Georgia, after flooding, and Bertie County, North Carolina, after hurricane-related flooding. Results Using this method, the percentage of pregnant and postpartum women interviewed in each pilot increased from 0.06% to 21%, 8% to 19%, and 9% to 17%, respectively. Conclusion and Discussion Two-stage cluster sampling with referral can be used to increase the proportion of pregnant and postpartum women included in a postdisaster assessment. This strategy may be a promising way to assess unmet needs of pregnant and postpartum women in disaster-affected communities. PMID:22365134
Attitude Estimation in Fractionated Spacecraft Cluster Systems
NASA Technical Reports Server (NTRS)
Hadaegh, Fred Y.; Blackmore, James C.
2011-01-01
An attitude estimation was examined in fractioned free-flying spacecraft. Instead of a single, monolithic spacecraft, a fractionated free-flying spacecraft uses multiple spacecraft modules. These modules are connected only through wireless communication links and, potentially, wireless power links. The key advantage of this concept is the ability to respond to uncertainty. For example, if a single spacecraft module in the cluster fails, a new one can be launched at a lower cost and risk than would be incurred with onorbit servicing or replacement of the monolithic spacecraft. In order to create such a system, however, it is essential to know what the navigation capabilities of the fractionated system are as a function of the capabilities of the individual modules, and to have an algorithm that can perform estimation of the attitudes and relative positions of the modules with fractionated sensing capabilities. Looking specifically at fractionated attitude estimation with startrackers and optical relative attitude sensors, a set of mathematical tools has been developed that specify the set of sensors necessary to ensure that the attitude of the entire cluster ( cluster attitude ) can be observed. Also developed was a navigation filter that can estimate the cluster attitude if these conditions are satisfied. Each module in the cluster may have either a startracker, a relative attitude sensor, or both. An extended Kalman filter can be used to estimate the attitude of all modules. A range of estimation performances can be achieved depending on the sensors used and the topology of the sensing network.
Tidal radius estimates for three open clusters
NASA Astrophysics Data System (ADS)
Danilov, V. M.; Loktin, A. V.
2015-10-01
A new method is developed for estimating tidal radii and masses of open star clusters (OCL) based on the sky-plane coordinates and proper motions and/or radial velocities of cluster member stars. To this end, we perform the correlation and spectral analysis of oscillations of absolute values of stellar velocity components relative to the cluster mass center along three coordinate planes and along each coordinate axis in five OCL models. Mutual correlation functions for fluctuations of absolute values of velocity field components are computed. The spatial Fourier transform of the mutual correlation functions in the case of zero time offset is used to compute wavenumber spectra of oscillations of absolute values of stellar velocity components. The oscillation spectra of these quantities contain series of local maxima at equidistant wavenumber k values. The ratio of the tidal radius of the cluster to the wavenumber difference Δ k of adjacent local maxima in the oscillation spectra of absolute values of velocity field components is found to be the same for all five OCL models. This ratio is used to estimate the tidal radii and masses of the Pleiades, Praesepe, and M67 based on the proper motions and sky-plane coordinates of the member stars of these clusters. The radial dependences of the absolute values of the tangential and radial projections of cluster star velocities computed using the proper motions relative to the cluster center are determined, along with the corresponding autocorrelation functions and wavenumber spectra of oscillations of absolute values of velocity field components. The Pleiades virial mass is estimated assuming that the cluster is either isolated or non-isolated. Also derived are the estimates of the Pleiades dynamical mass assuming that it is non-stationary and non-isolated. The inferred Pleiades tidal radii corresponding to these masses are reported.
Optimizing weak lensing mass estimates for cluster profile uncertainty
Gruen, D.; Bernstein, G. M.; Lam, T. Y.; Seitz, S.
2011-09-11
Weak lensing measurements of cluster masses are necessary for calibrating mass-observable relations (MORs) to investigate the growth of structure and the properties of dark energy. However, the measured cluster shear signal varies at fixed mass M_{200m }due to inherent ellipticity of background galaxies, intervening structures along the line of sight, and variations in the cluster structure due to scatter in concentrations, asphericity and substructure. We use N-body simulated halos to derive and evaluate a weak lensing circular aperture mass measurement M_{ap} that minimizes the mass estimate variance <(M_{ap} - M_{200m})^{2}> in the presence of all these forms of variability. Depending on halo mass and observational conditions, the resulting mass estimator improves on M_{ap} filters optimized for circular NFW-profile clusters in the presence of uncorrelated large scale structure (LSS) about as much as the latter improve on an estimator that only minimizes the influence of shape noise. Optimizing for uncorrelated LSS while ignoring the variation of internal cluster structure puts too much weight on the profile near the cores of halos, and under some circumstances can even be worse than not accounting for LSS at all. As a result, we discuss the impact of variability in cluster structure and correlated structures on the design and performance of weak lensing surveys intended to calibrate cluster MORs.
Optimizing weak lensing mass estimates for cluster profile uncertainty
Gruen, D.; Bernstein, G. M.; Lam, T. Y.; ...
2011-09-11
Weak lensing measurements of cluster masses are necessary for calibrating mass-observable relations (MORs) to investigate the growth of structure and the properties of dark energy. However, the measured cluster shear signal varies at fixed mass M200m due to inherent ellipticity of background galaxies, intervening structures along the line of sight, and variations in the cluster structure due to scatter in concentrations, asphericity and substructure. We use N-body simulated halos to derive and evaluate a weak lensing circular aperture mass measurement Map that minimizes the mass estimate variance <(Map - M200m)2> in the presence of all these forms of variability. Dependingmore » on halo mass and observational conditions, the resulting mass estimator improves on Map filters optimized for circular NFW-profile clusters in the presence of uncorrelated large scale structure (LSS) about as much as the latter improve on an estimator that only minimizes the influence of shape noise. Optimizing for uncorrelated LSS while ignoring the variation of internal cluster structure puts too much weight on the profile near the cores of halos, and under some circumstances can even be worse than not accounting for LSS at all. As a result, we discuss the impact of variability in cluster structure and correlated structures on the design and performance of weak lensing surveys intended to calibrate cluster MORs.« less
Improvements in Ionized Cluster-Beam Deposition
NASA Technical Reports Server (NTRS)
Fitzgerald, D. J.; Compton, L. E.; Pawlik, E. V.
1986-01-01
Lower temperatures result in higher purity and fewer equipment problems. In cluster-beam deposition, clusters of atoms formed by adiabatic expansion nozzle and with proper nozzle design, expanding vapor cools sufficiently to become supersaturated and form clusters of material deposited. Clusters are ionized and accelerated in electric field and then impacted on substrate where films form. Improved cluster-beam technique useful for deposition of refractory metals.
Nagwani, Naresh Kumar; Deo, Shirish V.
2014-01-01
Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm. PMID:25374939
Nagwani, Naresh Kumar; Deo, Shirish V
2014-01-01
Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm.
Clustering method for estimating principal diffusion directions
Nazem-Zadeh, Mohammad-Reza; Jafari-Khouzani, Kourosh; Davoodi-Bojd, Esmaeil; Jiang, Quan; Soltanian-Zadeh, Hamid
2012-01-01
Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive tool for the investigation of white matter structure within the brain. However, the traditional tensor model is unable to characterize anisotropies of orders higher than two in heterogeneous areas containing more than one fiber population. To resolve this issue, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding gradients is used along with reconstruction methods such as Q-ball. Using HARDI data, the fiber orientation distribution function (ODF) on the unit sphere is calculated and used to extract the principal diffusion directions (PDDs). Fast and accurate estimation of PDDs is a prerequisite for tracking algorithms that deal with fiber crossings. In this paper, the PDDs are defined as the directions around which the ODF data is concentrated. Estimates of the PDDs based on this definition are less sensitive to noise in comparison with the previous approaches. A clustering approach to estimate the PDDs is proposed which is an extension of fuzzy c-means clustering developed for orientation of points on a sphere. MDL (Minimum description length) principle is proposed to estimate the number of PDDs. Using both simulated and real diffusion data, the proposed method has been evaluated and compared with some previous protocols. Experimental results show that the proposed clustering algorithm is more accurate, more resistant to noise, and faster than some of techniques currently being utilized. PMID:21642005
The School Improvement Cluster: A Concept Paper.
ERIC Educational Resources Information Center
Colorado State Dept. of Education, Denver. Office of Field Services.
This paper describes school improvement through clusters (or leagues) of the Colorado Department of Education. School improvement clusters are defined as associations of schools and cooperating organizations dedicated to improving the quality of education. Participants work together with a common goal or unifying concept. The paper describes 14…
Estimation of rank correlation for clustered data.
Rosner, Bernard; Glynn, Robert J
2017-04-11
It is well known that the sample correlation coefficient (Rxy ) is the maximum likelihood estimator of the Pearson correlation (ρxy ) for independent and identically distributed (i.i.d.) bivariate normal data. However, this is not true for ophthalmologic data where X (e.g., visual acuity) and Y (e.g., visual field) are available for each eye and there is positive intraclass correlation for both X and Y in fellow eyes. In this paper, we provide a regression-based approach for obtaining the maximum likelihood estimator of ρxy for clustered data, which can be implemented using standard mixed effects model software. This method is also extended to allow for estimation of partial correlation by controlling both X and Y for a vector U_ of other covariates. In addition, these methods can be extended to allow for estimation of rank correlation for clustered data by (i) converting ranks of both X and Y to the probit scale, (ii) estimating the Pearson correlation between probit scores for X and Y, and (iii) using the relationship between Pearson and rank correlation for bivariate normally distributed data. The validity of the methods in finite-sized samples is supported by simulation studies. Finally, two examples from ophthalmology and analgesic abuse are used to illustrate the methods. Copyright © 2017 John Wiley & Sons, Ltd.
Improved Estimates of Thermodynamic Parameters
NASA Technical Reports Server (NTRS)
Lawson, D. D.
1982-01-01
Techniques refined for estimating heat of vaporization and other parameters from molecular structure. Using parabolic equation with three adjustable parameters, heat of vaporization can be used to estimate boiling point, and vice versa. Boiling points and vapor pressures for some nonpolar liquids were estimated by improved method and compared with previously reported values. Technique for estimating thermodynamic parameters should make it easier for engineers to choose among candidate heat-exchange fluids for thermochemical cycles.
Improved dynamical modelling of the Arches cluster
NASA Astrophysics Data System (ADS)
Lee, Joowon; Kim, Sungsoo S.
2014-05-01
Recently, Clarkson et al. (2012) measured the intrinsic velocity dispersion of the Arches cluster, a young and massive star cluster in the Galactic center. Using the observed velocity dispersion profile and the surface brightness profile of Espinoza et al. (2009), they estimate the cluster's present-day mass to be ˜ 1.5×104 M⊙ by fitting an isothermal King model. In this study, we trace the best-fit initial mass for the Arches cluster using the same observed data set and also the anisotropic Fokker-Planck calculations for the dynamical evolution.
Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters
Tellaroli, Paola; Bazzi, Marco; Donato, Michele; Brazzale, Alessandra R.; Drăghici, Sorin
2016-01-01
Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository. PMID:27015427
State estimation and prediction using clustered particle filters
Lee, Yoonsang; Majda, Andrew J.
2016-01-01
Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors. PMID:27930332
van Breukelen, Gerard Jp; Candel, Math Jjm; Berger, Martijn Pf
2008-08-01
Cluster randomized and multicentre trials evaluate the effect of a treatment on persons nested within clusters, for instance patients within clinics or pupils within schools. Although equal sample sizes per cluster are generally optimal for parameter estimation, they are rarely feasible. This paper addresses the relative efficiency (RE) of unequal versus equal cluster sizes for estimating variance components in cluster randomized trials and in multicentre trials with person randomization within centres, assuming a quantitative outcome. Starting from maximum likelihood estimation, the RE is investigated numerically for a range of cluster size distributions. An approximate formula is presented for computing the RE as a function of the mean and variance of cluster sizes and the intraclass correlation. The accuracy of this approximation is checked and found to be good. It is concluded that the loss of efficiency for variance component estimation due to variation of cluster sizes rarely exceeds 20% and can be compensated by sampling 25% more clusters.
Estimating potential evapotranspiration with improved radiation estimation
Technology Transfer Automated Retrieval System (TEKTRAN)
Potential evapotranspiration (PET) is of great importance to estimation of surface energy budget and water balance calculation. The accurate estimation of PET will facilitate efficient irrigation scheduling, drainage design, and other agricultural and meteorological applications. However, accuracy o...
Improved Event Location Uncertainty Estimates
2008-06-30
model (such as Gaussian, spherical or exponential) typically used in geostatistics, we define the robust variogram model as the median regression curve...variogram model estimation We define the robust variogram model as the median regression curve of the residual difference squares for station pairs of...develop methodologies that improve location uncertainties in the presence of correlated, systematic model errors and non-Gaussian measurement errors. We
Entropy-based cluster validation and estimation of the number of clusters in gene expression data.
Novoselova, Natalia; Tom, Igor
2012-10-01
Many external and internal validity measures have been proposed in order to estimate the number of clusters in gene expression data but as a rule they do not consider the analysis of the stability of the groupings produced by a clustering algorithm. Based on the approach assessing the predictive power or stability of a partitioning, we propose the new measure of cluster validation and the selection procedure to determine the suitable number of clusters. The validity measure is based on the estimation of the "clearness" of the consensus matrix, which is the result of a resampling clustering scheme or consensus clustering. According to the proposed selection procedure the stable clustering result is determined with the reference to the validity measure for the null hypothesis encoding for the absence of clusters. The final number of clusters is selected by analyzing the distance between the validity plots for initial and permutated data sets. We applied the selection procedure to estimate the clustering results on several datasets. As a result the proposed procedure produced an accurate and robust estimate of the number of clusters, which are in agreement with the biological knowledge and gold standards of cluster quality.
Cluster Stability Estimation Based on a Minimal Spanning Trees Approach
NASA Astrophysics Data System (ADS)
Volkovich, Zeev (Vladimir); Barzily, Zeev; Weber, Gerhard-Wilhelm; Toledano-Kitai, Dvora
2009-08-01
Among the areas of data and text mining which are employed today in science, economy and technology, clustering theory serves as a preprocessing step in the data analyzing. However, there are many open questions still waiting for a theoretical and practical treatment, e.g., the problem of determining the true number of clusters has not been satisfactorily solved. In the current paper, this problem is addressed by the cluster stability approach. For several possible numbers of clusters we estimate the stability of partitions obtained from clustering of samples. Partitions are considered consistent if their clusters are stable. Clusters validity is measured as the total number of edges, in the clusters' minimal spanning trees, connecting points from different samples. Actually, we use the Friedman and Rafsky two sample test statistic. The homogeneity hypothesis, of well mingled samples within the clusters, leads to asymptotic normal distribution of the considered statistic. Resting upon this fact, the standard score of the mentioned edges quantity is set, and the partition quality is represented by the worst cluster corresponding to the minimal standard score value. It is natural to expect that the true number of clusters can be characterized by the empirical distribution having the shortest left tail. The proposed methodology sequentially creates the described value distribution and estimates its left-asymmetry. Numerical experiments, presented in the paper, demonstrate the ability of the approach to detect the true number of clusters.
Learning Markov Random Walks for robust subspace clustering and estimation.
Liu, Risheng; Lin, Zhouchen; Su, Zhixun
2014-11-01
Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation. In our framework, both the local pairwise similarity and the global subspace structure can be learnt from the transition probabilities of MRW. We prove that under some suitable conditions, our proposed local/global criteria can exactly capture the multiple subspace structure and learn a low-dimensional embedding for the data, in which giving the true segmentation of subspaces. To improve robustness in real situations, we also propose an extension of the MRW learning model based on integrating transition matrix learning and error correction in a unified framework. Experimental results on both synthetic data and real applications demonstrate that our proposed MRW learning model and its robust extension outperform the state-of-the-art subspace clustering methods.
Proportion estimation using prior cluster purities
NASA Technical Reports Server (NTRS)
Terrell, G. R. (Principal Investigator)
1980-01-01
The prior distribution of CLASSY component purities is studied, and this information incorporated into maximum likelihood crop proportion estimators. The method is tested on Transition Year spring small grain segments.
NASA Astrophysics Data System (ADS)
Gifford, Daniel William
2016-08-01
Galaxy clusters are large virialized structures that exist at the intersection of filaments of matter that make up the cosmic web. Due to their hierarchical growth history, they are excellent probes of the cosmology that governs our universe. Here, we aim to use clusters to better constrain cosmological parameters by systematically studying the uncertainties on galaxy cluster mass estimation for use in a halo mass function analysis. We find that the caustic technique is capable on average of recovering unbiased cluster masses to within 30% for well sampled systems. We also quantify potential statistical and systematic biases due to observational challenges. To address statistical biases in the caustic technique, we developed a new stacking algorithm to measure the average cluster mass for a single stack of projected cluster phase-spaces. By varying the number of galaxies and number of clusters we stack, we find that the single limited value is the total number of galaxies in the stack opening up the possibility for self-calibrated mass estimates of low mass or poorly sampled clusters in large surveys. We then utilize the SDSS-C4 catalog of galaxy clusters to place some of the tightest galaxy cluster based constraints on the matter density and power spectrum normalization for matter in our universe.
NASA Astrophysics Data System (ADS)
Bo, Yizhou; Shifa, Naima
2013-09-01
An estimator for finding the abundance of a rare, clustered and mobile population has been introduced. This model is based on adaptive cluster sampling (ACS) to identify the location of the population and negative binomial distribution to estimate the total in each site. To identify the location of the population we consider both sampling with replacement (WR) and sampling without replacement (WOR). Some mathematical properties of the model are also developed.
A hierarchical clustering methodology for the estimation of toxicity.
Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M
2008-01-01
ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.
The impact of baryons on massive galaxy clusters: halo structure and cluster mass estimates
NASA Astrophysics Data System (ADS)
Henson, Monique A.; Barnes, David J.; Kay, Scott T.; McCarthy, Ian G.; Schaye, Joop
2017-03-01
We use the BAHAMAS (BAryons and HAloes of MAssive Systems) and MACSIS (MAssive ClusterS and Intercluster Structures) hydrodynamic simulations to quantify the impact of baryons on the mass distribution and dynamics of massive galaxy clusters, as well as the bias in X-ray and weak lensing mass estimates. These simulations use the subgrid physics models calibrated in the BAHAMAS project, which include feedback from both supernovae and active galactic nuclei. They form a cluster population covering almost two orders of magnitude in mass, with more than 3500 clusters with masses greater than 1014 M⊙ at z = 0. We start by characterizing the clusters in terms of their spin, shape and density profile, before considering the bias in both weak lensing and hydrostatic mass estimates. Whilst including baryonic effects leads to more spherical, centrally concentrated clusters, the median weak lensing mass bias is unaffected by the presence of baryons. In both the dark matter only and hydrodynamic simulations, the weak lensing measurements underestimate cluster masses by ≈10 per cent for clusters with M200 ≤ 1015 M⊙ and this bias tends to zero at higher masses. We also consider the hydrostatic bias when using both the true density and temperature profiles, and those derived from X-ray spectroscopy. When using spectroscopic temperatures and densities, the hydrostatic bias decreases as a function of mass, leading to a bias of ≈40 per cent for clusters with M500 ≥ 1015 M⊙. This is due to the presence of cooler gas in the cluster outskirts. Using mass weighted temperatures and the true density profile reduces this bias to 5-15 per cent.
IMPROVING BIOGENIC EMISSION ESTIMATES WITH SATELLITE IMAGERY
This presentation will review how existing and future applications of satellite imagery can improve the accuracy of biogenic emission estimates. Existing applications of satellite imagery to biogenic emission estimates have focused on characterizing land cover. Vegetation dat...
A data-driven approach to estimating the number of clusters in hierarchical clustering
Zambelli, Antoine E.
2016-01-01
DNA microarray and gene expression problems often require a researcher to perform clustering on their data in a bid to better understand its structure. In cases where the number of clusters is not known, one can resort to hierarchical clustering methods. However, there currently exist very few automated algorithms for determining the true number of clusters in the data. We propose two new methods (mode and maximum difference) for estimating the number of clusters in a hierarchical clustering framework to create a fully automated process with no human intervention. These methods are compared to the established elbow and gap statistic algorithms using simulated datasets and the Biobase Gene ExpressionSet. We also explore a data mixing procedure inspired by cross validation techniques. We find that the overall performance of the maximum difference method is comparable or greater to that of the gap statistic in multi-cluster scenarios, and achieves that performance at a fraction of the computational cost. This method also responds well to our mixing procedure, which opens the door to future research. We conclude that both the mode and maximum difference methods warrant further study related to their mixing and cross-validation potential. We particularly recommend the use of the maximum difference method in multi-cluster scenarios given its accuracy and execution times, and present it as an alternative to existing algorithms.
Improved Ant Colony Clustering Algorithm and Its Performance Study
Gao, Wei
2016-01-01
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering. PMID:26839533
Improved Ant Colony Clustering Algorithm and Its Performance Study.
Gao, Wei
2016-01-01
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering.
Improving clustering by imposing network information
Gerber, Susanne; Horenko, Illia
2015-01-01
Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface. PMID:26601225
Clustering-based redshift estimation: application to VIPERS/CFHTLS
NASA Astrophysics Data System (ADS)
Scottez, V.; Mellier, Y.; Granett, B. R.; Moutard, T.; Kilbinger, M.; Scodeggio, M.; Garilli, B.; Bolzonella, M.; de la Torre, S.; Guzzo, L.; Abbas, U.; Adami, C.; Arnouts, S.; Bottini, D.; Branchini, E.; Cappi, A.; Cucciati, O.; Davidzon, I.; Fritz, A.; Franzetti, P.; Iovino, A.; Krywult, J.; Le Brun, V.; Le Fèvre, O.; Maccagni, D.; Małek, K.; Marulli, F.; Polletta, M.; Pollo, A.; Tasca, L. A. M.; Tojeiro, R.; Vergani, D.; Zanichelli, A.; Bel, J.; Coupon, J.; De Lucia, G.; Ilbert, O.; McCracken, H. J.; Moscardini, L.
2016-10-01
We explore the accuracy of the clustering-based redshift estimation proposed by Ménard et al. when applied to VIMOS Public Extragalactic Redshift Survey (VIPERS) and Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) real data. This method enables us to reconstruct redshift distributions from measurement of the angular clustering of objects using a set of secure spectroscopic redshifts. We use state-of-the-art spectroscopic measurements with iAB < 22.5 from the VIPERS as reference population to infer the redshift distribution of galaxies from the CFHTLS T0007 release. VIPERS provides a nearly representative sample to a flux limit of iAB < 22.5 at a redshift of >0.5 which allows us to test the accuracy of the clustering-based redshift distributions. We show that this method enables us to reproduce the true mean colour-redshift relation when both populations have the same magnitude limit. We also show that this technique allows the inference of redshift distributions for a population fainter than the reference and we give an estimate of the colour-redshift mapping in this case. This last point is of great interest for future large-redshift surveys which require a complete faint spectroscopic sample.
Improved correction of VIPERS angular selection effects in clustering measurements
NASA Astrophysics Data System (ADS)
Pezzotta, A.; Granett, B. R.; Bel, J.; Guzzo, L.; de la Torre, S.; Aff004
2016-10-01
Clustering estimates in galaxy redshift surveys need to account and correct for the way targets are selected from the general population, as to avoid biasing the measured values of cosmological parameters. The VIMOS Public Extragalactic Redshift Survey (VIPERS) is no exception to this, involving slit collisions and masking effects. Pushed by the increasing precision of the measurements, e.g. of the growth rate f, we have been re-assessing these effects in detail. We present here an improved correction for the two-point correlation function, capable to recover the amplitude of the monopole of the two-point correlation function ξ(r) above 1 h-1 Mpc to better than 2.
The improvement and simulation for LEACH clustering routing protocol
NASA Astrophysics Data System (ADS)
Ji, Ai-guo; Zhao, Jun-xiang
2017-01-01
An energy-balanced unequal multi-hop clustering routing protocol LEACH-EUMC is proposed in this paper. The candidate cluster head nodes are elected firstly, then they compete to be formal cluster head nodes by adding energy and distance factors, finally the date are transferred to sink through multi-hop. The results of simulation show that the improved algorithm is better than LEACH in network lifetime, energy consumption and the amount of data transmission.
Galaxy cluster mass estimation from stacked spectroscopic analysis
NASA Astrophysics Data System (ADS)
Farahi, Arya; Evrard, August E.; Rozo, Eduardo; Rykoff, Eli S.; Wechsler, Risa H.
2016-08-01
We use simulated galaxy surveys to study: (i) how galaxy membership in redMaPPer clusters maps to the underlying halo population, and (ii) the accuracy of a mean dynamical cluster mass, Mσ(λ), derived from stacked pairwise spectroscopy of clusters with richness λ. Using ˜130 000 galaxy pairs patterned after the Sloan Digital Sky Survey (SDSS) redMaPPer cluster sample study of Rozo et al., we show that the pairwise velocity probability density function of central-satellite pairs with mi < 19 in the simulation matches the form seen in Rozo et al. Through joint membership matching, we deconstruct the main Gaussian velocity component into its halo contributions, finding that the top-ranked halo contributes ˜60 per cent of the stacked signal. The halo mass scale inferred by applying the virial scaling of Evrard et al. to the velocity normalization matches, to within a few per cent, the log-mean halo mass derived through galaxy membership matching. We apply this approach, along with miscentring and galaxy velocity bias corrections, to estimate the log-mean matched halo mass at z = 0.2 of SDSS redMaPPer clusters. Employing the velocity bias constraints of Guo et al., we find
Clustering of Casablanca stock market based on hurst exponent estimates
NASA Astrophysics Data System (ADS)
Lahmiri, Salim
2016-08-01
This paper deals with the problem of Casablanca Stock Exchange (CSE) topology modeling as a complex network during three different market regimes: general trend characterized by ups and downs, increasing trend, and decreasing trend. In particular, a set of seven different Hurst exponent estimates are used to characterize long-range dependence in each industrial sector generating process. They are employed in conjunction with hierarchical clustering approach to examine the co-movements of the Casablanca Stock Exchange industrial sectors. The purpose is to investigate whether cluster structures are similar across variable, increasing and decreasing regimes. It is observed that the general structure of the CSE topology has been considerably changed over 2009 (variable regime), 2010 (increasing regime), and 2011 (decreasing regime) time periods. The most important findings follow. First, in general a high value of Hurst exponent is associated to a variable regime and a small one to a decreasing regime. In addition, Hurst estimates during increasing regime are higher than those of a decreasing regime. Second, correlations between estimated Hurst exponent vectors of industrial sectors increase when Casablanca stock exchange follows an upward regime, whilst they decrease when the overall market follows a downward regime.
Motion estimation using point cluster method and Kalman filter.
Senesh, M; Wolf, A
2009-05-01
The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures--PCT, Kalman filter followed by PCT, and low pass filter followed by PCT--enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal
Improving performance through concept formation and conceptual clustering
NASA Technical Reports Server (NTRS)
Fisher, Douglas H.
1992-01-01
Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes.
Nanostar clustering improves the sensitivity of plasmonic assays
Park, Yong; Im, Hyungsoon; Weissleder, Ralph; Lee, Hakho
2017-01-01
Star-shaped Au nanoparticles (Au nanostars, AuNS) have been developed to improve the plasmonic sensitivity, but their application has largely been limited to single-particle probes. We herein describe a AuNS clustering assay based on nanoscale self-assembly of multiple AuNS and which further increases detection sensitivity. We show that each cluster contains multiple nanogaps to concentrate electric fields, thereby amplifying the signal via plasmon coupling. Numerical simulation indicated that AuNS clusters assume up to 460-fold higher field density than Au nanosphere clusters of similar mass. The results were validated in model assays of protein biomarker detection. The AuNS clustering assay showed higher sensitivity than Au nanosphere. Minimizing the size of affinity ligand was found important to tightly confine electric fields and improve the sensitivity. The resulting assay is simple and fast, and can be readily applied to point-of-care molecular detection schemes. PMID:26102604
Unsupervised, Robust Estimation-based Clustering for Multispectral Images
NASA Technical Reports Server (NTRS)
Netanyahu, Nathan S.
1997-01-01
To prepare for the challenge of handling the archiving and querying of terabyte-sized scientific spatial databases, the NASA Goddard Space Flight Center's Applied Information Sciences Branch (AISB, Code 935) developed a number of characterization algorithms that rely on supervised clustering techniques. The research reported upon here has been aimed at continuing the evolution of some of these supervised techniques, namely the neural network and decision tree-based classifiers, plus extending the approach to incorporating unsupervised clustering algorithms, such as those based on robust estimation (RE) techniques. The algorithms developed under this task should be suited for use by the Intelligent Information Fusion System (IIFS) metadata extraction modules, and as such these algorithms must be fast, robust, and anytime in nature. Finally, so that the planner/schedule module of the IlFS can oversee the use and execution of these algorithms, all information required by the planner/scheduler must be provided to the IIFS development team to ensure the timely integration of these algorithms into the overall system.
Improved entropy rate estimation in physiological data.
Lake, D E
2011-01-01
Calculating entropy rate in physiologic signals has proven very useful in many settings. Common entropy estimates for this purpose are sample entropy (SampEn) and its less robust elder cousin, approximate entropy (ApEn). Both approaches count matches within a tolerance r for templates of length m consecutive observations. When physiologic data records are long and well-behaved, both approaches work very well for a wide range of m and r. However, more attention to the details of the estimation algorithm is needed for short records and signals with anomalies. In addition, interpretation of the magnitude of these estimates is highly dependent on how r is chosen and precludes comparison across studies with even slightly different methodologies. In this paper, we summarize recent novel approaches to improve the accuracy of entropy estimation. An important (but not necessarily new) alternative to current approaches is to develop estimates that convert probabilities to densities by normalizing by the matching region volume. This approach leads to a novel concept introduced here of reporting entropy rate in equivalent Gaussian white noise units. Another approach is to allow r to vary so that a pre-specified number of matches are found, called the minimum numerator count, to ensure confident probability estimation. The approaches are illustrated using a simple example of detecting abnormal cardiac rhythms in heart rate records.
Estimating cougar predation rates from GPS location clusters
Anderson, C.R.; Lindzey, F.G.
2003-01-01
We examined cougar (Puma concolor) predation from Global Positioning System (GPS) location clusters (???2 locations within 200 m on the same or consecutive nights) of 11 cougars during September-May, 1999-2001. Location success of GPS averaged 2.4-5.0 of 6 location attempts/night/cougar. We surveyed potential predation sites during summer-fall 2000 and summer 2001 to identify prey composition (n = 74; 3-388 days post predation) and record predation-site variables (n = 97; 3-270 days post predation). We developed a model to estimate probability that a cougar killed a large mammal from data collected at GPS location clusters where the probability of predation increased with number of nights (defined as locations at 2200, 0200, or 0500 hr) of cougar presence within a 200-m radius (P < 0.001). Mean estimated cougar predation rates for large mammals were 7.3 days/kill for subadult females (1-2.5 yr; n = 3, 90% CI: 6.3 to 9.9), 7.0 days/kill for adult females (n = 2, 90% CI: 5.8 to 10.8), 5.4 days/kill for family groups (females with young; n = 3, 90% CI: 4.5 to 8.4), 9.5 days/kill for a subadult male (1-2.5 yr; n = 1, 90% CI: 6.9 to 16.4), and 7.8 days/kill for adult males (n = 2, 90% CI: 6.8 to 10.7). We may have slightly overestimated cougar predation rates due to our inability to separate scavenging from predation. We detected 45 deer (Odocoileus spp.), 15 elk (Cervus elaphus), 6 pronghorn (Antilocapra americana), 2 livestock, 1 moose (Alces alces), and 6 small mammals at cougar predation sites. Comparisons between cougar sexes suggested that females selected mule deer and males selected elk (P < 0.001). Cougars averaged 3.0 nights on pronghorn carcasses, 3.4 nights on deer carcasses, and 6.0 nights on elk carcasses. Most cougar predation (81.7%) occurred between 1901-0500 hr and peaked from 2201-0200 hr (31.7%). Applying GPS technology to identify predation rates and prey selection will allow managers to efficiently estimate the ability of an area's prey base to
Desai, Manisha; Bryson, Susan W; Robinson, Thomas
2013-03-01
This paper examines the implications of using robust estimators (REs) of standard errors in the presence of clustering when cluster membership is unclear as may commonly occur in clustered randomized trials. For example, in such trials, cluster membership may not be recorded for one or more treatment arms and/or cluster membership may be dynamic. When clusters are well defined, REs have properties that are robust to misspecification of the correlation structure. To examine whether results were sensitive to assumptions about the clustering membership, we conducted simulation studies for a two-arm clinical trial, where the number of clusters, the intracluster correlation (ICC), and the sample size varied. REs of standard errors that incorrectly assumed clustering of data that were truly independent yielded type I error rates of up to 40%. Partial and complete misspecifications of membership (where some and no knowledge of true membership were incorporated into assumptions) for data generated from a large number of clusters (50) with a moderate ICC (0.20) yielded type I error rates that ranged from 7.2% to 9.1% and 10.5% to 45.6%, respectively; incorrectly assuming independence gave a type I error rate of 10.5%. REs of standard errors can be useful when the ICC and knowledge of cluster membership are high. When the ICC is weak, a number of factors must be considered. Our findings suggest guidelines for making sensible analytic choices in the presence of clustering.
A Hierarchical Clustering Methodology for the Estimation of Toxicity
A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...
Improvement of propeller static thrust estimation
NASA Technical Reports Server (NTRS)
Brusse, J.; Kettleborough, C. F.
1975-01-01
The problem of improving the performance estimation of propellers operating in the heavily loaded static thrust condition was studied. The Goldstein theory was assessed as it applies to propellers operating in the static thrust. A review of theoretical considerations is presented along with a summary of the attempts made to obtain a numerical solution. The chordwise pressure distribution was determined during operation at a tip speed of 500 ft/sec. Chordwise integration of the pressures leads to the spanwise load distribution and further integration would give the axial thrust.
Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering
Zheng, Ming; Sun, Ying; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang
2012-01-01
Background Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved. PMID:23173043
Improvement of Targeting Efficiency in Chaos Control Using Clustering
NASA Astrophysics Data System (ADS)
Sutcu, Y.; Iplikci, S.; Denizhan, Y.
2002-09-01
In this paper an improved version of the previously presented ECR (Extended Control Regions) targeting method is proposed, where the system data is first pre-processed and subdivided into clusters, and then one artificial neural network is assigned to each such cluster. Furthermore, an analytical criterion for determining the region of the current system state during targeting is introduced, whereas in the original ECR method the region information was hidden in the neural networks. Simulation results on several chaotic systems show that this modified version of the ECR method reduces the average reaching time and in general also the training time of the neural networks.
Improving Lidar Turbulence Estimates for Wind Energy
Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.; Klein, Petra
2016-10-06
Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. This presentation primarily focuses on the physics-based corrections, which include corrections for instrument noise, volume averaging, and variance contamination. As different factors affect TI under different stability conditions, the combination of physical corrections applied in L-TERRA changes depending on the atmospheric stability during each 10-minute time period. This stability-dependent version of L-TERRA performed well at both sites, reducing TI error and bringing lidar TI estimates closer to estimates from instruments on towers. However, there is still scatter evident in the lidar TI estimates, indicating that there are physics that are not being captured in the current version of L-TERRA. Two options are discussed for modeling the remainder of the TI error physics in L-TERRA: machine learning and lidar simulations. Lidar simulations appear to be a better approach, as they can help improve understanding of atmospheric effects on TI error and do not require a large training data set.
An improved Chebyshev distance metric for clustering medical images
NASA Astrophysics Data System (ADS)
Mousa, Aseel; Yusof, Yuhanis
2015-12-01
A metric or distance function is a function which defines a distance between elements of a set. In clustering, measuring the similarity between objects has become an important issue. In practice, there are various similarity measures used and this includes the Euclidean, Manhattan and Minkowski. In this paper, an improved Chebyshev similarity measure is introduced to replace existing metrics (such as Euclidean and standard Chebyshev) in clustering analysis. The proposed measure is later realized in analyzing blood cancer images. Results demonstrate that the proposed measure produces the smallest objective function value and converge at the lowest number of iteration. Hence, it can be concluded that the proposed distance metric contribute in producing better clusters.
Liu, Bo; Lu, Wenbin; Zhang, Jiajia
2013-01-01
Summary Clustered survival data frequently arise in biomedical applications, where event times of interest are clustered into groups such as families. In this article we consider an accelerated failure time frailty model for clustered survival data and develop nonparametric maximum likelihood estimation for it via a kernel smoother aided EM algorithm. We show that the proposed estimator for the regression coefficients is consistent, asymptotically normal and semiparametric efficient when the kernel bandwidth is properly chosen. An EM-aided numerical differentiation method is derived for estimating its variance. Simulation studies evaluate the finite sample performance of the estimator, and it is applied to the Diabetic Retinopathy data set. PMID:24443587
A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set
Peng, Yi; Zhang, Yong; Kou, Gang; Shi, Yong
2012-01-01
Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study. PMID:22870181
Estimation of Carcinogenicity using Hierarchical Clustering and Nearest Neighbor Methodologies
Previously a hierarchical clustering (HC) approach and a nearest neighbor (NN) approach were developed to model acute aquatic toxicity end points. These approaches were developed to correlate the toxicity for large, noncongeneric data sets. In this study these approaches applie...
An improved unsupervised clustering-based intrusion detection method
NASA Astrophysics Data System (ADS)
Hai, Yong J.; Wu, Yu; Wang, Guo Y.
2005-03-01
Practical Intrusion Detection Systems (IDSs) based on data mining are facing two key problems, discovering intrusion knowledge from real-time network data, and automatically updating them when new intrusions appear. Most data mining algorithms work on labeled data. In order to set up basic data set for mining, huge volumes of network data need to be collected and labeled manually. In fact, it is rather difficult and impractical to label intrusions, which has been a big restrict for current IDSs and has led to limited ability of identifying all kinds of intrusion types. An improved unsupervised clustering-based intrusion model working on unlabeled training data is introduced. In this model, center of a cluster is defined and used as substitution of this cluster. Then all cluster centers are adopted to detect intrusions. Testing on data sets of KDDCUP"99, experimental results demonstrate that our method has good performance in detection rate. Furthermore, the incremental-learning method is adopted to detect those unknown-type intrusions and it decreases false positive rate.
IMPROVED RISK ESTIMATES FOR CARBON TETRACHLORIDE
Benson, Janet M.; Springer, David L.
1999-12-31
Carbon tetrachloride has been used extensively within the DOE nuclear weapons facilities. Rocky Flats was formerly the largest volume consumer of CCl4 in the United States using 5000 gallons in 1977 alone (Ripple, 1992). At the Hanford site, several hundred thousand gallons of CCl4 were discharged between 1955 and 1973 into underground cribs for storage. Levels of CCl4 in groundwater at highly contaminated sites at the Hanford facility have exceeded 8 the drinking water standard of 5 ppb by several orders of magnitude (Illman, 1993). High levels of CCl4 at these facilities represent a potential health hazard for workers conducting cleanup operations and for surrounding communities. The level of CCl4 cleanup required at these sites and associated costs are driven by current human health risk estimates, which assume that CCl4 is a genotoxic carcinogen. The overall purpose of these studies was to improve the scientific basis for assessing the health risk associated with human exposure to CCl4. Specific research objectives of this project were to: (1) compare the rates of CCl4 metabolism by rats, mice and hamsters in vivo and extrapolate those rates to man based on parallel studies on the metabolism of CCl4 by rat, mouse, hamster and human hepatic microsomes in vitro; (2) using hepatic microsome preparations, determine the role of specific cytochrome P450 isoforms in CCl4-mediated toxicity and the effects of repeated inhalation and ingestion of CCl4 on these isoforms; and (3) evaluate the toxicokinetics of inhaled CCl4 in rats, mice and hamsters. This information has been used to improve the physiologically based pharmacokinetic (PBPK) model for CCl4 originally developed by Paustenbach et al. (1988) and more recently revised by Thrall and Kenny (1996). Another major objective of the project was to provide scientific evidence that CCl4, like chloroform, is a hepatocarcinogen only when exposure results in cell damage, cell killing and regenerative proliferation. In
VizieR Online Data Catalog: Metallicity estimates of M31 globular clusters (Galleti+, 2009)
NASA Astrophysics Data System (ADS)
Galleti, S.; Bellazzini, M.; Buzzoni, A.; Federici, L.; Fusi Pecci, F.
2010-04-01
New empirical relations of [Fe/H] as a function of [MgFe] and Mg2 indices are based on the well-studied galactic globular clusters, complemented with theoretical model predictions for -0.2<=[Fe/H]<=+0.5. Lick indices for M31 clusters from various literature sources (225 clusters) and from new observations by our team (71 clusters) have been transformed into the Trager et al. (2000AJ....119.1645T) system, yielding new metallicity estimates for 245 globular clusters of M31. (3 data files).
Research opportunities to improve DSM impact estimates
Misuriello, H.; Hopkins, M.E.F. )
1992-03-01
This report was commissioned by the California Institute for Energy Efficiency (CIEE) as part of its research mission to advance the energy efficiency and productivity of all end-use sectors in California. Our specific goal in this effort has been to identify viable research and development (R D) opportunities that can improve capabilities to determine the energy-use and demand reductions achieved through demand-side management (DSM) programs and measures. We surveyed numerous practitioners in California and elsewhere to identify the major obstacles to effective impact evaluation, drawing on their collective experience. As a separate effort, we have also profiled the status of regulatory practices in leading states with respect to DSM impact evaluation. We have synthesized this information, adding our own perspective and experience to those of our survey-respondent colleagues, to characterize today's state of the art in impact-evaluation practices. This scoping study takes a comprehensive look at the problems and issues involved in DSM impact estimates at the customer-facility or site level. The major portion of our study investigates three broad topic areas of interest to CIEE: Data analysis issues, field-monitoring issues, issues in evaluating DSM measures. Across these three topic areas, we have identified 22 potential R D opportunities, to which we have assigned priority levels. These R D opportunities are listed by topic area and priority.
Research opportunities to improve DSM impact estimates
Misuriello, H.; Hopkins, M.E.F.
1992-03-01
This report was commissioned by the California Institute for Energy Efficiency (CIEE) as part of its research mission to advance the energy efficiency and productivity of all end-use sectors in California. Our specific goal in this effort has been to identify viable research and development (R&D) opportunities that can improve capabilities to determine the energy-use and demand reductions achieved through demand-side management (DSM) programs and measures. We surveyed numerous practitioners in California and elsewhere to identify the major obstacles to effective impact evaluation, drawing on their collective experience. As a separate effort, we have also profiled the status of regulatory practices in leading states with respect to DSM impact evaluation. We have synthesized this information, adding our own perspective and experience to those of our survey-respondent colleagues, to characterize today`s state of the art in impact-evaluation practices. This scoping study takes a comprehensive look at the problems and issues involved in DSM impact estimates at the customer-facility or site level. The major portion of our study investigates three broad topic areas of interest to CIEE: Data analysis issues, field-monitoring issues, issues in evaluating DSM measures. Across these three topic areas, we have identified 22 potential R&D opportunities, to which we have assigned priority levels. These R&D opportunities are listed by topic area and priority.
Towards Improved Estimates of Ocean Heat Flux
NASA Astrophysics Data System (ADS)
Bentamy, Abderrahim; Hollman, Rainer; Kent, Elisabeth; Haines, Keith
2014-05-01
Recommendations and priorities for ocean heat flux research are for instance outlined in recent CLIVAR and WCRP reports, eg. Yu et al (2013). Among these is the need for improving the accuracy, the consistency, and the spatial and temporal resolution of air-sea fluxes over global as well as at region scales. To meet the main air-sea flux requirements, this study is aimed at obtaining and analyzing all the heat flux components (latent, sensible and radiative) at the ocean surface over global oceans using multiple satellite sensor observations in combination with in-situ measurements and numerical model analyses. The fluxes will be generated daily and monthly for the 20-year (1992-2011) period, between 80N and 80S and at 0.25deg resolution. Simultaneous estimates of all surface heat flux terms have not yet been calculated at such large scale and long time period. Such an effort requires a wide range of expertise and data sources that only recently are becoming available. Needed are methods for integrating many data sources to calculate energy fluxes (short-wave, long wave, sensible and latent heat) across the air-sea interface. We have access to all the relevant, recently available satellite data to perform such computations. Yu, L., K. Haines, M. Bourassa, M. Cronin, S. Gulev, S. Josey, S. Kato, A. Kumar, T. Lee, D. Roemmich: Towards achieving global closure of ocean heat and freshwater budgets: Recommendations for advancing research in air-sea fluxes through collaborative activities. INTERNATIONAL CLIVAR PROJECT OFFICE, 2013: International CLIVAR Publication Series No 189. http://www.clivar.org/sites/default/files/ICPO189_WHOI_fluxes_workshop.pdf
NASA Astrophysics Data System (ADS)
Strauss, Cesar; Rosa, Marcelo Barbio; Stephany, Stephan
2013-12-01
Convective cells are cloud formations whose growth, maturation and dissipation are of great interest among meteorologists since they are associated with severe storms with large precipitation structures. Some works suggest a strong correlation between lightning occurrence and convective cells. The current work proposes a new approach to analyze the correlation between precipitation and lightning, and to identify electrically active cells. Such cells may be employed for tracking convective events in the absence of weather radar coverage. This approach employs a new spatio-temporal clustering technique based on a temporal sliding-window and a standard kernel density estimation to process lightning data. Clustering allows the identification of the cells from lightning data and density estimation bounds the contours of the cells. The proposed approach was evaluated for two convective events in Southeast Brazil. Image segmentation of radar data was performed to identify convective precipitation structures using the Steiner criteria. These structures were then compared and correlated to the electrically active cells in particular instants of time for both events. It was observed that most precipitation structures have associated cells, by comparing the ground tracks of their centroids. In addition, for one particular cell of each event, its temporal evolution was compared to that of the associated precipitation structure. Results show that the proposed approach may improve the use of lightning data for tracking convective events in countries that lack weather radar coverage.
Improved diagnostic model for estimating wind energy
Endlich, R.M.; Lee, J.D.
1983-03-01
Because wind data are available only at scattered locations, a quantitative method is needed to estimate the wind resource at specific sites where wind energy generation may be economically feasible. This report describes a computer model that makes such estimates. The model uses standard weather reports and terrain heights in deriving wind estimates; the method of computation has been changed from what has been used previously. The performance of the current model is compared with that of the earlier version at three sites; estimates of wind energy at four new sites are also presented.
Open-Source Sequence Clustering Methods Improve the State Of the Art.
Kopylova, Evguenia; Navas-Molina, Jose A; Mercier, Céline; Xu, Zhenjiang Zech; Mahé, Frédéric; He, Yan; Zhou, Hong-Wei; Rognes, Torbjørn; Caporaso, J Gregory; Knight, Rob
2016-01-01
Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http
Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates
Hinne, Max; Janssen, Ronald J.; Heskes, Tom; van Gerven, Marcel A.J.
2015-01-01
Functional connectivity concerns the correlated activity between neuronal populations in spatially segregated regions of the brain, which may be studied using functional magnetic resonance imaging (fMRI). This coupled activity is conveniently expressed using covariance, but this measure fails to distinguish between direct and indirect effects. A popular alternative that addresses this issue is partial correlation, which regresses out the signal of potentially confounding variables, resulting in a measure that reveals only direct connections. Importantly, provided the data are normally distributed, if two variables are conditionally independent given all other variables, their respective partial correlation is zero. In this paper, we propose a probabilistic generative model that allows us to estimate functional connectivity in terms of both partial correlations and a graph representing conditional independencies. Simulation results show that this methodology is able to outperform the graphical LASSO, which is the de facto standard for estimating partial correlations. Furthermore, we apply the model to estimate functional connectivity for twenty subjects using resting-state fMRI data. Results show that our model provides a richer representation of functional connectivity as compared to considering partial correlations alone. Finally, we demonstrate how our approach can be extended in several ways, for instance to achieve data fusion by informing the conditional independence graph with data from probabilistic tractography. As our Bayesian formulation of functional connectivity provides access to the posterior distribution instead of only to point estimates, we are able to quantify the uncertainty associated with our results. This reveals that while we are able to infer a clear backbone of connectivity in our empirical results, the data are not accurately described by simply looking at the mode of the distribution over connectivity. The implication of this is that
Communication: Improved pair approximations in local coupled-cluster methods
NASA Astrophysics Data System (ADS)
Schwilk, Max; Usvyat, Denis; Werner, Hans-Joachim
2015-03-01
In local coupled cluster treatments the electron pairs can be classified according to the magnitude of their energy contributions or distances into strong, close, weak, and distant pairs. Different approximations are introduced for the latter three classes. In this communication, an improved simplified treatment of close and weak pairs is proposed, which is based on long-range cancellations of individually slowly decaying contributions in the amplitude equations. Benchmark calculations for correlation, reaction, and activation energies demonstrate that these approximations work extremely well, while pair approximations based on local second-order Møller-Plesset theory can lead to errors that are 1-2 orders of magnitude larger.
Salmaso, S.; Rota, M. C.; Ciofi Degli Atti, M. L.; Tozzi, A. E.; Kreidl, P.
1999-01-01
In 1998, a series of regional cluster surveys (the ICONA Study) was conducted simultaneously in 19 out of the 20 regions in Italy to estimate the mandatory immunization coverage of children aged 12-24 months with oral poliovirus (OPV), diphtheria-tetanus (DT) and viral hepatitis B (HBV) vaccines, as well as optional immunization coverage with pertussis, measles and Haemophilus influenzae b (Hib) vaccines. The study children were born in 1996 and selected from birth registries using the Expanded Programme of Immunization (EPI) cluster sampling technique. Interviews with parents were conducted to determine each child's immunization status and the reasons for any missed or delayed vaccinations. The study population comprised 4310 children aged 12-24 months. Coverage for both mandatory and optional vaccinations differed by region. The overall coverage for mandatory vaccines (OPV, DT and HBV) exceeded 94%, but only 79% had been vaccinated in accord with the recommended schedule (i.e. during the first year of life). Immunization coverage for pertussis increased from 40% (1993 survey) to 88%, but measles coverage (56%) remained inadequate for controlling the disease; Hib coverage was 20%. These results confirm that in Italy the coverage of only mandatory immunizations is satisfactory. Pertussis immunization coverage has improved dramatically since the introduction of acellular vaccines. A greater effort to educate parents and physicians is still needed to improve the coverage of optional vaccinations in all regions. PMID:10593033
SAR image segmentation using MSER and improved spectral clustering
NASA Astrophysics Data System (ADS)
Gui, Yang; Zhang, Xiaohu; Shang, Yang
2012-12-01
A novel approach is presented for synthetic aperture radar (SAR) image segmentation. By incorporating the advantages of maximally stable extremal regions (MSER) algorithm and spectral clustering (SC) method, the proposed approach provides effective and robust segmentation. First, the input image is transformed from a pixel-based to a region-based model by using the MSER algorithm. The input image after MSER procedure is composed of some disjoint regions. Then the regions are treated as nodes in the image plane, and a graph structure is applied to represent them. Finally, the improved SC is used to perform globally optimal clustering, by which the result of image segmentation can be generated. To avoid some incorrect partitioning when considering each region as one graph node, we assign different numbers of nodes to represent the regions according to area ratios among the regions. In addition, K-harmonic means instead of K-means is applied in the improved SC procedure in order to raise its stability and performance. Experimental results show that the proposed approach is effective on SAR image segmentation and has the advantage of calculating quickly.
Neuronal spike train entropy estimation by history clustering.
Watters, Nicholas; Reeke, George N
2014-09-01
Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed. Here we present two related model-based methods for estimating the entropy of neural signals and compare them to existing methods. One of the methods is fast and reasonably accurate, and it converges well with short spike time records; the other is impractically time-consuming but apparently very accurate, relying on generating artificial data that are a statistical match to the experimental data. Using the slow, accurate method to generate a best-estimate entropy value, we find that the faster estimator converges to this value more closely and with smaller data sets than many existing entropy estimators.
Formation of Education Clusters as a Way to Improve Education
ERIC Educational Resources Information Center
Aitbayeva, Gul'zamira D.; Zhubanova, Mariyash K.; Kulgildinova, Tulebike A.; Tusupbekova, Gulsum M.; Uaisova, Gulnar I.
2016-01-01
The purpose of this research is to analyze basic prerequisites formation and development factors of educational clusters of the world's leading nations for studying the possibility of cluster policy introduction and creating educational clusters in the Republic of Kazakhstan. The authors of this study concluded that educational cluster could be…
Dynamical mass estimates of young massive clusters in NGC1140 and M83
NASA Astrophysics Data System (ADS)
Moll, Sarah L.; de Grijs, Richard; Mengel, Sabine; Smith, Linda J.; Crowther, Paul A.
2009-12-01
We present virial mass estimates of young massive clusters (YMCs) in the starburst galaxies NGC1140 and M83, determined from high spectral resolution VLT echelle spectroscopy and high spatial resolution Hubble Space Telescope imaging. The survivability of such clusters is important in testing the scenario that YMCs are potentially proto-globular clusters. As young clusters, they lie in the domain in which dynamical masses appear to overestimate true cluster masses, most likely due to the clusters not being virialised. We find that the dynamical mass of NGC1140-1 is approximately ten times greater than its photometric mass. We propose that the most likely explanation for this disparity is the crowded environment of NGC1140-1, rather than this being solely due to a lack of virial equilibrium.
Yelland, Lisa N; Salter, Amy B; Ryan, Philip
2011-10-15
Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.
Clustering-based urbanisation to improve enterprise information systems agility
NASA Astrophysics Data System (ADS)
Imache, Rabah; Izza, Said; Ahmed-Nacer, Mohamed
2015-11-01
Enterprises are daily facing pressures to demonstrate their ability to adapt quickly to the unpredictable changes of their dynamic in terms of technology, social, legislative, competitiveness and globalisation. Thus, to ensure its place in this hard context, enterprise must always be agile and must ensure its sustainability by a continuous improvement of its information system (IS). Therefore, the agility of enterprise information systems (EISs) can be considered today as a primary objective of any enterprise. One way of achieving this objective is by the urbanisation of the EIS in the context of continuous improvement to make it a real asset servicing enterprise strategy. This paper investigates the benefits of EISs urbanisation based on clustering techniques as a driver for agility production and/or improvement to help managers and IT management departments to improve continuously the performance of the enterprise and make appropriate decisions in the scope of the enterprise objectives and strategy. This approach is applied to the urbanisation of a tour operator EIS.
Improving Lidar Turbulence Estimates for Wind Energy
Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.; ...
2016-10-03
Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.« less
Improving Lidar Turbulence Estimates for Wind Energy
Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.; Klein, Petra
2016-10-03
Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.
Improving lidar turbulence estimates for wind energy
NASA Astrophysics Data System (ADS)
Newman, J. F.; Clifton, A.; Churchfield, M. J.; Klein, P.
2016-09-01
Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.
A Clustering Classification of Spare Parts for Improving Inventory Policies
NASA Astrophysics Data System (ADS)
Meri Lumban Raja, Anton; Ai, The Jin; Diar Astanti, Ririn
2016-02-01
Inventory policies in a company may consist of storage, control, and replenishment policy. Since the result of common ABC inventory classification can only affect the replenishment policy, we are proposing a clustering based classification technique as a basis for developing inventory policy especially for storage and control policy. Hierarchical clustering procedure is used after clustering variables are defined. Since hierarchical clustering procedure requires metric variables only, therefore a step to convert non-metric variables to metric variables is performed. The clusters resulted from the clustering techniques are analyzed in order to define each cluster characteristics. Then, the inventory policies are determined for each group according to its characteristics. A real data, which consists of 612 items from a local manufacturer's spare part warehouse, are used in the research of this paper to show the applicability of the proposed methodology.
Effect of Random Clustering on Surface Damage Density Estimates
Matthews, M J; Feit, M D
2007-10-29
Identification and spatial registration of laser-induced damage relative to incident fluence profiles is often required to characterize the damage properties of laser optics near damage threshold. Of particular interest in inertial confinement laser systems are large aperture beam damage tests (>1cm{sup 2}) where the number of initiated damage sites for {phi}>14J/cm{sup 2} can approach 10{sup 5}-10{sup 6}, requiring automatic microscopy counting to locate and register individual damage sites. However, as was shown for the case of bacteria counting in biology decades ago, random overlapping or 'clumping' prevents accurate counting of Poisson-distributed objects at high densities, and must be accounted for if the underlying statistics are to be understood. In this work we analyze the effect of random clumping on damage initiation density estimates at fluences above damage threshold. The parameter {psi} = a{rho} = {rho}/{rho}{sub 0}, where a = 1/{rho}{sub 0} is the mean damage site area and {rho} is the mean number density, is used to characterize the onset of clumping, and approximations based on a simple model are used to derive an expression for clumped damage density vs. fluence and damage site size. The influence of the uncorrected {rho} vs. {phi} curve on damage initiation probability predictions is also discussed.
Kalia, Sumeet; Klar, Neil; Donner, Allan
2016-12-30
Cluster randomized trials (CRTs) involve the random assignment of intact social units rather than independent subjects to intervention groups. Time-to-event outcomes often are endpoints in CRTs. Analyses of such data need to account for the correlation among cluster members. The intracluster correlation coefficient (ICC) is used to assess the similarity among binary and continuous outcomes that belong to the same cluster. However, estimating the ICC in CRTs with time-to-event outcomes is a challenge because of the presence of censored observations. The literature suggests that the ICC may be estimated using either censoring indicators or observed event times. A simulation study explores the effect of administrative censoring on estimating the ICC. Results show that ICC estimators derived from censoring indicators or observed event times are negatively biased. Analytic work further supports these results. Observed event times are preferred to estimate the ICC under minimum frequency of administrative censoring. To our knowledge, the existing literature provides no practical guidance on the estimation of ICC when substantial amount of administrative censoring is present. The results from this study corroborate the need for further methodological research on estimating the ICC for correlated time-to-event outcomes. Copyright © 2016 John Wiley & Sons, Ltd.
Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference
ERIC Educational Resources Information Center
Schochet, Peter Z.
2013-01-01
This article examines the estimation of two-stage clustered designs for education randomized control trials (RCTs) using the nonparametric Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for…
How to Estimate the Value of Service Reliability Improvements
Sullivan, Michael J.; Mercurio, Matthew G.; Schellenberg, Josh A.; Eto, Joseph H.
2010-06-08
A robust methodology for estimating the value of service reliability improvements is presented. Although econometric models for estimating value of service (interruption costs) have been established and widely accepted, analysts often resort to applying relatively crude interruption cost estimation techniques in assessing the economic impacts of transmission and distribution investments. This paper first shows how the use of these techniques can substantially impact the estimated value of service improvements. A simple yet robust methodology that does not rely heavily on simplifying assumptions is presented. When a smart grid investment is proposed, reliability improvement is one of the most frequently cited benefits. Using the best methodology for estimating the value of this benefit is imperative. By providing directions on how to implement this methodology, this paper sends a practical, usable message to the industry.
Improving the Discipline of Cost Estimation and Analysis
NASA Technical Reports Server (NTRS)
Piland, William M.; Pine, David J.; Wilson, Delano M.
2000-01-01
The need to improve the quality and accuracy of cost estimates of proposed new aerospace systems has been widely recognized. The industry has done the best job of maintaining related capability with improvements in estimation methods and giving appropriate priority to the hiring and training of qualified analysts. Some parts of Government, and National Aeronautics and Space Administration (NASA) in particular, continue to need major improvements in this area. Recently, NASA recognized that its cost estimation and analysis capabilities had eroded to the point that the ability to provide timely, reliable estimates was impacting the confidence in planning man), program activities. As a result, this year the Agency established a lead role for cost estimation and analysis. The Independent Program Assessment Office located at the Langley Research Center was given this responsibility.
SYSTEM IMPROVEMENT USING SIGNAL-TO-NOISE RATIO ESTIMATION.
systems by using signal-to-noise ratio ( SNR ) estimation of the received signal. Such SNR estimates can be used to adaptively control important system...parameters whose design explicitly depends on SNR . The results of this investigation show, for certain types of systems, performance can indeed be...substantially improved by SNR estimation. The analysis of the report is basically in two parts. In the first part consideration is given to the design
Improving visual estimates of cervical spine range of motion.
Hirsch, Brandon P; Webb, Matthew L; Bohl, Daniel D; Fu, Michael; Buerba, Rafael A; Gruskay, Jordan A; Grauer, Jonathan N
2014-11-01
Cervical spine range of motion (ROM) is a common measure of cervical conditions, surgical outcomes, and functional impairment. Although ROM is routinely assessed by visual estimation in clinical practice, visual estimates have been shown to be unreliable and inaccurate. Reliable goniometers can be used for assessments, but the associated costs and logistics generally limit their clinical acceptance. To investigate whether training can improve visual estimates of cervical spine ROM, we asked attending surgeons, residents, and medical students at our institution to visually estimate the cervical spine ROM of healthy subjects before and after a training session. This training session included review of normal cervical spine ROM in 3 planes and demonstration of partial and full motion in 3 planes by multiple subjects. Estimates before, immediately after, and 1 month after this training session were compared to assess reliability and accuracy. Immediately after training, errors decreased by 11.9° (flexion-extension), 3.8° (lateral bending), and 2.9° (axial rotation). These improvements were statistically significant. One month after training, visual estimates remained improved, by 9.5°, 1.6°, and 3.1°, respectively, but were statistically significant only in flexion-extension. Although the accuracy of visual estimates can be improved, clinicians should be aware of the limitations of visual estimates of cervical spine ROM. Our study results support scrutiny of visual assessment of ROM as a criterion for diagnosing permanent impairment or disability.
High-Resolution Spatial Distribution and Estimation of Access to Improved Sanitation in Kenya
Jia, Peng; Anderson, John D.; Leitner, Michael; Rheingans, Richard
2016-01-01
Background Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals. Methods The surveyed households in 397 clusters from 2008–2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation. Results The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing. Conclusions There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally
NASA Technical Reports Server (NTRS)
Kalton, G.
1983-01-01
A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.
Mathews, William G.; Guo Fulai
2011-09-10
The total feedback energy injected into hot gas in galaxy clusters by central black holes can be estimated by comparing the potential energy of observed cluster gas profiles with the potential energy of non-radiating, feedback-free hot gas atmospheres resulting from gravitational collapse in clusters of the same total mass. Feedback energy from cluster-centered black holes expands the cluster gas, lowering the gas-to-dark-matter mass ratio below the cosmic value. Feedback energy is unnecessarily delivered by radio-emitting jets to distant gas far beyond the cooling radius where the cooling time equals the cluster lifetime. For clusters of mass (4-11) x 10{sup 14} M{sub sun}, estimates of the total feedback energy, (1-3) x 10{sup 63} erg, far exceed feedback energies estimated from observations of X-ray cavities and shocks in the cluster gas, energies gained from supernovae, and energies lost from cluster gas by radiation. The time-averaged mean feedback luminosity is comparable to those of powerful quasars, implying that some significant fraction of this energy may arise from the spin of the black hole. The universal entropy profile in feedback-free gaseous atmospheres in Navarro-Frenk-White cluster halos can be recovered by multiplying the observed gas entropy profile of any relaxed cluster by a factor involving the gas fraction profile. While the feedback energy and associated mass outflow in the clusters we consider far exceed that necessary to stop cooling inflow, the time-averaged mass outflow at the cooling radius almost exactly balances the mass that cools within this radius, an essential condition to shut down cluster cooling flows.
An improved fuzzy c-means clustering algorithm based on shadowed sets and PSO.
Zhang, Jian; Shen, Ling
2014-01-01
To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.
Improvements in estimating proportions of objects from multispectral data
NASA Technical Reports Server (NTRS)
Horwitz, H. M.; Hyde, P. D.; Richardson, W.
1974-01-01
Methods for estimating proportions of objects and materials imaged within the instantaneous field of view of a multispectral sensor were developed further. Improvements in the basic proportion estimation algorithm were devised as well as improved alien object detection procedures. Also, a simplified signature set analysis scheme was introduced for determining the adequacy of signature set geometry for satisfactory proportion estimation. Averaging procedures used in conjunction with the mixtures algorithm were examined theoretically and applied to artificially generated multispectral data. A computationally simpler estimator was considered and found unsatisfactory. Experiments conducted to find a suitable procedure for setting the alien object threshold yielded little definitive result. Mixtures procedures were used on a limited amount of ERTS data to estimate wheat proportion in selected areas. Results were unsatisfactory, partly because of the ill-conditioned nature of the pure signature set.
Evolving Improvements to TRMM Ground Validation Rainfall Estimates
NASA Technical Reports Server (NTRS)
Robinson, M.; Kulie, M. S.; Marks, D. A.; Wolff, D. B.; Ferrier, B. S.; Amitai, E.; Silberstein, D. S.; Fisher, B. L.; Wang, J.; Einaudi, Franco (Technical Monitor)
2000-01-01
The primary function of the TRMM Ground Validation (GV) Program is to create GV rainfall products that provide basic validation of satellite-derived precipitation measurements for select primary sites. Since the successful 1997 launch of the TRMM satellite, GV rainfall estimates have demonstrated systematic improvements directly related to improved radar and rain gauge data, modified science techniques, and software revisions. Improved rainfall estimates have resulted in higher quality GV rainfall products and subsequently, much improved evaluation products for the satellite-based precipitation estimates from TRMM. This presentation will demonstrate how TRMM GV rainfall products created in a semi-automated, operational environment have evolved and improved through successive generations. Monthly rainfall maps and rainfall accumulation statistics for each primary site will be presented for each stage of GV product development. Contributions from individual product modifications involving radar reflectivity (Ze)-rain rate (R) relationship refinements, improvements in rain gauge bulk-adjustment and data quality control processes, and improved radar and gauge data will be discussed. Finally, it will be demonstrated that as GV rainfall products have improved, rainfall estimation comparisons between GV and satellite have converged, lending confidence to the satellite-derived precipitation measurements from TRMM.
NASA Astrophysics Data System (ADS)
Mahmoud, E.; Takey, A.; Shoukry, A.
2016-07-01
We develop a galaxy cluster finding algorithm based on spectral clustering technique to identify optical counterparts and estimate optical redshifts for X-ray selected cluster candidates. As an application, we run our algorithm on a sample of X-ray cluster candidates selected from the third XMM-Newton serendipitous source catalog (3XMM-DR5) that are located in the Stripe 82 of the Sloan Digital Sky Survey (SDSS). Our method works on galaxies described in the color-magnitude feature space. We begin by examining 45 galaxy clusters with published spectroscopic redshifts in the range of 0.1-0.8 with a median of 0.36. As a result, we are able to identify their optical counterparts and estimate their photometric redshifts, which have a typical accuracy of 0.025 and agree with the published ones. Then, we investigate another 40 X-ray cluster candidates (from the same cluster survey) with no redshift information in the literature and found that 12 candidates are considered as galaxy clusters in the redshift range from 0.29 to 0.76 with a median of 0.57. These systems are newly discovered clusters in X-rays and optical data. Among them 7 clusters have spectroscopic redshifts for at least one member galaxy.
Eisenberg, E.; Baram, A.
2007-01-01
For a large class of repulsive interaction models, the Mayer cluster integrals can be transformed into a tridiagonal real symmetric matrix Rmn, whose elements converge to two constants with 1/n2 correction. We find exact expressions in terms of these correction terms for the two critical exponents describing the density near the two singular termination points of the fluid phase. We apply the method to the hard-spheres model and find that the metastable fluid phase terminates at ρt = 0.751[5]. The density near the transition is given by ρt-ρ ∼ (zt − z)σ′, where the critical exponent is predicted to be σ′ = 0.0877[25]. Interestingly, the termination density is close to the observed glass transition; thus, the above critical behavior is expected to be associated with the onset of glassy behavior in hard spheres. PMID:17389362
Galili, Tal; Meilijson, Isaac
2016-01-01
The Rao–Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a “better” one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao–Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao–Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized Bayes estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.] PMID:27499547
Galili, Tal; Meilijson, Isaac
2016-01-02
The Rao-Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized Bayes estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.].
Distributing Power Grid State Estimation on HPC Clusters A System Architecture Prototype
Liu, Yan; Jiang, Wei; Jin, Shuangshuang; Rice, Mark J.; Chen, Yousu
2012-08-20
The future power grid is expected to further expand with highly distributed energy sources and smart loads. The increased size and complexity lead to increased burden on existing computational resources in energy control centers. Thus the need to perform real-time assessment on such systems entails efficient means to distribute centralized functions such as state estimation in the power system. In this paper, we present our early prototype of a system architecture that connects distributed state estimators individually running parallel programs to solve non-linear estimation procedure. The prototype consists of a middleware and data processing toolkits that allows data exchange in the distributed state estimation. We build a test case based on the IEEE 118 bus system and partition the state estimation of the whole system model to available HPC clusters. The measurement from the testbed demonstrates the low overhead of our solution.
Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering.
Rodríguez-Sotelo, J L; Peluffo-Ordoñez, D; Cuesta-Frau, D; Castellanos-Domínguez, G
2012-10-01
The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes.
Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data.
Nguyen, Hien D; McLachlan, Geoffrey J; Orban, Pierre; Bellec, Pierre; Janke, Andrew L
2017-04-01
Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires evaluating products of large numbers of densities of normal random variables. In practical scenarios, these products converge to zero as the length of the time series increases, and thus the ML estimation of MoAR models becomes infeasible without the use of numerical tricks. We propose a maximum pseudolikelihood (MPL) estimation approach as an alternative to the use of numerical tricks. The MPL estimator is proved to be consistent and can be computed with an EM (expectation-maximization) algorithm. Simulations are used to assess the performance of the MPL estimator against that of the ML estimator in cases where the latter was able to be calculated. An application to the clustering of time series data arising from a resting state fMRI experiment is presented as a demonstration of the methodology.
Improving The Discipline of Cost Estimation and Analysis
NASA Technical Reports Server (NTRS)
Piland, William M.; Pine, David J.; Wilson, Delano M.
2000-01-01
The need to improve the quality and accuracy of cost estimates of proposed new aerospace systems has been widely recognized. The industry has done the best job of maintaining related capability with improvements in estimation methods and giving appropriate priority to the hiring and training of qualified analysts. Some parts of Government, and National Aeronautics and Space Administration (NASA) in particular, continue to need major improvements in this area. Recently, NASA recognized that its cost estimation and analysis capabilities had eroded to the point that the ability to provide timely, reliable estimates was impacting the confidence in planning many program activities. As a result, this year the Agency established a lead role for cost estimation and analysis. The Independent Program Assessment Office located at the Langley Research Center was given this responsibility. This paper presents the plans for the newly established role. Described is how the Independent Program Assessment Office, working with all NASA Centers, NASA Headquarters, other Government agencies, and industry, is focused on creating cost estimation and analysis as a professional discipline that will be recognized equally with the technical disciplines needed to design new space and aeronautics activities. Investments in selected, new analysis tools, creating advanced training opportunities for analysts, and developing career paths for future analysts engaged in the discipline are all elements of the plan. Plans also include increasing the human resources available to conduct independent cost analysis of Agency programs during their formulation, to improve near-term capability to conduct economic cost-benefit assessments, to support NASA management's decision process, and to provide cost analysis results emphasizing "full-cost" and "full-life cycle" considerations. The Agency cost analysis improvement plan has been approved for implementation starting this calendar year. Adequate financial
A cluster-based decision support system for estimating earthquake damage and casualties.
Aleskerov, Fuad; Say, Arzu Iseri; Toker, Aysegül; Akin, H Levent; Altay, Gülay
2005-09-01
This paper describes a Decision Support System for Disaster Management (DSS-DM) to aid operational and strategic planning and policy-making for disaster mitigation and preparedness in a less-developed infrastructural context. Such contexts require a more flexible and robust system for fast prediction of damage and losses. The proposed system is specifically designed for earthquake scenarios, estimating the extent of human losses and injuries, as well as the need for temporary shelters. The DSS-DM uses a scenario approach to calculate the aforementioned parameters at the district and sub-district level at different earthquake intensities. The following system modules have been created: clusters (buildings) with respect to use; buildings with respect to construction typology; and estimations of damage to clusters, human losses and injuries, and the need for shelters. The paper not only examines the components of the DSS-DM, but also looks at its application in Besiktas municipality in the city of Istanbul, Turkey.
Improved Versions of Common Estimators of the Recombination Rate.
Gärtner, Kerstin; Futschik, Andreas
2016-09-01
The scaled recombination parameter [Formula: see text] is one of the key parameters, turning up frequently in population genetic models. Accurate estimates of [Formula: see text] are difficult to obtain, as recombination events do not always leave traces in the data. One of the most widely used approaches is composite likelihood. Here, we show that popular implementations of composite likelihood estimators can often be uniformly improved by optimizing the trade-off between bias and variance. The amount of possible improvement depends on parameters such as the sequence length, the sample size, and the mutation rate, and it can be considerable in some cases. It turns out that approximate Bayesian computation, with composite likelihood as a summary statistic, also leads to improved estimates, but now in terms of the posterior risk. Finally, we demonstrate a practical application on real data from Drosophila.
Improved harmonic mean estimator for phylogenetic model evidence.
Arima, Serena; Tardella, Luca
2012-04-01
Bayesian phylogenetic methods are generating noticeable enthusiasm in the field of molecular systematics. Many phylogenetic models are often at stake, and different approaches are used to compare them within a Bayesian framework. The Bayes factor, defined as the ratio of the marginal likelihoods of two competing models, plays a key role in Bayesian model selection. We focus on an alternative estimator of the marginal likelihood whose computation is still a challenging problem. Several computational solutions have been proposed, none of which can be considered outperforming the others simultaneously in terms of simplicity of implementation, computational burden and precision of the estimates. Practitioners and researchers, often led by available software, have privileged so far the simplicity of the harmonic mean (HM) estimator. However, it is known that the resulting estimates of the Bayesian evidence in favor of one model are biased and often inaccurate, up to having an infinite variance so that the reliability of the corresponding conclusions is doubtful. We consider possible improvements of the generalized harmonic mean (GHM) idea that recycle Markov Chain Monte Carlo (MCMC) simulations from the posterior, share the computational simplicity of the original HM estimator, but, unlike it, overcome the infinite variance issue. We show reliability and comparative performance of the improved harmonic mean estimators comparing them to approximation techniques relying on improved variants of the thermodynamic integration.
Improved Estimation and Interpretation of Correlations in Neural Circuits
Yatsenko, Dimitri; Josić, Krešimir; Ecker, Alexander S.; Froudarakis, Emmanouil; Cotton, R. James; Tolias, Andreas S.
2015-01-01
Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this
ERIC Educational Resources Information Center
Rhoads, Christopher
2014-01-01
Recent publications have drawn attention to the idea of utilizing prior information about the correlation structure to improve statistical power in cluster randomized experiments. Because power in cluster randomized designs is a function of many different parameters, it has been difficult for applied researchers to discern a simple rule explaining…
Robust normal estimation of point cloud with sharp features via subspace clustering
NASA Astrophysics Data System (ADS)
Luo, Pei; Wu, Zhuangzhi; Xia, Chunhe; Feng, Lu; Jia, Bo
2014-01-01
Normal estimation is an essential step in point cloud based geometric processing, such as high quality point based rendering and surface reconstruction. In this paper, we present a clustering based method for normal estimation which preserves sharp features. For a piecewise smooth point cloud, the k-nearest neighbors of one point lie on a union of multiple subspaces. Given the PCA normals as input, we perform a subspace clustering algorithm to segment these subspaces. Normals are estimated by the points lying in the same subspace as the center point. In contrast to the previous method, we exploit the low-rankness of the input data, by seeking the lowest rank representation among all the candidates that can represent one normal as linear combinations of the others. Integration of Low-Rank Representation (LRR) makes our method robust to noise. Moreover, our method can simultaneously produce the estimated normals and the local structures which are especially useful for denoise and segmentation applications. The experimental results show that our approach successfully recovers sharp features and generates more reliable results compared with the state-of-theart.
Improving quantum state estimation with mutually unbiased bases.
Adamson, R B A; Steinberg, A M
2010-07-16
When used in quantum state estimation, projections onto mutually unbiased bases have the ability to maximize information extraction per measurement and to minimize redundancy. We present the first experimental demonstration of quantum state tomography of two-qubit polarization states to take advantage of mutually unbiased bases. We demonstrate improved state estimation as compared to standard measurement strategies and discuss how this can be understood from the structure of the measurements we use. We experimentally compared our method to the standard state estimation method for three different states and observe that the infidelity was up to 1.84 ± 0.06 times lower by using our technique than it was by using standard state estimation methods.
Thompson, William L.
2001-07-01
Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.
Ji, Ze-Xuan; Sun, Quan-Sen; Xia, De-Shen
2011-07-01
A modified possibilistic fuzzy c-means clustering algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities and noise. By introducing a novel adaptive method to compute the weights of local spatial in the objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus allowing the suppression of noise and helping to resolve classification ambiguity. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The segmentation target therefore is driven by two forces to smooth the derived optimal bias field and improve the accuracy of the segmentation task. The proposed method has been successfully applied to 3 T, 7 T, synthetic and real MR images with desirable results. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm. Moreover, the proposed algorithm is robust to initialization, thereby allowing fully automatic applications.
A simple recipe for estimating masses of elliptical galaxies and clusters of galaxies
NASA Astrophysics Data System (ADS)
Lyskova, N.
2013-04-01
We discuss a simple and robust procedure to evaluate the mass/circular velocity of massive elliptical galaxies and clusters of galaxies. It relies only on the surface density and the projected velocity dispersion profiles of tracer particles and therefore can be applied even in case of poor or noisy observational data. Stars, globular clusters or planetary nebulae can be used as tracers for mass determination of elliptical galaxies. For clusters the galaxies themselves can be used as tracer particles. The key element of the proposed procedure is the selection of a ``sweet'' radius R_sweet, where the sensitivity to the unknown anisotropy of the tracers' orbits is minimal. At this radius the surface density of tracers declines approximately as I(R)∝ R-2, thus placing R_sweet not far from the half-light radius of the tracers R_eff. The procedure was tested on a sample of cosmological simulations of individual galaxies and galaxy clusters and then applied to real observational data. Independently the total mass profile was derived from the hydrostatic equilibrium equation for the gaseous atmosphere. Mismatch in mass profiles obtained from optical and X-ray data is used to estimate the non-thermal contribution to the gas pressure and/or to constrain the distribution of tracers' orbits.
Distance Estimates for High Redshift Clusters SZ and X-Ray Measurements
NASA Technical Reports Server (NTRS)
Joy, Marshall K.
1999-01-01
I present interferometric images of the Sunyaev-Zel'dovich effect for the high redshift (z $ greater than $ 0.5) galaxy clusters in the \\emph(Einstein) Medium Sensitivity Survey: MS0451.5-0305 (z = 0.54), MS0015.9+1609 (z = 0.55), MS2053.7-0449 (z = 0.58), MS1 137.5+6625 (z = 0.78), and MS 1054.5-0321 (z = 0.83). Isothermal $\\beta$ models are applied to the data to determine the magnitude of the Sunyaev-Zel'dovich (S-Z) decrement in each cluster. Complementary ROSAT PSPC and HRI x-ray data are also analyzed, and are combined with the S-Z data to generate an independent estimate of the cluster distance. Since the Sunyaev-Zel'dovich Effect is invariant with redshift, sensitive S-Z imaging can provide an independent determination of the size, shape, density, and distance of high redshift galaxy clusters; we will discuss current systematic uncertainties with this approach, as well as future observations which will yield stronger constraints.
Wu, Sheng; Crespi, Catherine M; Wong, Weng Kee
2012-09-01
The intraclass correlation coefficient (ICC) is a fundamental parameter of interest in cluster randomized trials as it can greatly affect statistical power. We compare common methods of estimating the ICC in cluster randomized trials with binary outcomes, with a specific focus on their application to community-based cancer prevention trials with primary outcome of self-reported cancer screening. Using three real data sets from cancer screening intervention trials with different numbers and types of clusters and cluster sizes, we obtained point estimates and 95% confidence intervals for the ICC using five methods: the analysis of variance estimator, the Fleiss-Cuzick estimator, the Pearson estimator, an estimator based on generalized estimating equations and an estimator from a random intercept logistic regression model. We compared estimates of the ICC for the overall sample and by study condition. Our results show that ICC estimates from different methods can be quite different, although confidence intervals generally overlap. The ICC varied substantially by study condition in two studies, suggesting that the common practice of assuming a common ICC across all clusters in the trial is questionable. A simulation study confirmed pitfalls of erroneously assuming a common ICC. Investigators should consider using sample size and analysis methods that allow the ICC to vary by study condition.
Yuwono, Mitchell; Su, Steven W; Moulton, Bruce D; Nguyen, Hung T
2013-01-01
When undertaking gait-analysis, one of the most important factors to consider is heel-strike (HS). Signals from a waist worn Inertial Measurement Unit (IMU) provides sufficient accelerometric and gyroscopic information for estimating gait parameter and identifying HS events. In this paper we propose a novel adaptive, unsupervised, and parameter-free identification method for detection of HS events during gait episodes. Our proposed method allows the device to learn and adapt to the profile of the user without the need of supervision. The algorithm is completely parameter-free and requires no prior fine tuning. Autocorrelation features (ACF) of both antero-posterior acceleration (aAP) and medio-lateral acceleration (aML) are used to determine cadence episodes. The Discrete Wavelet Transform (DWT) features of signal peaks during cadence are extracted and clustered using Swarm Rapid Centroid Estimation (Swarm RCE). Left HS (LHS), Right HS (RHS), and movement artifacts are clustered based on intra-cluster correlation. Initial pilot testing of the system on 8 subjects show promising results up to 84.3%±9.2% and 86.7%±6.9% average accuracy with 86.8%±9.2% and 88.9%±7.1% average precision for the segmentation of LHS and RHS respectively.
Leon-Perez, Jose M; Notelaers, Guy; Arenas, Alicia; Munduate, Lourdes; Medina, Francisco J
2014-05-01
Research findings underline the negative effects of exposure to bullying behaviors and document the detrimental health effects of being a victim of workplace bullying. While no one disputes its negative consequences, debate continues about the magnitude of this phenomenon since very different prevalence rates of workplace bullying have been reported. Methodological aspects may explain these findings. Our contribution to this debate integrates behavioral and self-labeling estimation methods of workplace bullying into a measurement model that constitutes a bullying typology. Results in the present sample (n = 1,619) revealed that six different groups can be distinguished according to the nature and intensity of reported bullying behaviors. These clusters portray different paths for the workplace bullying process, where negative work-related and person-degrading behaviors are strongly intertwined. The analysis of the external validity showed that integrating previous estimation methods into a single measurement latent class model provides a reliable estimation method of workplace bullying, which may overcome previous flaws.
Unsupervised Texture Flow Estimation Using Appearance-Space Clustering and Correspondence.
Choi, Sunghwan; Min, Dongbo; Ham, Bumsub; Sohn, Kwanghoon
2015-11-01
This paper presents a texture flow estimation method that uses an appearance-space clustering and a correspondence search in the space of deformed exemplars. To estimate the underlying texture flow, such as scale, orientation, and texture label, most existing approaches require a certain amount of user interactions. Strict assumptions on a geometric model further limit the flow estimation to such a near-regular texture as a gradient-like pattern. We address these problems by extracting distinct texture exemplars in an unsupervised way and using an efficient search strategy on a deformation parameter space. This enables estimating a coherent flow in a fully automatic manner, even when an input image contains multiple textures of different categories. A set of texture exemplars that describes the input texture image is first extracted via a medoid-based clustering in appearance space. The texture exemplars are then matched with the input image to infer deformation parameters. In particular, we define a distance function for measuring a similarity between the texture exemplar and a deformed target patch centered at each pixel from the input image, and then propose to use a randomized search strategy to estimate these parameters efficiently. The deformation flow field is further refined by adaptively smoothing the flow field under guidance of a matching confidence score. We show that a local visual similarity, directly measured from appearance space, explains local behaviors of the flow very well, and the flow field can be estimated very efficiently when the matching criterion meets the randomized search strategy. Experimental results on synthetic and natural images show that the proposed method outperforms existing methods.
2015-03-26
ESTIMATING SINGLE AND MULTIPLE TARGET LOCATIONS USING K-MEANS CLUSTERING WITH RADIO TOMOGRAPHIC IMAGING IN WIRELESS SENSOR NETWORKS THESIS Jeffrey K...AND MULTIPLE TARGET LOCATIONS USING K-MEANS CLUSTERING WITH RADIO TOMOGRAPHIC IMAGING IN WIRELESS SENSOR NETWORKS THESIS Presented to the Faculty...SINGLE AND MULTIPLE TARGET LOCATIONS USING K-MEANS CLUSTERING WITH RADIO TOMOGRAPHIC IMAGING IN WIRELESS SENSOR NETWORKS Jeffrey K. Nishida, B.S.E.E
ROC Estimation from Clustered Data with an Application to Liver Cancer Data
Kim, Joungyoun; Yun, Sung-Cheol; Lim, Johan; Lee, Moo-Song; Son, Won; Park, DoHwan
2016-01-01
In this article, we propose a regression model to compare the performances of different diagnostic methods having clustered ordinal test outcomes. The proposed model treats ordinal test outcomes (an ordinal categorical variable) as grouped-survival time data and uses random effects to explain correlation among outcomes from the same cluster. To compare different diagnostic methods, we introduce a set of covariates indicating diagnostic methods and compare their coefficients. We find that the proposed model defines a Lehmann family and can also introduce a location-scale family of a receiver operating characteristic (ROC) curve. The proposed model can easily be estimated using standard statistical software such as SAS and SPSS. We illustrate its practical usefulness by applying it to testing different magnetic resonance imaging (MRI) methods to detect abnormal lesions in a liver. PMID:28050126
Age estimates of globular clusters in the Milky Way: constraints on cosmology.
Krauss, Lawrence M; Chaboyer, Brian
2003-01-03
Recent observations of stellar globular clusters in the Milky Way Galaxy, combined with revised ranges of parameters in stellar evolution codes and new estimates of the earliest epoch of globular cluster formation, result in a 95% confidence level lower limit on the age of the Universe of 11.2 billion years. This age is inconsistent with the expansion age for a flat Universe for the currently allowed range of the Hubble constant, unless the cosmic equation of state is dominated by a component that violates the strong energy condition. This means that the three fundamental observables in cosmology-the age of the Universe, the distance-redshift relation, and the geometry of the Universe-now independently support the case for a dark energy-dominated Universe.
An improved method of monopulse estimation in PD radar
NASA Astrophysics Data System (ADS)
Wang, Jun; Guo, Peng; Lei, Peng; Wei, Shaoming
2011-10-01
Monopulse estimation is an angle measurement method with high data rate, measurement precision and anti-jamming ability, since the angle information of target is obtained by comparing echoes received in two or more simultaneous antenna beams. However, the data rate of this method decreases due to coherent integration when applied in pulse Doppler (PD) radar. This paper presents an improved method of monopulse estimation in PD radar. In this method, the received echoes are selected by shift before coherent integration, detection and angle measurement. It can increase data rate while maintain angle measurement precision. And the validity of this method is verified by theoretical analysis and simulation results.
Performance Analysis of an Improved MUSIC DoA Estimator
NASA Astrophysics Data System (ADS)
Vallet, Pascal; Mestre, Xavier; Loubaton, Philippe
2015-12-01
This paper adresses the statistical performance of subspace DoA estimation using a sensor array, in the asymptotic regime where the number of samples and sensors both converge to infinity at the same rate. Improved subspace DoA estimators were derived (termed as G-MUSIC) in previous works, and were shown to be consistent and asymptotically Gaussian distributed in the case where the number of sources and their DoA remain fixed. In this case, which models widely spaced DoA scenarios, it is proved in the present paper that the traditional MUSIC method also provides DoA consistent estimates having the same asymptotic variances as the G-MUSIC estimates. The case of DoA that are spaced of the order of a beamwidth, which models closely spaced sources, is also considered. It is shown that G-MUSIC estimates are still able to consistently separate the sources, while it is no longer the case for the MUSIC ones. The asymptotic variances of G-MUSIC estimates are also evaluated.
NASA Astrophysics Data System (ADS)
Dogulu, Nilay; Solomatine, Dimitri; Lal Shrestha, Durga
2014-05-01
Within the context of flood forecasting, assessment of predictive uncertainty has become a necessity for most of the modelling studies in operational hydrology. There are several uncertainty analysis and/or prediction methods available in the literature; however, most of them rely on normality and homoscedasticity assumptions for model residuals occurring in reproducing the observed data. This study focuses on a statistical method analyzing model residuals without having any assumptions and based on a clustering approach: Uncertainty Estimation based on local Errors and Clustering (UNEEC). The aim of this work is to provide a comprehensive evaluation of the UNEEC method's performance in view of clustering approach employed within its methodology. This is done by analyzing normality of model residuals and comparing uncertainty analysis results (for 50% and 90% confidence level) with those obtained from uniform interval and quantile regression methods. An important part of the basis by which the methods are compared is analysis of data clusters representing different hydrometeorological conditions. The validation measures used are PICP, MPI, ARIL and NUE where necessary. A new validation measure linking prediction interval to the (hydrological) model quality - weighted mean prediction interval (WMPI) - is also proposed for comparing the methods more effectively. The case study is Brue catchment, located in the South West of England. A different parametrization of the method than its previous application in Shrestha and Solomatine (2008) is used, i.e. past error values in addition to discharge and effective rainfall is considered. The results show that UNEEC's notable characteristic in its methodology, i.e. applying clustering to data of predictors upon which catchment behaviour information is encapsulated, contributes increased accuracy of the method's results for varying flow conditions. Besides, classifying data so that extreme flow events are individually
NASA Astrophysics Data System (ADS)
Eadie, Gwendolyn; Harris, William E.; Springford, Aaron; Widrow, Larry
2017-01-01
The mass and cumulative mass profile of the Milky Way's dark matter halo is a fundamental property of the Galaxy, and yet these quantities remain poorly constrained and span almost two orders of magnitude in the literature. There are a variety of methods to measure the mass of the Milky Way, and a common way to constrain the mass uses kinematic information of satellite objects (e.g. globular clusters) orbiting the Galaxy. One reason precise estimates of the mass and mass profile remain elusive is that the kinematic data of the globular clusters are incomplete; for some both line-of-sight and proper motion measurements are available (i.e. complete data), and for others there are only line-of-sight velocities (i.e. incomplete data). Furthermore, some proper motion measurements suffer from large measurement uncertainties, and these uncertainties can be difficult to take into account because they propagate in complicated ways. Past methods have dealt with incomplete data by using either only the line-of-sight measurements (and throwing away the proper motions), or only using the complete data. In either case, valuable information is not included in the analysis. During my PhD research, I have been developing a coherent hierarchical Bayesian method to estimate the mass and mass profile of the Galaxy that 1) includes both complete and incomplete kinematic data simultaneously in the analysis, and 2) includes measurement uncertainties in a meaningful way. In this presentation, I will introduce our approach in a way that is accessible and clear, and will also present our estimates of the Milky Way's total mass and mass profile using all available kinematic data from the globular cluster population of the Galaxy.
Aloisio, Kathryn M; Swanson, Sonja A; Micali, Nadia; Field, Alison; Horton, Nicholas J
2014-10-01
Clustered data arise in many settings, particularly within the social and biomedical sciences. As an example, multiple-source reports are commonly collected in child and adolescent psychiatric epidemiologic studies where researchers use various informants (e.g. parent and adolescent) to provide a holistic view of a subject's symptomatology. Fitzmaurice et al. (1995) have described estimation of multiple source models using a standard generalized estimating equation (GEE) framework. However, these studies often have missing data due to additional stages of consent and assent required. The usual GEE is unbiased when missingness is Missing Completely at Random (MCAR) in the sense of Little and Rubin (2002). This is a strong assumption that may not be tenable. Other options such as weighted generalized estimating equations (WEEs) are computationally challenging when missingness is non-monotone. Multiple imputation is an attractive method to fit incomplete data models while only requiring the less restrictive Missing at Random (MAR) assumption. Previously estimation of partially observed clustered data was computationally challenging however recent developments in Stata have facilitated their use in practice. We demonstrate how to utilize multiple imputation in conjunction with a GEE to investigate the prevalence of disordered eating symptoms in adolescents reported by parents and adolescents as well as factors associated with concordance and prevalence. The methods are motivated by the Avon Longitudinal Study of Parents and their Children (ALSPAC), a cohort study that enrolled more than 14,000 pregnant mothers in 1991-92 and has followed the health and development of their children at regular intervals. While point estimates were fairly similar to the GEE under MCAR, the MAR model had smaller standard errors, while requiring less stringent assumptions regarding missingness.
An improved approximate-Bayesian model-choice method for estimating shared evolutionary history
2014-01-01
Background To understand biological diversification, it is important to account for large-scale processes that affect the evolutionary history of groups of co-distributed populations of organisms. Such events predict temporally clustered divergences times, a pattern that can be estimated using genetic data from co-distributed species. I introduce a new approximate-Bayesian method for comparative phylogeographical model-choice that estimates the temporal distribution of divergences across taxa from multi-locus DNA sequence data. The model is an extension of that implemented in msBayes. Results By reparameterizing the model, introducing more flexible priors on demographic and divergence-time parameters, and implementing a non-parametric Dirichlet-process prior over divergence models, I improved the robustness, accuracy, and power of the method for estimating shared evolutionary history across taxa. Conclusions The results demonstrate the improved performance of the new method is due to (1) more appropriate priors on divergence-time and demographic parameters that avoid prohibitively small marginal likelihoods for models with more divergence events, and (2) the Dirichlet-process providing a flexible prior on divergence histories that does not strongly disfavor models with intermediate numbers of divergence events. The new method yields more robust estimates of posterior uncertainty, and thus greatly reduces the tendency to incorrectly estimate models of shared evolutionary history with strong support. PMID:24992937
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
Scrucca, Luca; Fop, Michael; Murphy, T. Brendan; Raftery, Adrian E.
2016-01-01
Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling. PMID:27818791
An Accurate Link Correlation Estimator for Improving Wireless Protocol Performance
Zhao, Zhiwei; Xu, Xianghua; Dong, Wei; Bu, Jiajun
2015-01-01
Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. PMID:25686314
Improving Estimates Of Phase Parameters When Amplitude Fluctuates
NASA Technical Reports Server (NTRS)
Vilnrotter, V. A.; Brown, D. H.; Hurd, W. J.
1989-01-01
Adaptive inverse filter applied to incoming signal and noise. Time-varying inverse-filtering technique developed to improve digital estimate of phase of received carrier signal. Intended for use where received signal fluctuates in amplitude as well as in phase and signal tracked by digital phase-locked loop that keeps its phase error much smaller than 1 radian. Useful in navigation systems, reception of time- and frequency-standard signals, and possibly spread-spectrum communication systems.
Improving the estimation of the tuberculosis burden in India.
Cowling, Krycia; Dandona, Rakhi; Dandona, Lalit
2014-11-01
Although India is considered to be the country with the greatest tuberculosis burden, estimates of the disease's incidence, prevalence and mortality in India rely on sparse data with substantial uncertainty. The relevant available data are less reliable than those from countries that have recently improved systems for case reporting or recently invested in national surveys of tuberculosis prevalence. We explored ways to improve the estimation of the tuberculosis burden in India. We focused on case notification data - among the most reliable data available - and ways to investigate the associated level of underreporting, as well as the need for a national tuberculosis prevalence survey. We discuss several recent developments - i.e. changes in national policies relating to tuberculosis, World Health Organization guidelines for the investigation of the disease, and a rapid diagnostic test - that should improve data collection for the estimation of the tuberculosis burden in India and elsewhere. We recommend the implementation of an inventory study in India to assess the underreporting of tuberculosis cases, as well as a national survey of tuberculosis prevalence. A national assessment of drug resistance in Indian strains of Mycobacterium tuberculosis should also be considered. The results of such studies will be vital for the accurate monitoring of tuberculosis control efforts in India and globally.
Improvement of Source Number Estimation Method for Single Channel Signal
Du, Bolun; He, Yunze
2016-01-01
Source number estimation methods for single channel signal have been investigated and the improvements for each method are suggested in this work. Firstly, the single channel data is converted to multi-channel form by delay process. Then, algorithms used in the array signal processing, such as Gerschgorin’s disk estimation (GDE) and minimum description length (MDL), are introduced to estimate the source number of the received signal. The previous results have shown that the MDL based on information theoretic criteria (ITC) obtains a superior performance than GDE at low SNR. However it has no ability to handle the signals containing colored noise. On the contrary, the GDE method can eliminate the influence of colored noise. Nevertheless, its performance at low SNR is not satisfactory. In order to solve these problems and contradictions, the work makes remarkable improvements on these two methods on account of the above consideration. A diagonal loading technique is employed to ameliorate the MDL method and a jackknife technique is referenced to optimize the data covariance matrix in order to improve the performance of the GDE method. The results of simulation have illustrated that the performance of original methods have been promoted largely. PMID:27736959
Increasing fMRI sampling rate improves Granger causality estimates.
Lin, Fa-Hsuan; Ahveninen, Jyrki; Raij, Tommi; Witzel, Thomas; Chu, Ying-Hua; Jääskeläinen, Iiro P; Tsai, Kevin Wen-Kai; Kuo, Wen-Jui; Belliveau, John W
2014-01-01
Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.
Estimating accuracy of land-cover composition from two-stage cluster sampling
Stehman, S.V.; Wickham, J.D.; Fattorini, L.; Wade, T.D.; Baffetta, F.; Smith, J.H.
2009-01-01
Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), root mean square error (RMSE), and correlation (CORR) to quantify accuracy of land-cover composition for a general two-stage cluster sampling design, and for the special case of simple random sampling without replacement (SRSWOR) at each stage. The bias of the estimators for the two-stage SRSWOR design is evaluated via a simulation study. The estimators of RMSE and CORR have small bias except when sample size is small and the land-cover class is rare. The estimator of MAD is biased for both rare and common land-cover classes except when sample size is large. A general recommendation is that rare land-cover classes require large sample sizes to ensure that the accuracy estimators have small bias. ?? 2009 Elsevier Inc.
Can modeling improve estimation of desert tortoise population densities?
Nussear, K.E.; Tracy, C.R.
2007-01-01
The federally listed desert tortoise (Gopherus agassizii) is currently monitored using distance sampling to estimate population densities. Distance sampling, as with many other techniques for estimating population density, assumes that it is possible to quantify the proportion of animals available to be counted in any census. Because desert tortoises spend much of their life in burrows, and the proportion of tortoises in burrows at any time can be extremely variable, this assumption is difficult to meet. This proportion of animals available to be counted is used as a correction factor (g0) in distance sampling and has been estimated from daily censuses of small populations of tortoises (6-12 individuals). These censuses are costly and produce imprecise estimates of g0 due to small sample sizes. We used data on tortoise activity from a large (N = 150) experimental population to model activity as a function of the biophysical attributes of the environment, but these models did not improve the precision of estimates from the focal populations. Thus, to evaluate how much of the variance in tortoise activity is apparently not predictable, we assessed whether activity on any particular day can predict activity on subsequent days with essentially identical environmental conditions. Tortoise activity was only weakly correlated on consecutive days, indicating that behavior was not repeatable or consistent among days with similar physical environments. ?? 2007 by the Ecological Society of America.
Can modeling improve estimation of desert tortoise population densities?
Nussear, Kenneth E; Tracy, C Richard
2007-03-01
The federally listed desert tortoise (Gopherus agassizii) is currently monitored using distance sampling to estimate population densities. Distance sampling, as with many other techniques for estimating population density, assumes that it is possible to quantify the proportion of animals available to be counted in any census. Because desert tortoises spend much of their life in burrows, and the proportion of tortoises in burrows at any time can be extremely variable, this assumption is difficult to meet. This proportion of animals available to be counted is used as a correction factor (g0) in distance sampling and has been estimated from daily censuses of small populations of tortoises (6-12 individuals). These censuses are costly and produce imprecise estimates of go due to small sample sizes. We used data on tortoise activity from a large (N = 150) experimental population to model activity as a function of the biophysical attributes of the environment, but these models did not improve the precision of estimates from the focal populations. Thus, to evaluate how much of the variance in tortoise activity is apparently not predictable, we assessed whether activity on any particular day can predict activity on subsequent days with essentially identical environmental conditions. Tortoise activity was only weakly correlated on consecutive days, indicating that behavior was not repeatable or consistent among days with similar physical environments.
Improving estimates of tree mortality probability using potential growth rate
Das, Adrian J.; Stephenson, Nathan L.
2015-01-01
Tree growth rate is frequently used to estimate mortality probability. Yet, growth metrics can vary in form, and the justification for using one over another is rarely clear. We tested whether a growth index (GI) that scales the realized diameter growth rate against the potential diameter growth rate (PDGR) would give better estimates of mortality probability than other measures. We also tested whether PDGR, being a function of tree size, might better correlate with the baseline mortality probability than direct measurements of size such as diameter or basal area. Using a long-term dataset from the Sierra Nevada, California, U.S.A., as well as existing species-specific estimates of PDGR, we developed growth–mortality models for four common species. For three of the four species, models that included GI, PDGR, or a combination of GI and PDGR were substantially better than models without them. For the fourth species, the models including GI and PDGR performed roughly as well as a model that included only the diameter growth rate. Our results suggest that using PDGR can improve our ability to estimate tree survival probability. However, in the absence of PDGR estimates, the diameter growth rate was the best empirical predictor of mortality, in contrast to assumptions often made in the literature.
IPEG- IMPROVED PRICE ESTIMATION GUIDELINES (IBM PC VERSION)
NASA Technical Reports Server (NTRS)
Aster, R. W.
1994-01-01
The Improved Price Estimation Guidelines, IPEG, program provides a simple yet accurate estimate of the price of a manufactured product. IPEG facilitates sensitivity studies of price estimates at considerably less expense than would be incurred by using the Standard Assembly-line Manufacturing Industry Simulation, SAMIS, program (COSMIC program NPO-16032). A difference of less than one percent between the IPEG and SAMIS price estimates has been observed with realistic test cases. However, the IPEG simplification of SAMIS allows the analyst with limited time and computing resources to perform a greater number of sensitivity studies than with SAMIS. Although IPEG was developed for the photovoltaics industry, it is readily adaptable to any standard assembly line type of manufacturing industry. IPEG estimates the annual production price per unit. The input data includes cost of equipment, space, labor, materials, supplies, and utilities. Production on an industry wide basis or a process wide basis can be simulated. Once the IPEG input file is prepared, the original price is estimated and sensitivity studies may be performed. The IPEG user selects a sensitivity variable and a set of values. IPEG will compute a price estimate and a variety of other cost parameters for every specified value of the sensitivity variable. IPEG is designed as an interactive system and prompts the user for all required information and offers a variety of output options. The IPEG/PC program is written in TURBO PASCAL for interactive execution on an IBM PC computer under DOS 2.0 or above with at least 64K of memory. The IBM PC color display and color graphics adapter are needed to use the plotting capabilities in IPEG/PC. IPEG/PC was developed in 1984. The original IPEG program is written in SIMSCRIPT II.5 for interactive execution and has been implemented on an IBM 370 series computer with a central memory requirement of approximately 300K of 8 bit bytes. The original IPEG was developed in 1980.
IPEG- IMPROVED PRICE ESTIMATION GUIDELINES (IBM 370 VERSION)
NASA Technical Reports Server (NTRS)
Chamberlain, R. G.
1994-01-01
The Improved Price Estimation Guidelines, IPEG, program provides a simple yet accurate estimate of the price of a manufactured product. IPEG facilitates sensitivity studies of price estimates at considerably less expense than would be incurred by using the Standard Assembly-line Manufacturing Industry Simulation, SAMIS, program (COSMIC program NPO-16032). A difference of less than one percent between the IPEG and SAMIS price estimates has been observed with realistic test cases. However, the IPEG simplification of SAMIS allows the analyst with limited time and computing resources to perform a greater number of sensitivity studies than with SAMIS. Although IPEG was developed for the photovoltaics industry, it is readily adaptable to any standard assembly line type of manufacturing industry. IPEG estimates the annual production price per unit. The input data includes cost of equipment, space, labor, materials, supplies, and utilities. Production on an industry wide basis or a process wide basis can be simulated. Once the IPEG input file is prepared, the original price is estimated and sensitivity studies may be performed. The IPEG user selects a sensitivity variable and a set of values. IPEG will compute a price estimate and a variety of other cost parameters for every specified value of the sensitivity variable. IPEG is designed as an interactive system and prompts the user for all required information and offers a variety of output options. The IPEG/PC program is written in TURBO PASCAL for interactive execution on an IBM PC computer under DOS 2.0 or above with at least 64K of memory. The IBM PC color display and color graphics adapter are needed to use the plotting capabilities in IPEG/PC. IPEG/PC was developed in 1984. The original IPEG program is written in SIMSCRIPT II.5 for interactive execution and has been implemented on an IBM 370 series computer with a central memory requirement of approximately 300K of 8 bit bytes. The original IPEG was developed in 1980.
An improved K-means clustering algorithm in agricultural image segmentation
NASA Astrophysics Data System (ADS)
Cheng, Huifeng; Peng, Hui; Liu, Shanmei
Image segmentation is the first important step to image analysis and image processing. In this paper, according to color crops image characteristics, we firstly transform the color space of image from RGB to HIS, and then select proper initial clustering center and cluster number in application of mean-variance approach and rough set theory followed by clustering calculation in such a way as to automatically segment color component rapidly and extract target objects from background accurately, which provides a reliable basis for identification, analysis, follow-up calculation and process of crops images. Experimental results demonstrate that improved k-means clustering algorithm is able to reduce the computation amounts and enhance precision and accuracy of clustering.
Improving Estimated Optical Constants With MSTM and DDSCAT Modeling
NASA Astrophysics Data System (ADS)
Pitman, K. M.; Wolff, M. J.
2015-12-01
We present numerical experiments to determine quantitatively the effects of mineral particle clustering on Mars spacecraft spectral signatures and to improve upon the values of refractive indices (optical constants n, k) derived from Mars dust laboratory analog spectra such as those from RELAB and MRO CRISM libraries. Whereas spectral properties for Mars analog minerals and actual Mars soil are dominated by aggregates of particles smaller than the size of martian atmospheric dust, the analytic radiative transfer (RT) solutions used to interpret planetary surfaces assume that individual, well-separated particles dominate the spectral signature. Both in RT models and in the refractive index derivation methods that include analytic RT approximations, spheres are also over-used to represent nonspherical particles. Part of the motivation is that the integrated effect over randomly oriented particles on quantities such as single scattering albedo and phase function are relatively less than for single particles. However, we have seen in previous numerical experiments that when varying the shape and size of individual grains within a cluster, the phase function changes in both magnitude and slope, thus the "relatively less" effect is more significant than one might think. Here we examine the wavelength dependence of the forward scattering parameter with multisphere T-matrix (MSTM) and discrete dipole approximation (DDSCAT) codes that compute light scattering by layers of particles on planetary surfaces to see how albedo is affected and integrate our model results into refractive index calculations to remove uncertainties in approximations and parameters that can lower the accuracy of optical constants. By correcting the single scattering albedo and phase function terms in the refractive index determinations, our data will help to improve the understanding of Mars in identifying, mapping the distributions, and quantifying abundances for these minerals and will address long
Improving the quality of parameter estimates obtained from slug tests
Butler, J.J.; McElwee, C.D.; Liu, W.
1996-01-01
The slug test is one of the most commonly used field methods for obtaining in situ estimates of hydraulic conductivity. Despite its prevalence, this method has received criticism from many quarters in the ground-water community. This criticism emphasizes the poor quality of the estimated parameters, a condition that is primarily a product of the somewhat casual approach that is often employed in slug tests. Recently, the Kansas Geological Survey (KGS) has pursued research directed it improving methods for the performance and analysis of slug tests. Based on extensive theoretical and field research, a series of guidelines have been proposed that should enable the quality of parameter estimates to be improved. The most significant of these guidelines are: (1) three or more slug tests should be performed at each well during a given test period; (2) two or more different initial displacements (Ho) should be used at each well during a test period; (3) the method used to initiate a test should enable the slug to be introduced in a near-instantaneous manner and should allow a good estimate of Ho to be obtained; (4) data-acquisition equipment that enables a large quantity of high quality data to be collected should be employed; (5) if an estimate of the storage parameter is needed, an observation well other than the test well should be employed; (6) the method chosen for analysis of the slug-test data should be appropriate for site conditions; (7) use of pre- and post-analysis plots should be an integral component of the analysis procedure, and (8) appropriate well construction parameters should be employed. Data from slug tests performed at a number of KGS field sites demonstrate the importance of these guidelines.
Estimating {Omega} from galaxy redshifts: Linear flow distortions and nonlinear clustering
Bromley, B.C. |; Warren, M.S.; Zurek, W.H.
1997-02-01
We propose a method to determine the cosmic mass density {Omega} from redshift-space distortions induced by large-scale flows in the presence of nonlinear clustering. Nonlinear structures in redshift space, such as fingers of God, can contaminate distortions from linear flows on scales as large as several times the small-scale pairwise velocity dispersion {sigma}{sub {nu}}. Following Peacock & Dodds, we work in the Fourier domain and propose a model to describe the anisotropy in the redshift-space power spectrum; tests with high-resolution numerical data demonstrate that the model is robust for both mass and biased galaxy halos on translinear scales and above. On the basis of this model, we propose an estimator of the linear growth parameter {beta}={Omega}{sup 0.6}/b, where b measures bias, derived from sampling functions that are tuned to eliminate distortions from nonlinear clustering. The measure is tested on the numerical data and found to recover the true value of {beta} to within {approximately}10{percent}. An analysis of {ital IRAS} 1.2 Jy galaxies yields {beta}=0.8{sub {minus}0.3}{sup +0.4} at a scale of 1000kms{sup {minus}1}, which is close to optimal given the shot noise and finite size of the survey. This measurement is consistent with dynamical estimates of {beta} derived from both real-space and redshift-space information. The importance of the method presented here is that nonlinear clustering effects are removed to enable linear correlation anisotropy measurements on scales approaching the translinear regime. We discuss implications for analyses of forthcoming optical redshift surveys in which the dispersion is more than a factor of 2 greater than in the {ital IRAS} data. {copyright} {ital 1997} {ital The American Astronomical Society}
NASA Astrophysics Data System (ADS)
Cruz, S. M. A.; Marques, J. M. C.; Pereira, F. B.
2016-10-01
We propose improvements to our evolutionary algorithm (EA) [J. M. C. Marques and F. B. Pereira, J. Mol. Liq. 210, 51 (2015)] in order to avoid dissociative solutions in the global optimization of clusters with competing attractive and repulsive interactions. The improved EA outperforms the original version of the method for charged colloidal clusters in the size range 3 ≤ N ≤ 25, which is a very stringent test for global optimization algorithms. While the Bernal spiral is the global minimum for clusters in the interval 13 ≤ N ≤ 18, the lowest-energy structure is a peculiar, so-called beaded-necklace, motif for 19 ≤ N ≤ 25. We have also applied the method for larger sizes and unusual quasi-linear and branched clusters arise as low-energy structures.
Improving the computational efficiency of recursive cluster elimination for gene selection.
Luo, Lin-Kai; Huang, Deng-Feng; Ye, Ling-Jun; Zhou, Qi-Feng; Shao, Gui-Fang; Peng, Hong
2011-01-01
The gene expression data are usually provided with a large number of genes and a relatively small number of samples, which brings a lot of new challenges. Selecting those informative genes becomes the main issue in microarray data analysis. Recursive cluster elimination based on support vector machine (SVM-RCE) has shown the better classification accuracy on some microarray data sets than recursive feature elimination based on support vector machine (SVM-RFE). However, SVM-RCE is extremely time-consuming. In this paper, we propose an improved method of SVM-RCE called ISVM-RCE. ISVM-RCE first trains a SVM model with all clusters, then applies the infinite norm of weight coefficient vector in each cluster to score the cluster, finally eliminates the gene clusters with the lowest score. In addition, ISVM-RCE eliminates genes within the clusters instead of removing a cluster of genes when the number of clusters is small. We have tested ISVM-RCE on six gene expression data sets and compared their performances with SVM-RCE and linear-discriminant-analysis-based RFE (LDA-RFE). The experiment results on these data sets show that ISVM-RCE greatly reduces the time cost of SVM-RCE, meanwhile obtains comparable classification performance as SVM-RCE, while LDA-RFE is not stable.
Improving stochastic estimates with inference methods: Calculating matrix diagonals
NASA Astrophysics Data System (ADS)
Selig, Marco; Oppermann, Niels; Enßlin, Torsten A.
2012-02-01
Estimating the diagonal entries of a matrix, that is not directly accessible but only available as a linear operator in the form of a computer routine, is a common necessity in many computational applications, especially in image reconstruction and statistical inference. Here, methods of statistical inference are used to improve the accuracy or the computational costs of matrix probing methods to estimate matrix diagonals. In particular, the generalized Wiener filter methodology, as developed within information field theory, is shown to significantly improve estimates based on only a few sampling probes, in cases in which some form of continuity of the solution can be assumed. The strength, length scale, and precise functional form of the exploited autocorrelation function of the matrix diagonal is determined from the probes themselves. The developed algorithm is successfully applied to mock and real world problems. These performance tests show that, in situations where a matrix diagonal has to be calculated from only a small number of computationally expensive probes, a speedup by a factor of 2 to 10 is possible with the proposed method.
Improved Goldstein Interferogram Filter Based on Local Fringe Frequency Estimation
Feng, Qingqing; Xu, Huaping; Wu, Zhefeng; You, Yanan; Liu, Wei; Ge, Shiqi
2016-01-01
The quality of an interferogram, which is limited by various phase noise, will greatly affect the further processes of InSAR, such as phase unwrapping. Interferometric SAR (InSAR) geophysical measurements’, such as height or displacement, phase filtering is therefore an essential step. In this work, an improved Goldstein interferogram filter is proposed to suppress the phase noise while preserving the fringe edges. First, the proposed adaptive filter step, performed before frequency estimation, is employed to improve the estimation accuracy. Subsequently, to preserve the fringe characteristics, the estimated fringe frequency in each fixed filtering patch is removed from the original noisy phase. Then, the residual phase is smoothed based on the modified Goldstein filter with its parameter alpha dependent on both the coherence map and the residual phase frequency. Finally, the filtered residual phase and the removed fringe frequency are combined to generate the filtered interferogram, with the loss of signal minimized while reducing the noise level. The effectiveness of the proposed method is verified by experimental results based on both simulated and real data. PMID:27886081
Speed Profiles for Improvement of Maritime Emission Estimation.
Yau, Pui Shan; Lee, Shun-Cheng; Ho, Kin Fai
2012-12-01
Maritime emissions play an important role in anthropogenic emissions, particularly for cities with busy ports such as Hong Kong. Ship emissions are strongly dependent on vessel speed, and thus accurate vessel speed is essential for maritime emission studies. In this study, we determined minute-by-minute high-resolution speed profiles of container ships on four major routes in Hong Kong waters using Automatic Identification System (AIS). The activity-based ship emissions of NO(x), CO, HC, CO(2), SO(2), and PM(10) were estimated using derived vessel speed profiles, and results were compared with those using the speed limits of control zones. Estimation using speed limits resulted in up to twofold overestimation of ship emissions. Compared with emissions estimated using the speed limits of control zones, emissions estimated using vessel speed profiles could provide results with up to 88% higher accuracy. Uncertainty analysis and sensitivity analysis of the model demonstrated the significance of improvement of vessel speed resolution. From spatial analysis, it is revealed that SO(2) and PM(10) emissions during maneuvering within 1 nautical mile from port were the highest. They contributed 7%-22% of SO(2) emissions and 8%-17% of PM(10) emissions of the entire voyage in Hong Kong.
NASA Astrophysics Data System (ADS)
Brewick, Patrick T.; Smyth, Andrew W.
2016-12-01
The authors have previously shown that many traditional approaches to operational modal analysis (OMA) struggle to properly identify the modal damping ratios for bridges under traffic loading due to the interference caused by the driving frequencies of the traffic loads. This paper presents a novel methodology for modal parameter estimation in OMA that overcomes the problems presented by driving frequencies and significantly improves the damping estimates. This methodology is based on finding the power spectral density (PSD) of a given modal coordinate, and then dividing the modal PSD into separate regions, left- and right-side spectra. The modal coordinates were found using a blind source separation (BSS) algorithm and a curve-fitting technique was developed that uses optimization to find the modal parameters that best fit each side spectra of the PSD. Specifically, a pattern-search optimization method was combined with a clustering analysis algorithm and together they were employed in a series of stages in order to improve the estimates of the modal damping ratios. This method was used to estimate the damping ratios from a simulated bridge model subjected to moving traffic loads. The results of this method were compared to other established OMA methods, such as Frequency Domain Decomposition (FDD) and BSS methods, and they were found to be more accurate and more reliable, even for modes that had their PSDs distorted or altered by driving frequencies.
Improved estimates of coordinate error for molecular replacement
Oeffner, Robert D.; Bunkóczi, Gábor; McCoy, Airlie J.; Read, Randy J.
2013-11-01
A function for estimating the effective root-mean-square deviation in coordinates between two proteins has been developed that depends on both the sequence identity and the size of the protein and is optimized for use with molecular replacement in Phaser. A top peak translation-function Z-score of over 8 is found to be a reliable metric of when molecular replacement has succeeded. The estimate of the root-mean-square deviation (r.m.s.d.) in coordinates between the model and the target is an essential parameter for calibrating likelihood functions for molecular replacement (MR). Good estimates of the r.m.s.d. lead to good estimates of the variance term in the likelihood functions, which increases signal to noise and hence success rates in the MR search. Phaser has hitherto used an estimate of the r.m.s.d. that only depends on the sequence identity between the model and target and which was not optimized for the MR likelihood functions. Variance-refinement functionality was added to Phaser to enable determination of the effective r.m.s.d. that optimized the log-likelihood gain (LLG) for a correct MR solution. Variance refinement was subsequently performed on a database of over 21 000 MR problems that sampled a range of sequence identities, protein sizes and protein fold classes. Success was monitored using the translation-function Z-score (TFZ), where a TFZ of 8 or over for the top peak was found to be a reliable indicator that MR had succeeded for these cases with one molecule in the asymmetric unit. Good estimates of the r.m.s.d. are correlated with the sequence identity and the protein size. A new estimate of the r.m.s.d. that uses these two parameters in a function optimized to fit the mean of the refined variance is implemented in Phaser and improves MR outcomes. Perturbing the initial estimate of the r.m.s.d. from the mean of the distribution in steps of standard deviations of the distribution further increases MR success rates.
Improved phase arrival estimate and location for local earthquakes in South Korea
NASA Astrophysics Data System (ADS)
Morton, E. A.; Rowe, C. A.; Begnaud, M. L.
2012-12-01
The Korean Institute of Geoscience and Mineral Resources (KIGAM) and the Korean Meteorological Agency (KMA) regularly report local (distance < ~1200 km) seismicity recorded with their networks; we obtain preliminary event location estimates as well as waveform data, but no phase arrivals are reported, so the data are not immediately useful for earthquake location. Our goal is to identify seismic events that are sufficiently well-located to provide accurate seismic travel-time information for events within the KIGAM and KMA networks, and also recorded by some regional stations. Toward that end, we are using a combination of manual phase identification and arrival-time picking, with waveform cross-correlation, to cluster events that have occurred in close proximity to one another, which allows for improved phase identification by comparing the highly correlating waveforms. We cross-correlate the known events with one another on 5 seismic stations and cluster events that correlate above a correlation coefficient threshold of 0.7, which reveals few clusters containing few events each. The small number of repeating events suggests that the online catalogs have had mining and quarry blasts removed before publication, as these can contribute significantly to repeating seismic sources in relatively aseismic regions such as South Korea. The dispersed source locations in our catalog, however, are ideal for seismic velocity modeling by providing superior sampling through the dense seismic station arrangement, which produces favorable event-to-station ray path coverage. Following careful manual phase picking on 104 events chosen to provide adequate ray coverage, we re-locate the events to obtain improved source coordinates. The re-located events are used with Thurber's Simul2000 pseudo-bending local tomography code to estimate the crustal structure on the Korean Peninsula, which is an important contribution to ongoing calibration for events of interest in the region.
Improvement of the noradrenergic symptom cluster following treatment with milnacipran.
Kasper, Siegfried; Meshkat, Diana; Kutzelnigg, Alexandra
2011-01-01
Depression has a major impact on social functioning. Decreased concentration, mental and physical slowing, loss of energy, lassitude, tiredness, and reduced self-care are all symptoms related to reduced noradrenergic activity. Depressed mood; loss of interest or pleasure; sleep disturbances; and feelings of worthlessness, pessimism, and anxiety are related to reduced activity of both serotonergic and noradrenergic neurotransmission. The importance of noradrenergic neurotransmission in social functioning is supported by studies with the specific norepinephrine reuptake inhibitor reboxetine. In healthy volunteers, reboxetine increases cooperative social behavior and social drive. A placebo-controlled study in depressed patients comparing reboxetine with the selective serotonin reuptake inhibitor (SSRI) fluoxetine showed significantly greater improvement in social adaptation with reboxetine. Two recent studies have examined the effect of the serotonin and norepinephrine reuptake inhibitor milnacipran on social adaptation. A study in depressed patients found that at the end of 8 weeks of treatment with milnacipran, 42.2% patients were in remission on the Social Adaptation Self-evaluation Scale (SASS). Another study in depressed workers or homemakers found that mean depression scores were significantly reduced after 2 weeks, whereas the SASS scores were significantly improved after 4 weeks. A preliminary study comparing depressed patients treated with milnacipran or the SSRI paroxetine showed that milnacipran treatment resulted in a greater number of patients in social remission. The available data thus suggest that milnacipran may improve social functioning, with a possibly greater effect than the SSRI paroxetine. These preliminary data suggest further evaluation of social dysfunction and its treatment outcome in future trials of milnacipran.
Improvements on foF1 estimation at polar regions
NASA Astrophysics Data System (ADS)
Sabbagh, Dario; Scotto, Carlo; Sgrigna, Vittorio
2016-04-01
The analysis of a sample of polar ionograms reveals that the DuCharme and Petrie empirical formula often fails in the foF1 estimation at polar regions. A study of the discrepancies between modeled and observed foF1 values is presented, using a data set of Antarctic ionograms from different stations. Such discrepancies have been quantitatively evaluated. Based on this study a correction to the DuCharme and Petrie formula is proposed. This correction is performed to be implemented in an improved version of Autoscala software for a particular ionospheric station, in the frame of AUSPICIO (Automatic Interpretation of Polar Ionograms and Cooperative Ionospheric Observations) project.
Improved risk estimates for carbon tetrachloride. 1998 annual progress report
Benson, J.M.; Springer, D.L.; Thrall, K.D.
1998-06-01
'The overall purpose of these studies is to improve the scientific basis for assessing the cancer risk associated with human exposure to carbon tetrachloride. Specifically, the toxicokinetics of inhaled carbon tetrachloride is being determined in rats, mice and hamsters. Species differences in the metabolism of carbon tetrachloride by rats, mice and hamsters is being determined in vivo and in vitro using tissues and microsomes from these rodent species and man. Dose-response relationships will be determined in all studies. The information will be used to improve the current physiologically based pharmacokinetic model for carbon tetrachloride. The authors will also determine whether carbon tetrachloride is a hepatocarcinogen only when exposure results in cell damage, cell killing, and regenerative cell proliferation. In combination, the results of these studies will provide the types of information needed to enable a refined risk estimate for carbon tetrachloride under EPA''s new guidelines for cancer risk assessment.'
Improving Distribution Resiliency with Microgrids and State and Parameter Estimation
Tuffner, Francis K.; Williams, Tess L.; Schneider, Kevin P.; Elizondo, Marcelo A.; Sun, Yannan; Liu, Chen-Ching; Xu, Yin; Gourisetti, Sri Nikhil Gup
2015-09-30
Modern society relies on low-cost reliable electrical power, both to maintain industry, as well as provide basic social services to the populace. When major disturbances occur, such as Hurricane Katrina or Hurricane Sandy, the nation’s electrical infrastructure can experience significant outages. To help prevent the spread of these outages, as well as facilitating faster restoration after an outage, various aspects of improving the resiliency of the power system are needed. Two such approaches are breaking the system into smaller microgrid sections, and to have improved insight into the operations to detect failures or mis-operations before they become critical. Breaking the system into smaller sections of microgrid islands, power can be maintained in smaller areas where distribution generation and energy storage resources are still available, but bulk power generation is no longer connected. Additionally, microgrid systems can maintain service to local pockets of customers when there has been extensive damage to the local distribution system. However, microgrids are grid connected a majority of the time and implementing and operating a microgrid is much different than when islanded. This report discusses work conducted by the Pacific Northwest National Laboratory that developed improvements for simulation tools to capture the characteristics of microgrids and how they can be used to develop new operational strategies. These operational strategies reduce the cost of microgrid operation and increase the reliability and resilience of the nation’s electricity infrastructure. In addition to the ability to break the system into microgrids, improved observability into the state of the distribution grid can make the power system more resilient. State estimation on the transmission system already provides great insight into grid operations and detecting abnormal conditions by leveraging existing measurements. These transmission-level approaches are expanded to using
Reducing measurement scale mismatch to improve surface energy flux estimation
NASA Astrophysics Data System (ADS)
Iwema, Joost; Rosolem, Rafael; Rahman, Mostaquimur; Blyth, Eleanor; Wagener, Thorsten
2016-04-01
Soil moisture importantly controls land surface processes such as energy and water partitioning. A good understanding of these controls is needed especially when recognizing the challenges in providing accurate hyper-resolution hydrometeorological simulations at sub-kilometre scales. Soil moisture controlling factors can, however, differ at distinct scales. In addition, some parameters in land surface models are still often prescribed based on observations obtained at another scale not necessarily employed by such models (e.g., soil properties obtained from lab samples used in regional simulations). To minimize such effects, parameters can be constrained with local data from Eddy-Covariance (EC) towers (i.e., latent and sensible heat fluxes) and Point Scale (PS) soil moisture observations (e.g., TDR). However, measurement scales represented by EC and PS still differ substantially. Here we use the fact that Cosmic-Ray Neutron Sensors (CRNS) estimate soil moisture at horizontal footprint similar to that of EC fluxes to help answer the following question: Does reduced observation scale mismatch yield better soil moisture - surface fluxes representation in land surface models? To answer this question we analysed soil moisture and surface fluxes measurements from twelve COSMOS-Ameriflux sites in the USA characterized by distinct climate, soils and vegetation types. We calibrated model parameters of the Joint UK Land Environment Simulator (JULES) against PS and CRNS soil moisture data, respectively. We analysed the improvement in soil moisture estimation compared to uncalibrated model simulations and then evaluated the degree of improvement in surface fluxes before and after calibration experiments. Preliminary results suggest that a more accurate representation of soil moisture dynamics is achieved when calibrating against observed soil moisture and further improvement obtained with CRNS relative to PS. However, our results also suggest that a more accurate
Zhong, Wei; Altun, Gulsah; Harrison, Robert; Tai, Phang C; Pan, Yi
2005-09-01
Information about local protein sequence motifs is very important to the analysis of biologically significant conserved regions of protein sequences. These conserved regions can potentially determine the diverse conformation and activities of proteins. In this work, recurring sequence motifs of proteins are explored with an improved K-means clustering algorithm on a new dataset. The structural similarity of these recurring sequence clusters to produce sequence motifs is studied in order to evaluate the relationship between sequence motifs and their structures. To the best of our knowledge, the dataset used by our research is the most updated dataset among similar studies for sequence motifs. A new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Our experiments indicate that the improved K-means algorithm satisfactorily increases the percentage of sequence segments belonging to clusters with high structural similarity. Careful comparison of sequence motifs obtained by the improved and traditional algorithms also suggests that the improved K-means clustering algorithm may discover some relatively weak and subtle sequence motifs, which are undetectable by the traditional K-means algorithms. Many biochemical tests reported in the literature show that these sequence motifs are biologically meaningful. Experimental results also indicate that the improved K-means algorithm generates more detailed sequence motifs representing common structures than previous research. Furthermore, these motifs are universally conserved sequence patterns across protein families, overcoming some weak points of other popular sequence motifs. The satisfactory result of the experiment suggests that this new K-means algorithm may be applied to other areas of bioinformatics
An improved clustering algorithm of tunnel monitoring data for cloud computing.
Zhong, Luo; Tang, KunHao; Li, Lin; Yang, Guang; Ye, JingJing
2014-01-01
With the rapid development of urban construction, the number of urban tunnels is increasing and the data they produce become more and more complex. It results in the fact that the traditional clustering algorithm cannot handle the mass data of the tunnel. To solve this problem, an improved parallel clustering algorithm based on k-means has been proposed. It is a clustering algorithm using the MapReduce within cloud computing that deals with data. It not only has the advantage of being used to deal with mass data but also is more efficient. Moreover, it is able to compute the average dissimilarity degree of each cluster in order to clean the abnormal data.
An improved local immunization strategy for scale-free networks with a high degree of clustering
NASA Astrophysics Data System (ADS)
Xia, Lingling; Jiang, Guoping; Song, Yurong; Song, Bo
2017-01-01
The design of immunization strategies is an extremely important issue for disease or computer virus control and prevention. In this paper, we propose an improved local immunization strategy based on node's clustering which was seldom considered in the existing immunization strategies. The main aim of the proposed strategy is to iteratively immunize the node which has a high connectivity and a low clustering coefficient. To validate the effectiveness of our strategy, we compare it with two typical local immunization strategies on both real and artificial networks with a high degree of clustering. Simulations on these networks demonstrate that the performance of our strategy is superior to that of two typical strategies. The proposed strategy can be regarded as a compromise between computational complexity and immune effect, which can be widely applied in scale-free networks of high clustering, such as social network, technological networks and so on. In addition, this study provides useful hints for designing optimal immunization strategy for specific network.
Laser photogrammetry improves size and demographic estimates for whale sharks.
Rohner, Christoph A; Richardson, Anthony J; Prebble, Clare E M; Marshall, Andrea D; Bennett, Michael B; Weeks, Scarla J; Cliff, Geremy; Wintner, Sabine P; Pierce, Simon J
2015-01-01
Whale sharks Rhincodon typus are globally threatened, but a lack of biological and demographic information hampers an accurate assessment of their vulnerability to further decline or capacity to recover. We used laser photogrammetry at two aggregation sites to obtain more accurate size estimates of free-swimming whale sharks compared to visual estimates, allowing improved estimates of biological parameters. Individual whale sharks ranged from 432-917 cm total length (TL) (mean ± SD = 673 ± 118.8 cm, N = 122) in southern Mozambique and from 420-990 cm TL (mean ± SD = 641 ± 133 cm, N = 46) in Tanzania. By combining measurements of stranded individuals with photogrammetry measurements of free-swimming sharks, we calculated length at 50% maturity for males in Mozambique at 916 cm TL. Repeat measurements of individual whale sharks measured over periods from 347-1,068 days yielded implausible growth rates, suggesting that the growth increment over this period was not large enough to be detected using laser photogrammetry, and that the method is best applied to estimating growth rates over longer (decadal) time periods. The sex ratio of both populations was biased towards males (74% in Mozambique, 89% in Tanzania), the majority of which were immature (98% in Mozambique, 94% in Tanzania). The population structure for these two aggregations was similar to most other documented whale shark aggregations around the world. Information on small (<400 cm) whale sharks, mature individuals, and females in this region is lacking, but necessary to inform conservation initiatives for this globally threatened species.
Laser photogrammetry improves size and demographic estimates for whale sharks
Richardson, Anthony J.; Prebble, Clare E.M.; Marshall, Andrea D.; Bennett, Michael B.; Weeks, Scarla J.; Cliff, Geremy; Wintner, Sabine P.; Pierce, Simon J.
2015-01-01
Whale sharks Rhincodon typus are globally threatened, but a lack of biological and demographic information hampers an accurate assessment of their vulnerability to further decline or capacity to recover. We used laser photogrammetry at two aggregation sites to obtain more accurate size estimates of free-swimming whale sharks compared to visual estimates, allowing improved estimates of biological parameters. Individual whale sharks ranged from 432–917 cm total length (TL) (mean ± SD = 673 ± 118.8 cm, N = 122) in southern Mozambique and from 420–990 cm TL (mean ± SD = 641 ± 133 cm, N = 46) in Tanzania. By combining measurements of stranded individuals with photogrammetry measurements of free-swimming sharks, we calculated length at 50% maturity for males in Mozambique at 916 cm TL. Repeat measurements of individual whale sharks measured over periods from 347–1,068 days yielded implausible growth rates, suggesting that the growth increment over this period was not large enough to be detected using laser photogrammetry, and that the method is best applied to estimating growth rates over longer (decadal) time periods. The sex ratio of both populations was biased towards males (74% in Mozambique, 89% in Tanzania), the majority of which were immature (98% in Mozambique, 94% in Tanzania). The population structure for these two aggregations was similar to most other documented whale shark aggregations around the world. Information on small (<400 cm) whale sharks, mature individuals, and females in this region is lacking, but necessary to inform conservation initiatives for this globally threatened species. PMID:25870776
Which Elements of Improvement Collaboratives Are Most Effective? A Cluster-Randomized Trial
Gustafson, D. H.; Quanbeck, A. R.; Robinson, J. M.; Ford, J. H.; Pulvermacher, A.; French, M. T.; McConnell, K. J.; Batalden, P. B.; Hoffman, K. A.; McCarty, D.
2013-01-01
Aims Improvement collaboratives consisting of various components are used throughout healthcare to improve quality, but no study has identified which components work best. This study tested the effectiveness of different components in addiction treatment services, hypothesizing that a combination of all components would be most effective. Design An unblinded cluster-randomized trial assigned clinics to one of four groups: interest circle calls (group teleconferences), clinic-level coaching, learning sessions (large face-to-face meetings), and a combination of all three. Interest circle calls functioned as a minimal intervention comparison group. Setting Outpatient addiction treatment clinics in the U.S. Participants 201 clinics in 5 states. Measurements Clinic data managers submitted data on three primary outcomes: waiting time (mean days between first contact and first treatment), retention (percent of patients retained from first to fourth treatment session), and annual number of new patients. State and group costs were collected for a cost-effectiveness analysis. Findings Waiting time declined significantly for 3 groups: coaching (an average of −4.6 days/clinic, P=0.001), learning sessions (−3.5 days/clinic, P=0.012), and the combination (−4.7 days/clinic, P=0.001). The coaching and combination groups significantly increased the number of new patients (19.5%, P=0.028; 8.9%, P=0.029; respectively). Interest circle calls showed no significant effects on outcomes. None of the groups significantly improved retention. The estimated cost/clinic was $2,878 for coaching versus $7,930 for the combination. Coaching and the combination of collaborative components were about equally effective in achieving study aims, but coaching was substantially more cost effective. Conclusions When trying to improve the effectiveness of addiction treatment services, clinic-level coaching appears to help improve waiting time and number of new patients while other components of
Raichoor, A.; Mei, S.; Huertas-Company, M.; Licitra, R.; Erben, T.; Hildebrandt, H.; Ilbert, O.; Boissier, S.; Boselli, A.; Ball, N. M.; Côté, P.; Ferrarese, L.; Gwyn, S. D. J.; Kavelaars, J. J.; Chen, Y.-T.; Cuillandre, J.-C.; Duc, P. A.; Guhathakurta, P.; and others
2014-12-20
The Next Generation Virgo Cluster Survey (NGVS) is an optical imaging survey covering 104 deg{sup 2} centered on the Virgo cluster. Currently, the complete survey area has been observed in the u*giz bands and one third in the r band. We present the photometric redshift estimation for the NGVS background sources. After a dedicated data reduction, we perform accurate photometry, with special attention to precise color measurements through point-spread function homogenization. We then estimate the photometric redshifts with the Le Phare and BPZ codes. We add a new prior that extends to i {sub AB} = 12.5 mag. When using the u* griz bands, our photometric redshifts for 15.5 mag ≤ i ≲ 23 mag or z {sub phot} ≲ 1 galaxies have a bias |Δz| < 0.02, less than 5% outliers, a scatter σ{sub outl.rej.}, and an individual error on z {sub phot} that increases with magnitude (from 0.02 to 0.05 and from 0.03 to 0.10, respectively). When using the u*giz bands over the same magnitude and redshift range, the lack of the r band increases the uncertainties in the 0.3 ≲ z {sub phot} ≲ 0.8 range (–0.05 < Δz < –0.02, σ{sub outl.rej} ∼ 0.06, 10%-15% outliers, and z {sub phot.err.} ∼ 0.15). We also present a joint analysis of the photometric redshift accuracy as a function of redshift and magnitude. We assess the quality of our photometric redshifts by comparison to spectroscopic samples and by verifying that the angular auto- and cross-correlation function w(θ) of the entire NGVS photometric redshift sample across redshift bins is in agreement with the expectations.
NASA Astrophysics Data System (ADS)
Raichoor, A.; Mei, S.; Erben, T.; Hildebrandt, H.; Huertas-Company, M.; Ilbert, O.; Licitra, R.; Ball, N. M.; Boissier, S.; Boselli, A.; Chen, Y.-T.; Côté, P.; Cuillandre, J.-C.; Duc, P. A.; Durrell, P. R.; Ferrarese, L.; Guhathakurta, P.; Gwyn, S. D. J.; Kavelaars, J. J.; Lançon, A.; Liu, C.; MacArthur, L. A.; Muller, M.; Muñoz, R. P.; Peng, E. W.; Puzia, T. H.; Sawicki, M.; Toloba, E.; Van Waerbeke, L.; Woods, D.; Zhang, H.
2014-12-01
The Next Generation Virgo Cluster Survey (NGVS) is an optical imaging survey covering 104 deg2 centered on the Virgo cluster. Currently, the complete survey area has been observed in the u*giz bands and one third in the r band. We present the photometric redshift estimation for the NGVS background sources. After a dedicated data reduction, we perform accurate photometry, with special attention to precise color measurements through point-spread function homogenization. We then estimate the photometric redshifts with the Le Phare and BPZ codes. We add a new prior that extends to i AB = 12.5 mag. When using the u* griz bands, our photometric redshifts for 15.5 mag <= i <~ 23 mag or z phot <~ 1 galaxies have a bias |Δz| < 0.02, less than 5% outliers, a scatter σoutl.rej., and an individual error on z phot that increases with magnitude (from 0.02 to 0.05 and from 0.03 to 0.10, respectively). When using the u*giz bands over the same magnitude and redshift range, the lack of the r band increases the uncertainties in the 0.3 <~ z phot <~ 0.8 range (-0.05 < Δz < -0.02, σoutl.rej ~ 0.06, 10%-15% outliers, and z phot.err. ~ 0.15). We also present a joint analysis of the photometric redshift accuracy as a function of redshift and magnitude. We assess the quality of our photometric redshifts by comparison to spectroscopic samples and by verifying that the angular auto- and cross-correlation function w(θ) of the entire NGVS photometric redshift sample across redshift bins is in agreement with the expectations.
Aging and memory improvement through semantic clustering: The role of list-presentation format.
Kuhlmann, Beatrice G; Touron, Dayna R
2016-11-01
The present study examined how the presentation format of the study list influences younger and older adults' semantic clustering. Spontaneous clustering did not differ between age groups or between an individual-words (presentation of individual study words in consecution) and a whole-list (presentation of the whole study list at once for the same total duration) presentation format in 132 younger (18-30 years, M = 19.7) and 120 older (60-84 years, M = 69.5) adults. However, after instructions to use semantic clustering (second list) age-related differences in recall magnified, indicating a utilization deficiency, and both age groups achieved higher recall in the whole-list than in the individual-words format. While this whole-list benefit was comparable across age groups, it is notable that older adults were only able to improve their average recall performance after clustering instructions in the whole-list but not in the individual-words format. In both formats, instructed clustering was correlated with processing resources (processing speed and, especially, working memory capacity), particularly in older adults. Spontaneous clustering, however, was not related to processing resources but to metacognitive beliefs about the efficacy and difficulty of semantic clustering, neither of which indicated awareness of the benefits of the whole-list presentation format in either age group. Taken together, the findings demonstrate that presentation format has a nontrivial influence on the utilization of semantic clustering in adults. The analyses further highlight important differences between output-based and list-based clustering measures. (PsycINFO Database Record
Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation
NASA Technical Reports Server (NTRS)
Forman, Bart; Reichle, Rofl; Rodell, Matt
2011-01-01
Passive microwave (e.g. AMSR-E) and visible spectrum (e.g. MODIS) measurements of snow states have been used in conjunction with land surface models to better characterize snow pack states, most notably snow water equivalent (SWE). However, both types of measurements have limitations. AMSR-E, for example, suffers a loss of information in deep/wet snow packs. Similarly, MODIS suffers a loss of temporal correlation information beyond the initial accumulation and final ablation phases of the snow season. Gravimetric measurements, on the other hand, do not suffer from these limitations. In this study, gravimetric measurements from the Gravity Recovery and Climate Experiment (GRACE) mission are used in a land surface model data assimilation (DA) framework to better characterize SWE in the Mackenzie River basin located in northern Canada. Comparisons are made against independent, ground-based SWE observations, state-of-the-art modeled SWE estimates, and independent, ground-based river discharge observations. Preliminary results suggest improved SWE estimates, including improved timing of the subsequent ablation and runoff of the snow pack. Additionally, use of the DA procedure can add vertical and horizontal resolution to the coarse-scale GRACE measurements as well as effectively downscale the measurements in time. Such findings offer the potential for better understanding of the hydrologic cycle in snow-dominated basins located in remote regions of the globe where ground-based observation collection if difficult, if not impossible. This information could ultimately lead to improved freshwater resource management in communities dependent on snow melt as well as a reduction in the uncertainty of river discharge into the Arctic Ocean.
Improved PPP ambiguity resolution by COES FCB estimation
NASA Astrophysics Data System (ADS)
Li, Yihe; Gao, Yang; Shi, Junbo
2016-05-01
Precise point positioning (PPP) integer ambiguity resolution is able to significantly improve the positioning accuracy with the correction of fractional cycle biases (FCBs) by shortening the time to first fix (TTFF) of ambiguities. When satellite orbit products are adopted to estimate the satellite FCB corrections, the narrow-lane (NL) FCB corrections will be contaminated by the orbit's line-of-sight (LOS) errors which subsequently affect ambiguity resolution (AR) performance, as well as positioning accuracy. To effectively separate orbit errors from satellite FCBs, we propose a cascaded orbit error separation (COES) method for the PPP implementation. Instead of using only one direction-independent component in previous studies, the satellite NL improved FCB corrections are modeled by one direction-independent component and three directional-dependent components per satellite in this study. More specifically, the direction-independent component assimilates actual FCBs, whereas the directional-dependent components are used to assimilate the orbit errors. To evaluate the performance of the proposed method, GPS measurements from a regional and a global network are processed with the IGSReal-time service (RTS), IGS rapid (IGR) products and predicted orbits with >10 cm 3D root mean square (RMS) error. The improvements by the proposed FCB estimation method are validated in terms of ambiguity fractions after applying FCB corrections and positioning accuracy. The numerical results confirm that the obtained FCBs using the proposed method outperform those by conventional method. The RMS of ambiguity fractions after applying FCB corrections is reduced by 13.2 %. The position RMSs in north, east and up directions are reduced by 30.0, 32.0 and 22.0 % on average.
Evaluation of Incremental Improvement in the NWS MPE Precipitation Estimates
NASA Astrophysics Data System (ADS)
Qin, L.; Habib, E. H.
2009-12-01
This study focuses on assessment of incremental improvement in the multi-sensor precipitation estimates (MPE) developed by the National Weather Service (NWS) River Forecast Centers (RFC). The MPE product is based upon merging of data from WSR-88D radar, surface rain gauge, and occasionally geo-stationary satellite data. The MPE algorithm produces 5 intermediate sets of products known as: RMOSAIC, BMOSAIC, MMOSAIC, LMOSAIC, and MLMOSAIC. These products have different bias-removal and optimal gauge-merging mechanisms. The final product used in operational applications is selected by the RFC forecasters. All the MPE products are provided at hourly temporal resolution and over a national Hydrologic Rainfall Analysis Project (HRAP) grid of a nominal size of 4 square kilometers. To help determine the incremental improvement of MPE estimates, an evaluation analysis was performed over a two-year period (2005-2006) using 13 independently operated rain gauges located within an area of ~30 km2 in south Louisiana. The close proximity of gauge sites to each other allows for multiple gauges to be located within the same HRAP pixel and thus provides reliable estimates of true surface rainfall to be used as a reference dataset. The evaluation analysis is performed over two temporal scales: hourly and event duration. Besides graphical comparisons using scatter and histogram plots, several statistical measures are also applied such as multiplicative bias, additive bias, correlation, and error standard deviation. The results indicated a mixed performance of the different products over the study site depending on which statistical metric is used. The products based on local bias adjustment have lowest error standard deviation but worst multiplicative bias. The opposite is true for products that are based on mean-filed bias adjustment. Optimal merging with gauge fields lead to a reduction in the error quantiles of the products. The results of the current study will provide insight into
Ironing out the wrinkles in the rare biosphere through improved OTU clustering.
Huse, Susan M; Welch, David Mark; Morrison, Hilary G; Sogin, Mitchell L
2010-07-01
Deep sequencing of PCR amplicon libraries facilitates the detection of low-abundance populations in environmental DNA surveys of complex microbial communities. At the same time, deep sequencing can lead to overestimates of microbial diversity through the generation of low-frequency, error-prone reads. Even with sequencing error rates below 0.005 per nucleotide position, the common method of generating operational taxonomic units (OTUs) by multiple sequence alignment and complete-linkage clustering significantly increases the number of predicted OTUs and inflates richness estimates. We show that a 2% single-linkage preclustering methodology followed by an average-linkage clustering based on pairwise alignments more accurately predicts expected OTUs in both single and pooled template preparations of known taxonomic composition. This new clustering method can reduce the OTU richness in environmental samples by as much as 30-60% but does not reduce the fraction of OTUs in long-tailed rank abundance curves that defines the rare biosphere.
Ip, Edward H; Wasserman, Richard; Barkin, Shari
2011-03-01
Designing cluster randomized trials in clinical studies often requires accurate estimates of intraclass correlation, which quantifies the strength of correlation between units, such as participants, within a cluster, such as a practice. Published ICC estimates, even when available, often suffer from the problem of wide confidence intervals. Using data from a national, randomized, controlled study concerning violence prevention for children--the Safety Check--we compare the ICC values derived from two approaches only baseline data and using both baseline and follow-up data. Using a variance component decomposition approach, the latter method allows flexibility in handling complex data sets. For example, it allows for shifts in the outcome variable over time and for an unbalanced cluster design. Furthermore, we evaluate the large-sample formula for ICC estimates and standard errors using the bootstrap method. Our findings suggest that ICC estimates range from 0.012 to 0.11 for providers within practice and range from 0.018 to 0.11 for families within provider. The estimates derived from the baseline-only and repeated-measurements approaches agree quite well except in cases in which variation over repeated measurements is large. The reductions in the widths of ICC confidence limits from using repeated measurement over baseline only are, respectively, 62% and 42% at the practice and provider levels. The contribution of this paper therefore includes two elements, which are a methodology for improving the accuracy of ICC, and the reporting of such quantities for pediatric and other researchers who are interested in designing clustered randomized trials similar to the current study.
Improved Estimates of Air Pollutant Emissions from Biorefinery
Tan, Eric C. D.
2015-11-13
We have attempted to use detailed kinetic modeling approach for improved estimation of combustion air pollutant emissions from biorefinery. We have developed a preliminary detailed reaction mechanism for biomass combustion. Lignin is the only biomass component included in the current mechanism and methane is used as the biogas surrogate. The model is capable of predicting the combustion emissions of greenhouse gases (CO2, N2O, CH4) and criteria air pollutants (NO, NO2, CO). The results are yet to be compared with the experimental data. The current model is still in its early stages of development. Given the acknowledged complexity of biomass oxidation, as well as the components in the feed to the combustor, obviously the modeling approach and the chemistry set discussed here may undergo revision, extension, and further validation in the future.
Improved methods of estimating critical indices via fractional calculus
NASA Astrophysics Data System (ADS)
Bandyopadhyay, S. K.; Bhattacharyya, K.
2002-05-01
Efficiencies of certain methods for the determination of critical indices from power-series expansions are shown to be considerably improved by a suitable implementation of fractional differentiation. In the context of the ratio method (RM), kinship of the modified strategy with the ad hoc `shifted' RM is established and the advantages are demonstrated. Further, in the course of the estimation of critical points, significant betterment of convergence properties of diagonal Padé approximants is observed on several occasions by invoking this concept. Test calculations are performed on (i) various Ising spin-1/2 lattice models for susceptibility series attended with a ferromagnetic phase transition, (ii) complex model situations involving confluent and antiferromagnetic singularities and (iii) the chain-generating functions for self-avoiding walks on triangular, square and simple cubic lattices.
Improving Accuracy of Influenza-Associated Hospitalization Rate Estimates
Reed, Carrie; Kirley, Pam Daily; Aragon, Deborah; Meek, James; Farley, Monica M.; Ryan, Patricia; Collins, Jim; Lynfield, Ruth; Baumbach, Joan; Zansky, Shelley; Bennett, Nancy M.; Fowler, Brian; Thomas, Ann; Lindegren, Mary L.; Atkinson, Annette; Finelli, Lyn; Chaves, Sandra S.
2015-01-01
Diagnostic test sensitivity affects rate estimates for laboratory-confirmed influenza–associated hospitalizations. We used data from FluSurv-NET, a national population-based surveillance system for laboratory-confirmed influenza hospitalizations, to capture diagnostic test type by patient age and influenza season. We calculated observed rates by age group and adjusted rates by test sensitivity. Test sensitivity was lowest in adults >65 years of age. For all ages, reverse transcription PCR was the most sensitive test, and use increased from <10% during 2003–2008 to ≈70% during 2009–2013. Observed hospitalization rates per 100,000 persons varied by season: 7.3–50.5 for children <18 years of age, 3.0–30.3 for adults 18–64 years, and 13.6–181.8 for adults >65 years. After 2009, hospitalization rates adjusted by test sensitivity were ≈15% higher for children <18 years, ≈20% higher for adults 18–64 years, and ≈55% for adults >65 years of age. Test sensitivity adjustments improve the accuracy of hospitalization rate estimates. PMID:26292017
Improved estimate for the muon g-2 using VMD constraints
NASA Astrophysics Data System (ADS)
Benayoun, M.
2012-04-01
The muon anomalous magnetic moment aμ and the hadronic vacuum polarization (HVP) are examined using data analyzed within the framework of a suitably broken HLS model. The analysis relies on all available scan data samples and leaves aside the existing ISR data. The framework provided by our broken HLS model allows for improved estimates of the contributions to aμ from the e+e- annihilation cross sections into π+π-,π0γ,ηγ,π+π-π0,K+K-,K0K up to slightly above the ϕ meson mass. Within this framework, the information provided by the τ±→π±π0ν decay and by the radiative decays (VPγ and Pγγ) of light flavor mesons play as strong constraints on the model parameters. The discrepancy between the theoretical estimate of the muon anomalous magnetic moment g-2 and its direct BNL measurement is shown to reach conservatively 4.1σ while standard methods used under the same conditions yield 3.5σ.
Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure
Xiao, Fan; Li, Bin; Zhang, Siguang
2016-01-01
Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods. PMID:27579031
Leão, Erico; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco
2017-01-01
The use of Wireless Sensor Network (WSN) technologies is an attractive option to support wide-scale monitoring applications, such as the ones that can be found in precision agriculture, environmental monitoring and industrial automation. The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable topology to build wide-scale WSNs. Despite some of its known advantages, including timing synchronisation and duty-cycle operation, cluster-tree networks may suffer from severe network congestion problems due to the convergecast pattern of its communication traffic. Therefore, the careful adjustment of transmission opportunities (superframe durations) allocated to the cluster-heads is an important research issue. This paper proposes a set of proportional Superframe Duration Allocation (SDA) schemes, based on well-defined protocol and timing models, and on the message load imposed by child nodes (Load-SDA scheme), or by number of descendant nodes (Nodes-SDA scheme) of each cluster-head. The underlying reasoning is to adequately allocate transmission opportunities (superframe durations) and parametrize buffer sizes, in order to improve the network throughput and avoid typical problems, such as: network congestion, high end-to-end communication delays and discarded messages due to buffer overflows. Simulation assessments show how proposed allocation schemes may clearly improve the operation of wide-scale cluster-tree networks. PMID:28134822
Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure.
Zhang, Wen; Xiao, Fan; Li, Bin; Zhang, Siguang
2016-01-01
Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.
Leão, Erico; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco
2017-01-27
The use ofWireless Sensor Network (WSN) technologies is an attractive option to support wide-scale monitoring applications, such as the ones that can be found in precision agriculture, environmental monitoring and industrial automation. The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable topology to build wide-scale WSNs. Despite some of its known advantages, including timing synchronisation and duty-cycle operation, cluster-tree networks may suffer from severe network congestion problems due to the convergecast pattern of its communication traffic. Therefore, the careful adjustment of transmission opportunities (superframe durations) allocated to the cluster-heads is an important research issue. This paper proposes a set of proportional Superframe Duration Allocation (SDA) schemes, based on well-defined protocol and timing models, and on the message load imposed by child nodes (Load-SDA scheme), or by number of descendant nodes (Nodes-SDA scheme) of each cluster-head. The underlying reasoning is to adequately allocate transmission opportunities (superframe durations) and parametrize buffer sizes, in order to improve the network throughput and avoid typical problems, such as: network congestion, high end-to-end communication delays and discarded messages due to buffer overflows. Simulation assessments show how proposed allocation schemes may clearly improve the operation of wide-scale cluster-tree networks.
Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network
Zhang, Yunong; Zhang, Xiang; Chen, Jinxia; Lv, Linsheng; Ma, Huijuan; Wu, Xiaoming; Zhao, Weihong; Lou, Tanqi
2013-01-01
Background Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance. Methods A total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation. Results In the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m2 vs. a range from 71.3 to 101.7 ml/min/1.73 m2, allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network. Conclusions A new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse
Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data
NASA Astrophysics Data System (ADS)
Ayuyev, Vadim V.; Jupin, Joseph; Harris, Philip W.; Obradovic, Zoran
The appropriate choice of a method for imputation of missing data becomes especially important when the fraction of missing values is large and the data are of mixed type. The proposed dynamic clustering imputation (DCI) algorithm relies on similarity information from shared neighbors, where mixed type variables are considered together. When evaluated on a public social science dataset of 46,043 mixed type instances with up to 33% missing values, DCI resulted in more than 20% improved imputation accuracy over Multiple Imputation, Predictive Mean Matching, Linear and Multilevel Regression, and Mean Mode Replacement methods. Data imputed by 6 methods were used for prediction tests by NB-Tree, Random Subset Selection and Neural Network-based classification models. In our experiments classification accuracy obtained using DCI-preprocessed data was much better than when relying on alternative imputation methods for data preprocessing.
Improved Soundings and Error Estimates using AIRS/AMSU Data
NASA Technical Reports Server (NTRS)
Susskind, Joel
2006-01-01
AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1 K, and layer precipitable water with an rms error of 20 percent, in cases with up to 80 percent effective cloud cover. The basic theory used to analyze AIRS/AMSU/HSB data in the presence of clouds, called the at-launch algorithm, and a post-launch algorithm which differed only in the minor details from the at-launch algorithm, have been described previously. The post-launch algorithm, referred to as AIRS Version 4.0, has been used by the Goddard DAAC to analyze and distribute AIRS retrieval products. In this paper we show progress made toward the AIRS Version 5.0 algorithm which will be used by the Goddard DAAC starting late in 2006. A new methodology has been developed to provide accurate case by case error estimates for retrieved geophysical parameters and for the channel by channel cloud cleared radiances used to derive the geophysical parameters from the AIRS/AMSU observations. These error estimates are in turn used for quality control of the derived geophysical parameters and clear column radiances. Improvements made to the retrieval algorithm since Version 4.0 are described as well as results comparing Version 5.0 retrieval accuracy and spatial coverage with those obtained using Version 4.0.
Improved fuzzy clustering algorithms in segmentation of DC-enhanced breast MRI.
Kannan, S R; Ramathilagam, S; Devi, Pandiyarajan; Sathya, A
2012-02-01
Segmentation of medical images is a difficult and challenging problem due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. The objective of this work is to develop some robust fuzzy clustering segmentation systems for effective segmentation of DCE - breast MRI. This paper obtains the robust fuzzy clustering algorithms by incorporating kernel methods, penalty terms, tolerance of the neighborhood attraction, additional entropy term and fuzzy parameters. The initial centers are obtained using initialization algorithm to reduce the computation complexity and running time of proposed algorithms. Experimental works on breast images show that the proposed algorithms are effective to improve the similarity measurement, to handle large amount of noise, to have better results in dealing the data corrupted by noise, and other artifacts. The clustering results of proposed methods are validated using Silhouette Method.
Improving lidar-derived turbulence estimates for wind energy
Newman, Jennifer F.; Clifton, Andrew
2016-07-08
Remote sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, commercially available lidars in locations where high-resolution sonic anemometer datamore » are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The algorithm, L-TERRA, can be applied using only data from a stand-alone commercially available lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. L-TERRA was tested on data from three sites – two in flat terrain and one in semicomplex terrain. L-TERRA significantly reduced errors in lidar turbulence at all three sites, even when the machine-learning portion of the model was trained on one site and applied to a different site. Errors in turbulence were then related to errors in power through the use of a power prediction model for a simulated 1.5 MW turbine. L-TERRA also reduced errors in power significantly at all three sites, although moderate power errors remained for
Improving lidar-derived turbulence estimates for wind energy
Newman, Jennifer F.; Clifton, Andrew
2016-07-08
Remote sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, commercially available lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry.
In this work, a new turbulence error reduction algorithm for lidars is described. The algorithm, L-TERRA, can be applied using only data from a stand-alone commercially available lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar.
L-TERRA was tested on data from three sites – two in flat terrain and one in semicomplex terrain. L-TERRA significantly reduced errors in lidar turbulence at all three sites, even when the machine-learning portion of the model was trained on one site and applied to a different site. Errors in turbulence were then related to errors in power through the use of a power prediction model for a simulated 1.5 MW turbine. L-TERRA also reduced errors in power significantly at all three sites, although moderate power errors
ERIC Educational Resources Information Center
Schochet, Peter Z.
2009-01-01
This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…
ERIC Educational Resources Information Center
Hunt, Charles R.
A study developed a model to assist school administrators to estimate costs associated with the delivery of a metals cluster program at Norfolk State College, Virginia. It sought to construct the model so that costs could be explained as a function of enrollment levels. Data were collected through a literature review, computer searches of the…
Improvement of gougerotin and nikkomycin production by engineering their biosynthetic gene clusters.
Du, Deyao; Zhu, Yu; Wei, Junhong; Tian, Yuqing; Niu, Guoqing; Tan, Huarong
2013-07-01
Nikkomycins and gougerotin are peptidyl nucleoside antibiotics with broad biological activities. The nikkomycin biosynthetic gene cluster comprises one pathway-specific regulatory gene (sanG) and 21 structural genes, whereas the gene cluster for gougerotin biosynthesis includes one putative regulatory gene, one major facilitator superfamily transporter gene, and 13 structural genes. In the present study, we introduced sanG driven by six different promoters into Streptomyces ansochromogenes TH322. Nikkomycin production was increased significantly with the highest increase in engineered strain harboring hrdB promoter-driven sanG. In the meantime, we replaced the native promoter of key structural genes in the gougerotin (gou) gene cluster with the hrdB promoters. The heterologous producer Streptomyces coelicolor M1146 harboring the modified gene cluster produced gougerotin up to 10-fold more than strains carrying the unmodified cluster. Therefore, genetic manipulations of genes involved in antibiotics biosynthesis with the constitutive hrdB promoter present a robust, easy-to-use system generally useful for the improvement of antibiotics production in Streptomyces.
Yeeles, Ksenija; Bremner, Stephen; Lauber, Christoph; Eldridge, Sandra; Ashby, Deborah; David, Anthony S; O’Connell, Nicola; Forrest, Alexandra; Burns, Tom
2013-01-01
Objective To test whether offering financial incentives to patients with psychotic disorders is effective in improving adherence to maintenance treatment with antipsychotics. Design Cluster randomised controlled trial. Setting Community mental health teams in secondary psychiatric care in the United Kingdom. Participants Patients with a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder, who were prescribed long acting antipsychotic (depot) injections but had received 75% or less of the prescribed injections. We randomly allocated 73 teams with a total of 141 patients. Primary outcome data were available for 35 intervention teams with 75 patients (96% of randomised) and for 31 control teams with 56 patients (89% of randomised). Interventions Participants in the intervention group were offered £15 (€17; $22) for each depot injection over a 12 month period. Participants in the control condition received treatment as usual. Main outcome measure The primary outcome was the percentage of prescribed depot injections given during the 12 month intervention period. Results 73 teams with 141 consenting patients were randomised, and outcomes were assessed for 131 patients (93%). Average baseline adherence was 69% in the intervention group and 67% in the control group. During the 12 month trial period adherence was 85% in the intervention group and 71% in the control group. The adjusted effect estimate was 11.5% (95% confidence interval 3.9% to 19.0%, P=0.003). A secondary outcome was an adherence of ≥95%, which was achieved in 28% of the intervention group and 5% of the control group (adjusted odds ratio 8.21, 95% confidence interval 2.00 to 33.67, P=0.003). Although differences in clinician rated clinical improvement between the groups failed to reach statistical significance, patients in the intervention group had more favourable subjective quality of life ratings (β=0.71, 95% confidence interval 0.26 to 1.15, P=0.002). The number of admissions
Adaptive arrival cost update for improving Moving Horizon Estimation performance.
Sánchez, G; Murillo, M; Giovanini, L
2017-03-01
Moving horizon estimation is an efficient technique to estimate states and parameters of constrained dynamical systems. It relies on the solution of a finite horizon optimization problem to compute the estimates, providing a natural framework to handle bounds and constraints on estimates, noises and parameters. However, the approximation of the arrival cost and its updating mechanism are an active research topic. The arrival cost is very important because it provides a mean to incorporate information from previous measurements to the current estimates and it is difficult to estimate its true value. In this work, we exploit the features of adaptive estimation methods to update the parameters of the arrival cost. We show that, having a better approximation of the arrival cost, the size of the optimization problem can be significantly reduced guaranteeing the stability and convergence of the estimates. These properties are illustrated through simulation studies.
Multi-RTM-based Radiance Assimilation to Improve Snow Estimates
NASA Astrophysics Data System (ADS)
Kwon, Y.; Zhao, L.; Hoar, T. J.; Yang, Z. L.; Toure, A. M.
2015-12-01
Data assimilation of microwave brightness temperature (TB) observations (i.e., radiance assimilation (RA)) has been proven to improve snowpack characterization at relatively small scales. However, large-scale applications of RA require a considerable amount of further efforts. Our objective in this study is to explore global-scale snow RA. In a RA scheme, a radiative transfer model (RTM) is an observational operator predicting TB; therefore, the quality of the assimilation results may strongly depend upon the RTM used as well as the land surface model (LSM). Several existing RTMs show different sensitivities to snowpack properties and thus they simulate significantly different TB. At the global scale, snow physical properties vary widely with local climate conditions. No single RTM has been shown to be able to accurately reproduce the observed TB for such a wide range of snow conditions. In this study, therefore, we hypothesize that snow estimates using a microwave RA scheme can be improved through the use of multiple RTMs (i.e., multi-RTM-based approaches). As a first step, here we use two snowpack RTMs, i.e., the Dense Media Radiative Transfer-Multi Layers model (DMRT-ML) and the Microwave Emission Model for Layered Snowpacks (MEMLS). The Community Land Model version 4 (CLM4) is used to simulate snow dynamics. The assimilation process is conducted by the Data Assimilation Research Testbed (DART), which is a community facility developed by the National Center for Atmospheric Research (NCAR) for ensemble-based data assimilation studies. In the RA experiments, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB at 18.7 and 36.5 GHz vertical polarization channels are assimilated into the RA system using the ensemble adjustment Kalman filter. The results are evaluated using the Canadian Meteorological Centre (CMC) daily snow depth, the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction, and in-situ snowpack and river
Using Satellite Rainfall Estimates to Improve Climate Services in Africa
NASA Astrophysics Data System (ADS)
Dinku, T.
2012-12-01
Climate variability and change pose serious challenges to sustainable development in Africa. The recent famine crisis in Horn of Africa is yet again another evidence of how fluctuations in the climate can destroy lives and livelihoods. Building resilience against the negative impacts of climate and maximizing the benefits from favorable conditions will require mainstreaming climate issues into development policy, planning and practice at different levels. The availability of decision-relevant climate information at different levels is very critical. The number and quality of weather stations in many part of Africa, however, has been declining. The available stations are unevenly distributed with most of the stations located along the main roads. This imposes severe limitations to the availability of climate information and services to rural communities where these services are needed most. Where observations are taken, they suffer from gaps and poor quality and are often unavailable beyond the respective national meteorological services. Combining available local observation with satellite products, making data and products available through the Internet, and training the user community to understand and use climate information will help to alleviate these problems. Improving data availability involves organizing and cleaning all available national station observations and combining them with satellite rainfall estimates. The main advantage of the satellite products is the excellent spatial coverage at increasingly improved spatial and temporal resolutions. This approach has been implemented in Ethiopia and Tanzania, and it is in the process being implemented in West Africa. The main outputs include: 1. Thirty-year times series of combined satellite-gauge rainfall time series at 10-daily time scale 10-km spatial resolution; 2. An array of user-specific products for climate analysis and monitoring; 3. An online facility providing user-friendly tools for
Improving estimates of air pollution exposure through ubiquitous sensing technologies
de Nazelle, Audrey; Seto, Edmund; Donaire-Gonzalez, David; Mendez, Michelle; Matamala, Jaume; Nieuwenhuijsen, Mark J; Jerrett, Michael
2013-01-01
Traditional methods of exposure assessment in epidemiological studies often fail to integrate important information on activity patterns, which may lead to bias, loss of statistical power or both in health effects estimates. Novel sensing technologies integrated with mobile phones offer potential to reduce exposure measurement error. We sought to demonstrate the usability and relevance of the CalFit smartphone technology to track person-level time, geographic location, and physical activity patterns for improved air pollution exposure assessment. We deployed CalFit-equipped smartphones in a free living-population of 36 subjects in Barcelona, Spain. Information obtained on physical activity and geographic location was linked to space-time air pollution mapping. For instance, we found on average travel activities accounted for 6% of people’s time and 24% of their daily inhaled NO2. Due to the large number of mobile phone users, this technology potentially provides an unobtrusive means of collecting epidemiologic exposure data at low cost. PMID:23416743
Improved Estimate of Phobos Secular Acceleration from MOLA Observations
NASA Technical Reports Server (NTRS)
Bills, Bruce; Neumann, Gregory; Smith, David; Zuber, Maria
2004-01-01
We report on new observations of the orbital position of Phobos, and use them to obtain a new and improved estimate of the rate of secular acceleration in longitude due to tidal dissipation within Mars. Phobos is the inner-most natural satellite of Mars, and one of the few natural satellites in the solar system with orbital period shorter than the rotation period of its primary. As a result, any departure from a perfect elastic response by Mars in the tides raised on it by Phobos will cause a transfer of angular momentum from the orbit of Phobos to the spin of Mars. Since its discovery in 1877, Phobos has completed over 145,500 orbits, and has one of the best studied orbits in the solar system, with over 6000 earth-based astrometric observations, and over 300 spacecraft observations. As early as 1945, Sharpless noted that there is a secular acceleration in mean longitude, with rate (1.88 + 0.25) 10(exp -3) degrees per square year. In preparation for the 1989 Russian spacecraft mission to Phobos, considerable work was done compiling past observations, and refining the orbital model. All of the published estimates from that era are in good agreement. A typical solution (Jacobson et al., 1989) yields (1.249 + 0.018) 10(exp -3) degrees per square year. The MOLA instrument on MGS is a laser altimeter, and was designed to measure the topography of Mars. However, it has also been used to make observations of the position of Phobos. In 1998, a direct range measurement was made, which indicated that Phobos was slightly ahead of the predicted position. The MOLA detector views the surface of Mars in a narrow field of view, at 1064 nanometer wavelength, and can detect shadows cast by Phobos on the surface of Mars. We have found 15 such serendipitous shadow transit events over the interval from xx to xx, and all of them show Phobos to be ahead of schedule, and getting progressively farther ahead of the predicted position. In contrast, the cross-track positions are quite close
de Gunst, M C; Luebeck, E G
1998-03-01
The problem of finding the number and size distribution of cell clusters that grow in an organ or tissue from observations of the number and sizes of transections of such cell clusters in a planar section is considered. This problem is closely related to the well-known corpuscle or Wicksell problem in stereology, which deals with transections of spherical objects. However, for most biological applications, it is unrealistic to assume that cell clusters have spherical shapes since they may grow in various ways. We therefore propose a method that allows for more general spatial configurations of the clusters. Under the assumption that a parametric growth model is available for the number and sizes of the cell clusters, expressions are obtained for the probability distributions of the number and sizes of transections of the clusters in a section plane for each point in time. These expressions contain coefficients that are independent of the parametric growth model and time but depend on which model is chosen for the configuration of the cell clusters in space. These results enable us to perform estimation of the parameters of the growth model by maximum likelihood directly on the data instead of having to deal with the inverse problem of estimation of three-dimensional quantities based on two-dimensional data. For realistic choices of the configuration model, it will not be possible to obtain the exact values of the coefficients, but they can easily be approximated by means of computer simulations of the spatial configuration. Monte Carlo simulations were performed to approximate the coefficients for two particular spatial configuration models. For these two configuration models, the proposed method is applied to data on preneoplastic minifoci in rat liver under the assumption of a two-event model of carcinogenesis as the parametric growth model.
Arpino, Bruno; Cannas, Massimo
2016-05-30
This article focuses on the implementation of propensity score matching for clustered data. Different approaches to reduce bias due to cluster-level confounders are considered and compared using Monte Carlo simulations. We investigated methods that exploit the clustered structure of the data in two ways: in the estimation of the propensity score model (through the inclusion of fixed or random effects) or in the implementation of the matching algorithm. In addition to a pure within-cluster matching, we also assessed the performance of a new approach, 'preferential' within-cluster matching. This approach first searches for control units to be matched to treated units within the same cluster. If matching is not possible within-cluster, then the algorithm searches in other clusters. All considered approaches successfully reduced the bias due to the omission of a cluster-level confounder. The preferential within-cluster matching approach, combining the advantages of within-cluster and between-cluster matching, showed a relatively good performance both in the presence of big and small clusters, and it was often the best method. An important advantage of this approach is that it reduces the number of unmatched units as compared with a pure within-cluster matching. We applied these methods to the estimation of the effect of caesarean section on the Apgar score using birth register data. Copyright © 2016 John Wiley & Sons, Ltd.
NASA Technical Reports Server (NTRS)
Lange, A. E.
1997-01-01
Measurements of the peculiar velocities of galaxy clusters with respect to the Hubble flow allow the determination of the gravitational field from all matter in the universe, not just the visible component. The Sunyaev-Zel'dovich (SZ) effect (the inverse-Compton scattering of cosmic microwave background photons by the hot gas in clusters of galaxies) allows these velocities to be measured without the use of empirical distance indicators. Additionally, because the magnitude of the SZ effect is independent of redshift, the technique can be used to measure velocities out to the epoch of cluster formation. The SZ technique requires a determination of the temperature of the hot cluster gas from X-ray observations, and measurements of the SZ effect at millimeter wavelengths to separate the contribution from the thermal motions within the gas from that of the cluster peculiax velocity. We have constructed a bolometric receiver, the Sunyaev-Zel'dovich Infrared Experiment, specifically to make measurements of the SZ effect at millimeter wavelengths in order to apply the SZ technique to peculiar velocity measurements. This receiver has already been used to set limits to the peculiar velocities of two galaxy clusters at z approx. 0.2. As a test of the SZ technique, the double cluster pair Abell 222 and 223 was selected for observation. Measurements of the redshifts of the two components suggest that, if the clusters are gravitationally bound, they should exhibit a relative velocity of 10OO km/ s, well above the expected precision of 200 km/ s (set by astrophysical confusion) that is expected from the SZ method. The temperature can be measured from ASCA data which we obtained for this cluster pair. However, in order to ensure that the temperature estimate from the ASCA data was not dominated by cooling flows within the cluster, we requested ROSAT HRI observations of this cluster pair. Analysis of the X-ray properties of the cluster pair is continuing by combining the ROSAT
An improved global dynamic routing strategy for scale-free network with tunable clustering
NASA Astrophysics Data System (ADS)
Sun, Lina; Huang, Ning; Zhang, Yue; Bai, Yannan
2016-08-01
An efficient routing strategy can deliver packets quickly to improve the network capacity. Node congestion and transmission path length are inevitable real-time factors for a good routing strategy. Existing dynamic global routing strategies only consider the congestion of neighbor nodes and the shortest path, which ignores other key nodes’ congestion on the path. With the development of detection methods and techniques, global traffic information is readily available and important for the routing choice. Reasonable use of this information can effectively improve the network routing. So, an improved global dynamic routing strategy is proposed, which considers the congestion of all nodes on the shortest path and incorporates the waiting time of the most congested node into the path. We investigate the effectiveness of the proposed routing for scale-free network with different clustering coefficients. The shortest path routing strategy and the traffic awareness routing strategy only considering the waiting time of neighbor node are analyzed comparatively. Simulation results show that network capacity is greatly enhanced compared with the shortest path; congestion state increase is relatively slow compared with the traffic awareness routing strategy. Clustering coefficient increase will not only reduce the network throughput, but also result in transmission average path length increase for scale-free network with tunable clustering. The proposed routing is favorable to ease network congestion and network routing strategy design.
NASA Astrophysics Data System (ADS)
Xi, Yakun; Zhang, Cheng
2017-03-01
We show that one can obtain improved L 4 geodesic restriction estimates for eigenfunctions on compact Riemannian surfaces with nonpositive curvature. We achieve this by adapting Sogge's strategy in (Improved critical eigenfunction estimates on manifolds of nonpositive curvature, Preprint). We first combine the improved L 2 restriction estimate of Blair and Sogge (Concerning Toponogov's Theorem and logarithmic improvement of estimates of eigenfunctions, Preprint) and the classical improved {L^∞} estimate of Bérard to obtain an improved weak-type L 4 restriction estimate. We then upgrade this weak estimate to a strong one by using the improved Lorentz space estimate of Bak and Seeger (Math Res Lett 18(4):767-781, 2011). This estimate improves the L 4 restriction estimate of Burq et al. (Duke Math J 138:445-486, 2007) and Hu (Forum Math 6:1021-1052, 2009) by a power of {(log logλ)^{-1}}. Moreover, in the case of compact hyperbolic surfaces, we obtain further improvements in terms of {(logλ)^{-1}} by applying the ideas from (Chen and Sogge, Commun Math Phys 329(3):435-459, 2014) and (Blair and Sogge, Concerning Toponogov's Theorem and logarithmic improvement of estimates of eigenfunctions, Preprint). We are able to compute various constants that appeared in (Chen and Sogge, Commun Math Phys 329(3):435-459, 2014) explicitly, by proving detailed oscillatory integral estimates and lifting calculations to the universal cover H^2.
Paireau, Juliette; Girond, Florian; Collard, Jean-Marc; Maïnassara, Halima B.; Jusot, Jean-François
2012-01-01
Background Meningococcal meningitis is a major health problem in the “African Meningitis Belt” where recurrent epidemics occur during the hot, dry season. In Niger, a central country belonging to the Meningitis Belt, reported meningitis cases varied between 1,000 and 13,000 from 2003 to 2009, with a case-fatality rate of 5–15%. Methodology/Principal Findings In order to gain insight in the epidemiology of meningococcal meningitis in Niger and to improve control strategies, the emergence of the epidemics and their diffusion patterns at a fine spatial scale have been investigated. A statistical analysis of the spatio-temporal distribution of confirmed meningococcal meningitis cases was performed between 2002 and 2009, based on health centre catchment areas (HCCAs) as spatial units. Anselin's local Moran's I test for spatial autocorrelation and Kulldorff's spatial scan statistic were used to identify spatial and spatio-temporal clusters of cases. Spatial clusters were detected every year and most frequently occurred within nine southern districts. Clusters most often encompassed few HCCAs within a district, without expanding to the entire district. Besides, strong intra-district heterogeneity and inter-annual variability in the spatio-temporal epidemic patterns were observed. To further investigate the benefit of using a finer spatial scale for surveillance and disease control, we compared timeliness of epidemic detection at the HCCA level versus district level and showed that a decision based on threshold estimated at the HCCA level may lead to earlier detection of outbreaks. Conclusions/Significance Our findings provide an evidence-based approach to improve control of meningitis in sub-Saharan Africa. First, they can assist public health authorities in Niger to better adjust allocation of resources (antibiotics, rapid diagnostic tests and medical staff). Then, this spatio-temporal analysis showed that surveillance at a finer spatial scale (HCCA) would be more
Estimating Treatment Effects via Multilevel Matching within Homogenous Groups of Clusters
ERIC Educational Resources Information Center
Steiner, Peter M.; Kim, Jee-Seon
2015-01-01
Despite the popularity of propensity score (PS) techniques they are not yet well studied for matching multilevel data where selection into treatment takes place among level-one units within clusters. This paper suggests a PS matching strategy that tries to avoid the disadvantages of within- and across-cluster matching. The idea is to first…
Improving statistical keyword detection in short texts: Entropic and clustering approaches
NASA Astrophysics Data System (ADS)
Carretero-Campos, C.; Bernaola-Galván, P.; Coronado, A. V.; Carpena, P.
2013-03-01
In the last years, two successful approaches have been introduced to tackle the problem of statistical keyword detection in a text without the use of external information: (i) The entropic approach, where Shannon’s entropy of information is used to quantify the information content of the sequence of occurrences of each word in the text; and (ii) The clustering approach, which links the heterogeneity of the spatial distribution of a word in the text (clustering) with its relevance. In this paper, first we present some modifications to both techniques which improve their results. Then, we propose new metrics to evaluate the performance of keyword detectors based specifically on the needs of a typical user, and we employ them to find out which approach performs better. Although both approaches work well in long texts, we obtain in general that measures based on word-clustering perform at least as well as the entropic measure, which needs a convenient partition of the text to be applied, such as chapters of a book. In the latter approach we also show that the partition of the text chosen affects strongly its results. Finally, we focus on short texts, a case of high practical importance, such as short reports, web pages, scientific articles, etc. We show that the performance of word-clustering measures is also good in generic short texts since these measures are able to discriminate better the degree of relevance of low frequency words than the entropic approach.
Bonci, T; Camomilla, V; Dumas, R; Chèze, L; Cappozzo, A
2015-11-26
When stereophotogrammetry and skin-markers are used, bone-pose estimation is jeopardised by the soft tissue artefact (STA). At marker-cluster level, this can be represented using a modal series of rigid (RT; translation and rotation) and non-rigid (NRT; homothety and scaling) geometrical transformations. The NRT has been found to be smaller than the RT and claimed to have a limited impact on bone-pose estimation. This study aims to investigate this matter and comparatively assessing the propagation of both STA components to bone-pose estimate, using different numbers of markers. Twelve skin-markers distributed over the anterior aspect of a thigh were considered and STA time functions were generated for each of them, as plausibly occurs during walking, using an ad hoc model and represented through the geometrical transformations. Using marker-clusters made of four to 12 markers affected by these STAs, and a Procrustes superimposition approach, bone-pose and the relevant accuracy were estimated. This was done also for a selected four marker-cluster affected by STAs randomly simulated by modifying the original STA NRT component, so that its energy fell in the range 30-90% of total STA energy. The pose error, which slightly decreased while increasing the number of markers in the marker-cluster, was independent from the NRT amplitude, and was always null when the RT component was removed. It was thus demonstrated that only the RT component impacts pose estimation accuracy and should thus be accounted for when designing algorithms aimed at compensating for STA.
Using Smartphone Sensors for Improving Energy Expenditure Estimation.
Pande, Amit; Zhu, Jindan; Das, Aveek K; Zeng, Yunze; Mohapatra, Prasant; Han, Jay J
2015-01-01
Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.
Using Smartphone Sensors for Improving Energy Expenditure Estimation
Zhu, Jindan; Das, Aveek K.; Zeng, Yunze; Mohapatra, Prasant; Han, Jay J.
2015-01-01
Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings. PMID:27170901
Improving Estimation Accuracy of Aggregate Queries on Data Cubes
Pourabbas, Elaheh; Shoshani, Arie
2008-08-15
In this paper, we investigate the problem of estimation of a target database from summary databases derived from a base data cube. We show that such estimates can be derived by choosing a primary database which uses a proxy database to estimate the results. This technique is common in statistics, but an important issue we are addressing is the accuracy of these estimates. Specifically, given multiple primary and multiple proxy databases, that share the same summary measure, the problem is how to select the primary and proxy databases that will generate the most accurate target database estimation possible. We propose an algorithmic approach for determining the steps to select or compute the source databases from multiple summary databases, which makes use of the principles of information entropy. We show that the source databases with the largest number of cells in common provide the more accurate estimates. We prove that this is consistent with maximizing the entropy. We provide some experimental results on the accuracy of the target database estimation in order to verify our results.
Gorfine, Malka; Bordo, Nadia; Hsu, Li
2017-01-01
SummaryConsider a popular case-control family study where individuals with a disease under study (case probands) and individuals who do not have the disease (control probands) are randomly sampled from a well-defined population. Possibly right-censored age at onset and disease status are observed for both probands and their relatives. For example, case probands are men diagnosed with prostate cancer, control probands are men free of prostate cancer, and the prostate cancer history of the fathers of the probands is also collected. Inherited genetic susceptibility, shared environment, and common behavior lead to correlation among the outcomes within a family. In this article, a novel nonparametric estimator of the marginal survival function is provided. The estimator is defined in the presence of intra-cluster dependence, and is based on consistent smoothed kernel estimators of conditional survival functions. By simulation, it is shown that the proposed estimator performs very well in terms of bias. The utility of the estimator is illustrated by the analysis of case-control family data of early onset prostate cancer. To our knowledge, this is the first article that provides a fully nonparametric marginal survival estimator based on case-control clustered age-at-onset data.
Improved estimation of random vibration loads in launch vehicles
NASA Technical Reports Server (NTRS)
Mehta, R.; Erwin, E.; Suryanarayan, S.; Krishna, Murali M. R.
1993-01-01
Random vibration induced load is an important component of the total design load environment for payload and launch vehicle components and their support structures. The current approach to random vibration load estimation is based, particularly at the preliminary design stage, on the use of Miles' equation which assumes a single degree-of-freedom (DOF) system and white noise excitation. This paper examines the implications of the use of multi-DOF system models and response calculation based on numerical integration using the actual excitation spectra for random vibration load estimation. The analytical study presented considers a two-DOF system and brings out the effects of modal mass, damping and frequency ratios on the random vibration load factor. The results indicate that load estimates based on the Miles' equation can be significantly different from the more accurate estimates based on multi-DOF models.
Bayesian fusion algorithm for improved oscillometric blood pressure estimation.
Forouzanfar, Mohamad; Dajani, Hilmi R; Groza, Voicu Z; Bolic, Miodrag; Rajan, Sreeraman; Batkin, Izmail
2016-11-01
A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively.
Zheng, Jian-Ting; Wang, Sheng-Lan; Yang, Ke-Qian
2007-09-01
Streptomyces venezuelae ISP5230 produces a group of jadomycin congeners with cytotoxic activities. To improve jadomycin fermentation process, a genetic engineering strategy was designed to replace a 3.4-kb regulatory region of jad gene cluster that contains four regulatory genes (3' end 272 bp of jadW2, jadW3, jadR2, and jadR1) and the native promoter upstream of jadJ (P(J)) with the ermEp* promoter sequence so that ermEp* drives the expression of the jadomycin biosynthetic genes from jadJ in the engineered strain. As expected, the mutant strain produced jadomycin B without ethanol treatment, and the yield increased to about twofold that of the stressed wild-type. These results indicated that manipulation of the regulation of a biosynthetic gene cluster is an effective strategy to increase product yield.
NASA Astrophysics Data System (ADS)
Schaan, Emmanuel; Takada, Masahiro; Spergel, David N.
2014-12-01
Naive estimates of the statistics of large-scale structure and weak lensing power spectrum measurements that include only Gaussian errors exaggerate their scientific impact. Nonlinear evolution and finite-volume effects are both significant sources of non-Gaussian covariance that reduce the ability of power spectrum measurements to constrain cosmological parameters. Using a halo model formalism, we derive an intuitive understanding of the various contributions to the covariance and show that our analytical treatment agrees with simulations. This approach enables an approximate derivation of a joint likelihood for the cluster number counts, the weak lensing power spectrum and the bispectrum. We show that this likelihood is a good description of the ray-tracing simulation. Since all of these observables are sensitive to the same finite-volume effects and contain information about the nonlinear evolution, a combined analysis recovers much of the "lost" information. For upcoming weak lensing surveys, we estimate that a joint analysis of power spectrum, number counts and bispectrum will produce an improvement of about 30-40% in determinations of the matter density and the scalar amplitude. This improvement is equivalent to doubling the survey area.
NASA Astrophysics Data System (ADS)
Wahyudi, Notodiputro, Khairil Anwar; Kurnia, Anang; Anisa, Rahma
2016-02-01
Empirical Best Linear Unbiased Prediction (EBLUP) is one of indirect estimating methods which used to estimate parameters of small areas. EBLUP methods works in using auxiliary variables of area while adding the area random effects. In estimating non-sampled area, the standard EBLUP can no longer be used due to no information of area random effects. To obtain more proper estimation methods for non sampled area, the standard EBLUP model has to be modified by adding cluster information. The aim of this research was to study clustering methods using factor analysis by means of simulation, provide better cluster information. The criteria used to evaluate the goodness of fit of the methods in the simulation study were the mean percentage of clustering accuracy. The results of the simulation study showed the use of factor analysis in clustering has increased the average percentage of accuracy particularly when using Ward method. The method was taken into account to estimate the per capita expenditures based on Small Area Estimation (SAE) techniques. The method was eventually used to estimate the per capita expenditures from SUSENAS and the quality of the estimates was measured by RMSE. This research has shown that the standard-modified EBLUP model provided with factor analysis better estimates when compared with standard EBLUP model and the standard-modified EBLUP without the factor analysis. Moreover, it was also shown that the clustering information is important in estimating non sampled area.
NASA Astrophysics Data System (ADS)
Priyatikanto, R.; Arifyanto, M. I.
2015-01-01
Stellar membership determination of an open cluster is an important process to do before further analysis. Basically, there are two classes of membership determination method: parametric and non-parametric. In this study, an alternative of non-parametric method based on Binned Kernel Density Estimation that accounts measurements errors (simply called BKDE- e) is proposed. This method is applied upon proper motions data to determine cluster's membership kinematically and estimate the average proper motions of the cluster. Monte Carlo simulations show that the average proper motions determination using this proposed method is statistically more accurate than ordinary Kernel Density Estimator (KDE). By including measurement errors in the calculation, the mode location from the resulting density estimate is less sensitive to non-physical or stochastic fluctuation as compared to ordinary KDE that excludes measurement errors. For the typical mean measurement error of 7 mas/yr, BKDE- e suppresses the potential of miscalculation by a factor of two compared to KDE. With median accuracy of about 93 %, BKDE- e method has comparable accuracy with respect to parametric method (modified Sanders algorithm). Application to real data from The Fourth USNO CCD Astrograph Catalog (UCAC4), especially to NGC 2682 is also performed. The mode of member stars distribution on Vector Point Diagram is located at μ α cos δ=-9.94±0.85 mas/yr and μ δ =-4.92±0.88 mas/yr. Although the BKDE- e performance does not overtake parametric approach, it serves a new view of doing membership analysis, expandable to astrometric and photometric data or even in binary cluster search.
Guan, Guijian; Zhang, Shuang-Yuan; Cai, Yongqing; Liu, Shuhua; Bharathi, M S; Low, Michelle; Yu, Yong; Xie, Jianping; Zheng, Yuangang; Zhang, Yong-Wei; Han, Ming-Yong
2014-06-01
An effective separation process is developed to remove free protein from the protein-protected gold clusters via co-precipitation with zinc hydroxide on their surface. After dialysis, the purified clusters exhibit an enhanced fluorescence for improved sensitive detection and selective visualization.
Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics
2016-01-01
Interpreting the potentially vast number of hypotheses generated by a shotgun proteomics experiment requires a valid and accurate procedure for assigning statistical confidence estimates to identified tandem mass spectra. Despite the crucial role such procedures play in most high-throughput proteomics experiments, the scientific literature has not reached a consensus about the best confidence estimation methodology. In this work, we evaluate, using theoretical and empirical analysis, four previously proposed protocols for estimating the false discovery rate (FDR) associated with a set of identified tandem mass spectra: two variants of the target-decoy competition protocol (TDC) of Elias and Gygi and two variants of the separate target-decoy search protocol of Käll et al. Our analysis reveals significant biases in the two separate target-decoy search protocols. Moreover, the one TDC protocol that provides an unbiased FDR estimate among the target PSMs does so at the cost of forfeiting a random subset of high-scoring spectrum identifications. We therefore propose the mix-max procedure to provide unbiased, accurate FDR estimates in the presence of well-calibrated scores. The method avoids biases associated with the two separate target-decoy search protocols and also avoids the propensity for target-decoy competition to discard a random subset of high-scoring target identifications. PMID:26152888
Li, Chunming; Xu, Chenyang; Anderson, Adam W; Gore, John C
2009-01-01
This paper presents a new energy minimization method for simultaneous tissue classification and bias field estimation of magnetic resonance (MR) images. We first derive an important characteristic of local image intensities--the intensities of different tissues within a neighborhood form separable clusters, and the center of each cluster can be well approximated by the product of the bias within the neighborhood and a tissue-dependent constant. We then introduce a coherent local intensity clustering (CLIC) criterion function as a metric to evaluate tissue classification and bias field estimation. An integration of this metric defines an energy on a bias field, membership functions of the tissues, and the parameters that approximate the true signal from the corresponding tissues. Thus, tissue classification and bias field estimation are simultaneously achieved by minimizing this energy. The smoothness of the derived optimal bias field is ensured by the spatially coherent nature of the CLIC criterion function. As a result, no extra effort is needed to smooth the bias field in our method. Moreover, the proposed algorithm is robust to the choice of initial conditions, thereby allowing fully automatic applications. Our algorithm has been applied to high field and ultra high field MR images with promising results.
The report discusses an EPA investigation of techniques to improve methods for estimating volatile organic compound (VOC) emissions from area sources. Using the automobile refinishing industry for a detailed area source case study, an emission estimation method is being developed...
Improved alternatives for estimating in-use material stocks.
Chen, Wei-Qiang; Graedel, T E
2015-03-03
Determinations of in-use material stocks are useful for exploring past patterns and future scenarios of materials use, for estimating end-of-life flows of materials, and thereby for guiding policies on recycling and sustainable management of materials. This is especially true when those determinations are conducted for individual products or product groups such as "automobiles" rather than general (and sometimes nebulous) sectors such as "transportation". We propose four alternatives to the existing top-down and bottom-up methods for estimating in-use material stocks, with the choice depending on the focus of the study and on the available data. We illustrate with aluminum use in automobiles the robustness of and consistencies and differences among these four alternatives and demonstrate that a suitable combination of the four methods permits estimation of the in-use stock of a material contained in all products employing that material, or in-use stocks of different materials contained in a particular product. Therefore, we anticipate the estimation in the future of in-use stocks for many materials in many products or product groups, for many regions, and for longer time periods, by taking advantage of methodologies that fully employ the detailed data sets now becoming available.
A novel ULA-based geometry for improving AOA estimation
NASA Astrophysics Data System (ADS)
Shirvani-Moghaddam, Shahriar; Akbari, Farida
2011-12-01
Due to relatively simple implementation, Uniform Linear Array (ULA) is a popular geometry for array signal processing. Despite this advantage, it does not have a uniform performance in all directions and Angle of Arrival (AOA) estimation performance degrades considerably in the angles close to endfire. In this article, a new configuration is proposed which can solve this problem. Proposed Array (PA) configuration adds two elements to the ULA in top and bottom of the array axis. By extending signal model of the ULA to the new proposed ULA-based array, AOA estimation performance has been compared in terms of angular accuracy and resolution threshold through two well-known AOA estimation algorithms, MUSIC and MVDR. In both algorithms, Root Mean Square Error (RMSE) of the detected angles descends as the input Signal to Noise Ratio (SNR) increases. Simulation results show that the proposed array geometry introduces uniform accurate performance and higher resolution in middle angles as well as border ones. The PA also presents less RMSE than the ULA in endfire directions. Therefore, the proposed array offers better performance for the border angles with almost the same array size and simplicity in both MUSIC and MVDR algorithms with respect to the conventional ULA. In addition, AOA estimation performance of the PA geometry is compared with two well-known 2D-array geometries: L-shape and V-shape, and acceptable results are obtained with equivalent or lower complexity.
Improved Uncertainty Quantification in Groundwater Flux Estimation Using GRACE
NASA Astrophysics Data System (ADS)
Reager, J. T., II; Rao, P.; Famiglietti, J. S.; Turmon, M.
2015-12-01
Groundwater change is difficult to monitor over large scales. One of the most successful approaches is in the remote sensing of time-variable gravity using NASA Gravity Recovery and Climate Experiment (GRACE) mission data, and successful case studies have created the opportunity to move towards a global groundwater monitoring framework for the world's largest aquifers. To achieve these estimates, several approximations are applied, including those in GRACE processing corrections, the formulation of the formal GRACE errors, destriping and signal recovery, and the numerical model estimation of snow water, surface water and soil moisture storage states used to isolate a groundwater component. A major weakness in these approaches is inconsistency: different studies have used different sources of primary and ancillary data, and may achieve different results based on alternative choices in these approximations. In this study, we present two cases of groundwater change estimation in California and the Colorado River basin, selected for their good data availability and varied climates. We achieve a robust numerical estimate of post-processing uncertainties resulting from land-surface model structural shortcomings and model resolution errors. Groundwater variations should demonstrate less variability than the overlying soil moisture state does, as groundwater has a longer memory of past events due to buffering by infiltration and drainage rate limits. We apply a model ensemble approach in a Bayesian framework constrained by the assumption of decreasing signal variability with depth in the soil column. We also discuss time variable errors vs. time constant errors, across-scale errors v. across-model errors, and error spectral content (across scales and across model). More robust uncertainty quantification for GRACE-based groundwater estimates would take all of these issues into account, allowing for more fair use in management applications and for better integration of GRACE
An improved scheduling algorithm for 3D cluster rendering with platform LSF
NASA Astrophysics Data System (ADS)
Xu, Wenli; Zhu, Yi; Zhang, Liping
2013-10-01
High-quality photorealistic rendering of 3D modeling needs powerful computing systems. On this demand highly efficient management of cluster resources develops fast to exert advantages. This paper is absorbed in the aim of how to improve the efficiency of 3D rendering tasks in cluster. It focuses research on a dynamic feedback load balance (DFLB) algorithm, the work principle of load sharing facility (LSF) and optimization of external scheduler plug-in. The algorithm can be applied into match and allocation phase of a scheduling cycle. Candidate hosts is prepared in sequence in match phase. And the scheduler makes allocation decisions for each job in allocation phase. With the dynamic mechanism, new weight is assigned to each candidate host for rearrangement. The most suitable one will be dispatched for rendering. A new plugin module of this algorithm has been designed and integrated into the internal scheduler. Simulation experiments demonstrate the ability of improved plugin module is superior to the default one for rendering tasks. It can help avoid load imbalance among servers, increase system throughput and improve system utilization.
Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy
2011-12-29
Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy Lushan Han1, Tim Finin1,2, Paul McNamee2, Anupam Joshi1 and Yelena Yesha1...Yesha, Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy , IEEE Transactions on Knowledge and Data Engineering, IEEE Computer...4. TITLE AND SUBTITLE Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c
NASA Astrophysics Data System (ADS)
Muda, Nora; Othman, Abdul Rahman
2015-10-01
The process of grouping a set of objects into classes of similar objects is called clustering. It divides a large group of observations into smaller groups so that the observations within each group are relatively similar and the observations in different groups are relatively dissimilar. In this study, an agglomerative method in hierarchical cluster analysis is chosen and clusters were constructed by using an average linkage technique. An average linkage technique requires distance between clusters, which is calculated based on the average distance between all pairs of points, one group with another group. In calculating the average distance, the distance will not be robust when there is an outlier. Therefore, the average distance in average linkage needs to be modified in order to overcome the problem of outlier. Therefore, the criteria of outlier detection based on MADn criteria is used and the average distance is recalculated without the outlier. Next, the distance in average linkage is calculated based on a modified one step M-estimator (MOM). The groups of cluster are presented in dendrogram graph. To evaluate the goodness of a modified distance in the average linkage clustering, the bootstrap analysis is conducted on the dendrogram graph and the bootstrap value (BP) are assessed for each branch in dendrogram that formed the group, to ensure the reliability of the branches constructed. This study found that the average linkage technique with modified distance is significantly superior than the usual average linkage technique, if there is an outlier. Both of these techniques are said to be similar if there is no outlier.
Improved Battery State Estimation Using Novel Sensing Techniques
NASA Astrophysics Data System (ADS)
Abdul Samad, Nassim
Lithium-ion batteries have been considered a great complement or substitute for gasoline engines due to their high energy and power density capabilities among other advantages. However, these types of energy storage devices are still yet not widespread, mainly because of their relatively high cost and safety issues, especially at elevated temperatures. This thesis extends existing methods of estimating critical battery states using model-based techniques augmented by real-time measurements from novel temperature and force sensors. Typically, temperature sensors are located near the edge of the battery, and away from the hottest core cell regions, which leads to slower response times and increased errors in the prediction of core temperatures. New sensor technology allows for flexible sensor placement at the cell surface between cells in a pack. This raises questions about the optimal locations of these sensors for best observability and temperature estimation. Using a validated model, which is developed and verified using experiments in laboratory fixtures that replicate vehicle pack conditions, it is shown that optimal sensor placement can lead to better and faster temperature estimation. Another equally important state is the state of health or the capacity fading of the cell. This thesis introduces a novel method of using force measurements for capacity fade estimation. Monitoring capacity is important for defining the range of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). Current capacity estimation techniques require a full discharge to monitor capacity. The proposed method can complement or replace current methods because it only requires a shallow discharge, which is especially useful in EVs and PHEVs. Using the accurate state estimation accomplished earlier, a method for downsizing a battery pack is shown to effectively reduce the number of cells in a pack without compromising safety. The influence on the battery performance (e
Hens, Niel; Beutels, Philippe; Leirs, Herwig; Reijniers, Jonas
2016-01-01
Diseases of humans and wildlife are typically tracked and studied through incidence, the number of new infections per time unit. Estimating incidence is not without difficulties, as asymptomatic infections, low sampling intervals and low sample sizes can introduce large estimation errors. After infection, biomarkers such as antibodies or pathogens often change predictably over time, and this temporal pattern can contain information about the time since infection that could improve incidence estimation. Antibody level and avidity have been used to estimate time since infection and to recreate incidence, but the errors on these estimates using currently existing methods are generally large. Using a semi-parametric model in a Bayesian framework, we introduce a method that allows the use of multiple sources of information (such as antibody level, pathogen presence in different organs, individual age, season) for estimating individual time since infection. When sufficient background data are available, this method can greatly improve incidence estimation, which we show using arenavirus infection in multimammate mice as a test case. The method performs well, especially compared to the situation in which seroconversion events between sampling sessions are the main data source. The possibility to implement several sources of information allows the use of data that are in many cases already available, which means that existing incidence data can be improved without the need for additional sampling efforts or laboratory assays. PMID:27177244
Improved covariance matrix estimators for weighted analysis of microarray data.
Astrand, Magnus; Mostad, Petter; Rudemo, Mats
2007-12-01
Empirical Bayes models have been shown to be powerful tools for identifying differentially expressed genes from gene expression microarray data. An example is the WAME model, where a global covariance matrix accounts for array-to-array correlations as well as differing variances between arrays. However, the existing method for estimating the covariance matrix is very computationally intensive and the estimator is biased when data contains many regulated genes. In this paper, two new methods for estimating the covariance matrix are proposed. The first method is a direct application of the EM algorithm for fitting the multivariate t-distribution of the WAME model. In the second method, a prior distribution for the log fold-change is added to the WAME model, and a discrete approximation is used for this prior. Both methods are evaluated using simulated and real data. The first method shows equal performance compared to the existing method in terms of bias and variability, but is superior in terms of computer time. For large data sets (>15 arrays), the second method also shows superior computer run time. Moreover, for simulated data with regulated genes the second method greatly reduces the bias. With the proposed methods it is possible to apply the WAME model to large data sets with reasonable computer run times. The second method shows a small bias for simulated data, but appears to have a larger bias for real data with many regulated genes.
Uncertainty Estimation Improves Energy Measurement and Verification Procedures
Walter, Travis; Price, Phillip N.; Sohn, Michael D.
2014-05-14
Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the baseline energy use). Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions. However, estimation of uncertainty is essential for weighing the risks of investing in retrofits. Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.
Recent Improvements in Estimating Convective and Stratiform Rainfall in Amazonia
NASA Technical Reports Server (NTRS)
Negri, Andrew J.
1999-01-01
In this paper we present results from the application of a satellite infrared (IR) technique for estimating rainfall over northern South America. Our main objectives are to examine the diurnal variability of rainfall and to investigate the relative contributions from the convective and stratiform components. We apply the technique of Anagnostou et al (1999). In simple functional form, the estimated rain area A(sub rain) may be expressed as: A(sub rain) = f(A(sub mode),T(sub mode)), where T(sub mode) is the mode temperature of a cloud defined by 253 K, and A(sub mode) is the area encompassed by T(sub mode). The technique was trained by a regression between coincident microwave estimates from the Goddard Profiling (GPROF) algorithm (Kummerow et al, 1996) applied to SSM/I data and GOES IR (11 microns) observations. The apportionment of the rainfall into convective and stratiform components is based on the microwave technique described by Anagnostou and Kummerow (1997). The convective area from this technique was regressed against an IR structure parameter (the Convective Index) defined by Anagnostou et al (1999). Finally, rainrates are assigned to the Am.de proportional to (253-temperature), with different rates for the convective and stratiform
RSQRT: AN HEURISTIC FOR ESTIMATING THE NUMBER OF CLUSTERS TO REPORT.
Carlis, John; Bruso, Kelsey
2012-03-01
Clustering can be a valuable tool for analyzing large datasets, such as in e-commerce applications. Anyone who clusters must choose how many item clusters, K, to report. Unfortunately, one must guess at K or some related parameter. Elsewhere we introduced a strongly-supported heuristic, RSQRT, which predicts K as a function of the attribute or item count, depending on attribute scales. We conducted a second analysis where we sought confirmation of the heuristic, analyzing data sets from theUCImachine learning benchmark repository. For the 25 studies where sufficient detail was available, we again found strong support. Also, in a side-by-side comparison of 28 studies, RSQRT best-predicted K and the Bayesian information criterion (BIC) predicted K are the same. RSQRT has a lower cost of O(log log n) versus O(n(2)) for BIC, and is more widely applicable. Using RSQRT prospectively could be much better than merely guessing.
Global Water Resources Under Future Changes: Toward an Improved Estimation
NASA Astrophysics Data System (ADS)
Islam, M.; Agata, Y.; Hanasaki, N.; Kanae, S.; Oki, T.
2005-05-01
Global water resources availability in the 21st century is going to be an important concern. Despite its international recognition, however, until now there are very limited global estimates of water resources, which considered the geographical linkage between water supply and demand, defined by runoff and its passage through river network. The available studies are again insufficient due to reasons like different approaches in defining water scarcity, simply based on annual average figures without considering the inter-annual or seasonal variability, absence of the inclusion of virtual water trading, etc. In this study, global water resources under future climate change associated with several socio-economic factors were estimated varying over both temporal and spatial scale. Global runoff data was derived from several land surface models under the GSWP2 (Global Soil Wetness Project) project, which was further processed through TRIP (Total Runoff Integrated Pathways) river routing model to produce a 0.5x0.5 degree grid based figure. Water abstraction was estimated for the same spatial resolution for three sectors as domestic, industrial and agriculture. GCM outputs from CCSR and MRI were collected to predict the runoff changes. Socio-economic factors like population and GDP growth, affected mostly the demand part. Instead of simply looking at annual figures, monthly figures for both supply and demand was considered. For an average year, such a seasonal variability can affect the crop yield significantly. In other case, inter-annual variability of runoff can cause for an absolute drought condition. To account for vulnerabilities of a region to future changes, both inter-annual and seasonal effects were thus considered. At present, the study assumed the future agricultural water uses to be unchanged under climatic changes. In this connection, EPIC model is underway to use for estimating future agricultural water demand under climatic changes on a monthly basis. From
Improved fire radiative energy estimation in high latitude ecosystems
NASA Astrophysics Data System (ADS)
Melchiorre, A.; Boschetti, L.
2014-12-01
Scientists, land managers, and policy makers are facing new challenges as fire regimes are evolving as a result of climate change (Westerling et al. 2006). In high latitudes fires are increasing in number and size as temperatures increase and precipitation decreases (Kasischke and Turetsky 2006). Peatlands, like the large complexes in the Alaskan tundra, are burning more frequently and severely as a result of these changes, releasing large amounts of greenhouse gases. Remotely sensed data are routinely used to monitor the location of active fires and the extent of burned areas, but they are not sensitive to the depth of the organic soil layer combusted, resulting in underestimation of peatland greenhouse gas emissions when employing the conventional 'bottom up' approach (Seiler and Crutzen 1980). An alternative approach would be the direct estimation of the biomass burned from the energy released by the fire (Fire Radiative Energy, FRE) (Wooster et al. 2003). Previous works (Boschetti and Roy 2009; Kumar et al. 2011) showed that the sampling interval of polar orbiting satellite systems severely limits the accuracy of the FRE in tropical ecosystems (up to four overpasses a day with MODIS), but because of the convergence of the orbits, more observations are available at higher latitudes. In this work, we used a combination of MODIS thermal data and Landsat optical data for the estimation of biomass burned in peatland ecosystems. First, the global MODIS active fire detection algorithm (Giglio et al. 2003) was modified, adapting the temperature thresholds to maximize the number of detections in boreal regions. Then, following the approach proposed by Boschetti and Roy (2009), the FRP point estimations were interpolated in time and space to cover the full temporal and spatial extent of the burned area, mapped with Landsat5 TM data. The methodology was tested on a large burned area in Alaska, and the results compared to published field measurements (Turetsky et al. 2011).
The CluTim algorithm: an improvement on the impact parameter estimates
NASA Astrophysics Data System (ADS)
Chiarello, G.; Chiri, C.; Cocciolo, G.; Corvaglia, A.; Grancagnolo, F.; Miccoli, A.; Panareo, M.; Pinto, C.; Pepino, A.; Spedicato, M.; Tassielli, G. F.
2017-03-01
A Drift Chamber is a detector used in high energy physics experiments for determining charged particles trajectories. The signal pulses from all the wires are then collected and the particle trajectory is tracked assuming that the distances of closest approach (the impact parameter) between the particle trajectory and the wires coincide with the distance between the cluster ions generated by the particle and the wire closer to it. The widespread use of helium based gas mixtures, which produces a low ionization clusters density (12 cluster/cm in a 90/10 helium/iso-butane mixture), introduces a sensible bias in the impact parameter assumption, particularly for short impact parameters and small cell drift chambers. Recently, an alternative track reconstruction (Cluster Counting/Timing) technique has been proposed, which consists in measuring the arrival times on the wires of each individual ionization cluster and combining these times to get a bias free estimate of the impact parameter. However, in order to efficiently exploiting the cluster timing technique, it is necessary to have read-out interfaces capable of processing a large quantity of high speed signals. We describe the design of a read-out board capable of acquiring the information coming from a fast digitization of the signals generated in a drift chamber and the algorithm for identifying the individual ionization pulse peaks and recording their time and amplitude.
Sun, Jinwei; Wu, Jiabing; Guan, Dexin; Yao, Fuqi; Yuan, Fenghui; Wang, Anzhi; Jin, Changjie
2014-01-01
Leaf respiration is an important component of carbon exchange in terrestrial ecosystems, and estimates of leaf respiration directly affect the accuracy of ecosystem carbon budgets. Leaf respiration is inhibited by light; therefore, gross primary production (GPP) will be overestimated if the reduction in leaf respiration by light is ignored. However, few studies have quantified GPP overestimation with respect to the degree of light inhibition in forest ecosystems. To determine the effect of light inhibition of leaf respiration on GPP estimation, we assessed the variation in leaf respiration of seedlings of the dominant tree species in an old mixed temperate forest with different photosynthetically active radiation levels using the Laisk method. Canopy respiration was estimated by combining the effect of light inhibition on leaf respiration of these species with within-canopy radiation. Leaf respiration decreased exponentially with an increase in light intensity. Canopy respiration and GPP were overestimated by approximately 20.4% and 4.6%, respectively, when leaf respiration reduction in light was ignored compared with the values obtained when light inhibition of leaf respiration was considered. This study indicates that accurate estimates of daytime ecosystem respiration are needed for the accurate evaluation of carbon budgets in temperate forests. In addition, this study provides a valuable approach to accurately estimate GPP by considering leaf respiration reduction in light in other ecosystems.
Sample Size Estimation in Cluster Randomized Educational Trials: An Empirical Bayes Approach
ERIC Educational Resources Information Center
Rotondi, Michael A.; Donner, Allan
2009-01-01
The educational field has now accumulated an extensive literature reporting on values of the intraclass correlation coefficient, a parameter essential to determining the required size of a planned cluster randomized trial. We propose here a simple simulation-based approach including all relevant information that can facilitate this task. An…
Improved Speech Coding Based on Open-Loop Parameter Estimation
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Chen, Ya-Chin; Longman, Richard W.
2000-01-01
A nonlinear optimization algorithm for linear predictive speech coding was developed early that not only optimizes the linear model coefficients for the open loop predictor, but does the optimization including the effects of quantization of the transmitted residual. It also simultaneously optimizes the quantization levels used for each speech segment. In this paper, we present an improved method for initialization of this nonlinear algorithm, and demonstrate substantial improvements in performance. In addition, the new procedure produces monotonically improving speech quality with increasing numbers of bits used in the transmitted error residual. Examples of speech encoding and decoding are given for 8 speech segments and signal to noise levels as high as 47 dB are produced. As in typical linear predictive coding, the optimization is done on the open loop speech analysis model. Here we demonstrate that minimizing the error of the closed loop speech reconstruction, instead of the simpler open loop optimization, is likely to produce negligible improvement in speech quality. The examples suggest that the algorithm here is close to giving the best performance obtainable from a linear model, for the chosen order with the chosen number of bits for the codebook.
Improving Mantel-Haenszel DIF Estimation through Bayesian Updating
ERIC Educational Resources Information Center
Zwick, Rebecca; Ye, Lei; Isham, Steven
2012-01-01
This study demonstrates how the stability of Mantel-Haenszel (MH) DIF (differential item functioning) methods can be improved by integrating information across multiple test administrations using Bayesian updating (BU). The authors conducted a simulation that showed that this approach, which is based on earlier work by Zwick, Thayer, and Lewis,…
Improving Lidar Turbulence Estimates for Wind Energy: Preprint
Newman, Jennifer; Clifton, Andrew; Churchfield, Matthew; Klein, Petra
2016-10-01
Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.
Estimating the Power Characteristics of Clusters of Large Offshore Wind Farms
NASA Astrophysics Data System (ADS)
Drew, D.; Barlow, J. F.; Coceal, O.; Coker, P.; Brayshaw, D.; Lenaghan, D.
2014-12-01
The next phase of offshore wind projects in the UK focuses on the development of very large wind farms clustered within several allocated zones. However, this change in the distribution of wind capacity brings uncertainty for the operational planning of the power system. Firstly, there are concerns that concentrating large amounts of capacity in one area could reduce some of the benefits seen by spatially dispersing the turbines, such as the smoothing of the power generation variability. Secondly, wind farms of the scale planned are likely to influence the boundary layer sufficiently to impact the performance of adjacent farms, therefore the power generation characteristics of the clusters are largely unknown. The aim of this study is to use the Weather Research and Forecasting (WRF) model to investigate the power output of a cluster of offshore wind farms for a range of extreme events, taking into account the wake effects of the individual turbines and the neighbouring farms. Each wind farm in the cluster is represented as an elevated momentum sink and a source of turbulent kinetic energy using the WRF Wind Farm Parameterization. The research focuses on the Dogger Bank zone (located in the North Sea approximately 125 km off the East coast of the UK), which could have 7.2 GW of installed capacity across six separate wind farms. For this site, a 33 year reanalysis data set (MERRA, from NASA-GMAO) has been used to identify a series of extreme event case studies. These are characterised by either periods of persistent low (or high) wind speeds, or by rapid changes in power output. The latter could be caused by small changes in the wind speed inducing large changes in power output, very high winds prompting turbine shut down, or a change in the wind direction which shifts the wake effects of the neighbouring farms in the cluster and therefore changes the wind resource available.
Covariance specification and estimation to improve top-down Green House Gas emission estimates
NASA Astrophysics Data System (ADS)
Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Whetstone, J. R.
2015-12-01
The National Institute of Standards and Technology (NIST) operates the North-East Corridor (NEC) project and the Indianapolis Flux Experiment (INFLUX) in order to develop measurement methods to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties in urban domains using a top down inversion method. Top down inversion updates prior knowledge using observations in a Bayesian way. One primary consideration in a Bayesian inversion framework is the covariance structure of (1) the emission prior residuals and (2) the observation residuals (i.e. the difference between observations and model predicted observations). These covariance matrices are respectively referred to as the prior covariance matrix and the model-data mismatch covariance matrix. It is known that the choice of these covariances can have large effect on estimates. The main objective of this work is to determine the impact of different covariance models on inversion estimates and their associated uncertainties in urban domains. We use a pseudo-data Bayesian inversion framework using footprints (i.e. sensitivities of tower measurements of GHGs to surface emissions) and emission priors (based on Hestia project to quantify fossil-fuel emissions) to estimate posterior emissions using different covariance schemes. The posterior emission estimates and uncertainties are compared to the hypothetical truth. We find that, if we correctly specify spatial variability and spatio-temporal variability in prior and model-data mismatch covariances respectively, then we can compute more accurate posterior estimates. We discuss few covariance models to introduce space-time interacting mismatches along with estimation of the involved parameters. We then compare several candidate prior spatial covariance models from the Matern covariance class and estimate their parameters with specified mismatches. We find that best-fitted prior covariances are not always best in recovering the truth. To achieve
Westgate, Philip M
2013-07-20
Generalized estimating equations (GEEs) are routinely used for the marginal analysis of correlated data. The efficiency of GEE depends on how closely the working covariance structure resembles the true structure, and therefore accurate modeling of the working correlation of the data is important. A popular approach is the use of an unstructured working correlation matrix, as it is not as restrictive as simpler structures such as exchangeable and AR-1 and thus can theoretically improve efficiency. However, because of the potential for having to estimate a large number of correlation parameters, variances of regression parameter estimates can be larger than theoretically expected when utilizing the unstructured working correlation matrix. Therefore, standard error estimates can be negatively biased. To account for this additional finite-sample variability, we derive a bias correction that can be applied to typical estimators of the covariance matrix of parameter estimates. Via simulation and in application to a longitudinal study, we show that our proposed correction improves standard error estimation and statistical inference.
Stochastic FDTD accuracy improvement through correlation coefficient estimation
NASA Astrophysics Data System (ADS)
Masumnia Bisheh, Khadijeh; Zakeri Gatabi, Bijan; Andargoli, Seyed Mehdi Hosseini
2015-04-01
This paper introduces a new scheme to improve the accuracy of the stochastic finite difference time domain (S-FDTD) method. S-FDTD, reported recently by Smith and Furse, calculates the variations in the electromagnetic fields caused by variability or uncertainty in the electrical properties of the materials in the model. The accuracy of the S-FDTD method is controlled by the approximations for correlation coefficients between the electrical properties of the materials in the model and the fields propagating in them. In this paper, new approximations for these correlation coefficients are obtained using Monte Carlo method with a small number of runs, terming them as Monte Carlo correlation coefficients (MC-CC). Numerical results for two bioelectromagnetic simulation examples demonstrate that MC-CC can improve the accuracy of the S-FDTD method and yield more accurate results than previous approximations.
Prague, Melanie; Wang, Rui; Stephens, Alisa; Tchetgen Tchetgen, Eric; DeGruttola, Victor
2016-01-01
Summary Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR), it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method. PMID:27060877
A cluster-randomized trial to improve stroke care in hospitals
Lakshminarayan, K.; Borbas, C.; McLaughlin, B.; Morris, N.E.; Vazquez, G.; Luepker, R.V.; Anderson, D.C.
2010-01-01
Objective: We evaluated the effect of performance feedback on acute ischemic stroke care quality in Minnesota hospitals. Methods: A cluster-randomized controlled trial design with hospital as the unit of randomization was used. Care quality was defined as adherence to 10 performance measures grouped into acute, in-hospital, and discharge care. Following preintervention data collection, all hospitals received a report on baseline care quality. Additionally, in experimental hospitals, clinical opinion leaders delivered customized feedback to care providers and study personnel worked with hospital administrators to implement changes targeting identified barriers to stroke care. Multilevel models examined experimental vs control, preintervention and postintervention performance changes and secular trends in performance. Results: Nineteen hospitals were randomized with a total of 1,211 acute ischemic stroke cases preintervention and 1,094 cases postintervention. Secular trends were significant with improvement in both experimental and control hospitals for acute (odds ratio = 2.7, p = 0.007) and in-hospital (odds ratio = 1.5, p < 0.0001) care but not discharge care. There was no significant intervention effect for acute, in-hospital, or discharge care. Conclusion: There was no definite intervention effect: both experimental and control hospitals showed significant secular trends with performance improvement. Our results illustrate the potential fallacy of using historical controls for evaluating quality improvement interventions. Classification of evidence: This study provides Class II evidence that informing hospital leaders of compliance with ischemic stroke quality indicators followed by a structured quality improvement intervention did not significantly improve compliance more than informing hospital leaders of compliance with stroke quality indicators without a quality improvement intervention. GLOSSARY CI = confidence interval; HERF = Healthcare Evaluation and
Estimating effects of improved drinking water and sanitation on cholera.
Leidner, Andrew J; Adusumilli, Naveen C
2013-12-01
Demand for adequate provision of drinking-water and sanitation facilities to promote public health and economic growth is increasing in the rapidly urbanizing countries of the developing world. With a panel of data on Asia and Africa from 1990 to 2008, associations are estimated between the occurrence of cholera outbreaks, the case rates in given outbreaks, the mortality rates associated with cholera and two disease control mechanisms, drinking-water and sanitation services. A statistically significant and negative effect is found between drinking-water services and both cholera case rates as well as cholera-related mortality rates. A relatively weak statistical relationship is found between the occurrence of cholera outbreaks and sanitation services.
Estimating Missing Features to Improve Multimedia Information Retrieval
Bagherjeiran, A; Love, N S; Kamath, C
2006-09-28
Retrieval in a multimedia database usually involves combining information from different modalities of data, such as text and images. However, all modalities of the data may not be available to form the query. The retrieval results from such a partial query are often less than satisfactory. In this paper, we present an approach to complete a partial query by estimating the missing features in the query. Our experiments with a database of images and their associated captions show that, with an initial text-only query, our completion method has similar performance to a full query with both image and text features. In addition, when we use relevance feedback, our approach outperforms the results obtained using a full query.
Improved estimates of ocean heat content from 1960 to 2015
Cheng, Lijing; Trenberth, Kevin E.; Fasullo, John; Boyer, Tim; Abraham, John; Zhu, Jiang
2017-01-01
Earth’s energy imbalance (EEI) drives the ongoing global warming and can best be assessed across the historical record (that is, since 1960) from ocean heat content (OHC) changes. An accurate assessment of OHC is a challenge, mainly because of insufficient and irregular data coverage. We provide updated OHC estimates with the goal of minimizing associated sampling error. We performed a subsample test, in which subsets of data during the data-rich Argo era are colocated with locations of earlier ocean observations, to quantify this error. Our results provide a new OHC estimate with an unbiased mean sampling error and with variability on decadal and multidecadal time scales (signal) that can be reliably distinguished from sampling error (noise) with signal-to-noise ratios higher than 3. The inferred integrated EEI is greater than that reported in previous assessments and is consistent with a reconstruction of the radiative imbalance at the top of atmosphere starting in 1985. We found that changes in OHC are relatively small before about 1980; since then, OHC has increased fairly steadily and, since 1990, has increasingly involved deeper layers of the ocean. In addition, OHC changes in six major oceans are reliable on decadal time scales. All ocean basins examined have experienced significant warming since 1998, with the greatest warming in the southern oceans, the tropical/subtropical Pacific Ocean, and the tropical/subtropical Atlantic Ocean. This new look at OHC and EEI changes over time provides greater confidence than previously possible, and the data sets produced are a valuable resource for further study. PMID:28345033
Improved estimates of ocean heat content from 1960 to 2015.
Cheng, Lijing; Trenberth, Kevin E; Fasullo, John; Boyer, Tim; Abraham, John; Zhu, Jiang
2017-03-01
Earth's energy imbalance (EEI) drives the ongoing global warming and can best be assessed across the historical record (that is, since 1960) from ocean heat content (OHC) changes. An accurate assessment of OHC is a challenge, mainly because of insufficient and irregular data coverage. We provide updated OHC estimates with the goal of minimizing associated sampling error. We performed a subsample test, in which subsets of data during the data-rich Argo era are colocated with locations of earlier ocean observations, to quantify this error. Our results provide a new OHC estimate with an unbiased mean sampling error and with variability on decadal and multidecadal time scales (signal) that can be reliably distinguished from sampling error (noise) with signal-to-noise ratios higher than 3. The inferred integrated EEI is greater than that reported in previous assessments and is consistent with a reconstruction of the radiative imbalance at the top of atmosphere starting in 1985. We found that changes in OHC are relatively small before about 1980; since then, OHC has increased fairly steadily and, since 1990, has increasingly involved deeper layers of the ocean. In addition, OHC changes in six major oceans are reliable on decadal time scales. All ocean basins examined have experienced significant warming since 1998, with the greatest warming in the southern oceans, the tropical/subtropical Pacific Ocean, and the tropical/subtropical Atlantic Ocean. This new look at OHC and EEI changes over time provides greater confidence than previously possible, and the data sets produced are a valuable resource for further study.
Monte Carlo Estimate to Improve Photon Energy Spectrum Reconstruction
NASA Astrophysics Data System (ADS)
Sawchuk, S.
Improvements to planning radiation treatment for cancer patients and quality control of medical linear accelerators (linacs) can be achieved with the explicit knowledge of the photon energy spectrum. Monte Carlo (MC) simulations of linac treatment heads and experimental attenuation analysis are among the most popular ways of obtaining these spectra. Attenuation methods which combine measurements under narrow beam geometry and the associated calculation techniques to reconstruct the spectrum from the acquired data are very practical in a clinical setting and they can also serve to validate MC simulations. A novel reconstruction method [1] which has been modified [2] utilizes a Simpson's rule (SR) to approximate and discretize (1)
Strategies for Improved CALIPSO Aerosol Optical Depth Estimates
NASA Technical Reports Server (NTRS)
Vaughan, Mark A.; Kuehn, Ralph E.; Tackett, Jason L.; Rogers, Raymond R.; Liu, Zhaoyan; Omar, A.; Getzewich, Brian J.; Powell, Kathleen A.; Hu, Yongxiang; Young, Stuart A.; Avery, Melody A.; Winker, David M.; Trepte, Charles R.
2010-01-01
In the spring of 2010, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) project will be releasing version 3 of its level 2 data products. In this paper we describe several changes to the algorithms and code that yield substantial improvements in CALIPSO's retrieval of aerosol optical depths (AOD). Among these are a retooled cloud-clearing procedure and a new approach to determining the base altitudes of aerosol layers in the planetary boundary layer (PBL). The results derived from these modifications are illustrated using case studies prepared using a late beta version of the level 2 version 3 processing code.
Improving a regional model using reduced complexity and parameter estimation
Kelson, Victor A.; Hunt, Randall J.; Haitjema, Henk M.
2002-01-01
The availability of powerful desktop computers and graphical user interfaces for ground water flow models makes possible the construction of ever more complex models. A proposed copper-zinc sulfide mine in northern Wisconsin offers a unique case in which the same hydrologic system has been modeled using a variety of techniques covering a wide range of sophistication and complexity. Early in the permitting process, simple numerical models were used to evaluate the necessary amount of water to be pumped from the mine, reductions in streamflow, and the drawdowns in the regional aquifer. More complex models have subsequently been used in an attempt to refine the predictions. Even after so much modeling effort, questions regarding the accuracy and reliability of the predictions remain. We have performed a new analysis of the proposed mine using the two-dimensional analytic element code GFLOW coupled with the nonlinear parameter estimation code UCODE. The new model is parsimonious, containing fewer than 10 parameters, and covers a region several times larger in areal extent than any of the previous models. The model demonstrates the suitability of analytic element codes for use with parameter estimation codes. The simplified model results are similar to the more complex models; predicted mine inflows and UCODE-derived 95% confidence intervals are consistent with the previous predictions. More important, the large areal extent of the model allowed us to examine hydrological features not included in the previous models, resulting in new insights about the effects that far-field boundary conditions can have on near-field model calibration and parameterization. In this case, the addition of surface water runoff into a lake in the headwaters of a stream while holding recharge constant moved a regional ground watershed divide and resulted in some of the added water being captured by the adjoining basin. Finally, a simple analytical solution was used to clarify the GFLOW model
Ionospheric perturbation degree estimates for improving GNSS applications
NASA Astrophysics Data System (ADS)
Jakowski, Norbert; Mainul Hoque, M.; Wilken, Volker; Berdermann, Jens; Hlubek, Nikolai
Ionosphere can adversely affect accuracy, continuity, availability, and integrity of modern Global Navigation Satellite Systems (GNSS) in different ways. Hence, reliable information on key parameters describing the perturbation degree of the ionosphere is helpful for estimating the potential degradation of the performance of these systems. So, to guarantee the required safety level in aviation, Ground Based Augmentation Systems (GBAS) and Satellite Based Augmentation Systems (SBAS) have been established for detecting and mitigating ionospheric threats in particular due to ionospheric gradients. The paper reviews various attempts and capabilities to characterize the perturbation degree of the ionosphere currently being used in precise positioning and safety of life applications. Continuity and availability of signals are mainly impacted by amplitude and phase scintillations characterized by indices such as S4 or phase noise. To characterize medium and large scale ionospheric perturbations that may seriously affect accuracy and integrity of GNSS, the use of an internationally standardized Disturbance Ionosphere Index (DIX) is recommended. The definition of such a DIX must take into account the practical needs, should be an objective measure of ionospheric conditions and easy and reproducible to compute. A preliminary DIX approach is presented and discussed. Such a robust and easy adaptable index should have a great potential for being used in operational ionospheric weather services and GNSS augmentation systems.
Improving hyperspectral band selection by constructing an estimated reference map
NASA Astrophysics Data System (ADS)
Guo, Baofeng; Damper, Robert I.; Gunn, Steve R.; Nelson, James D. B.
2014-01-01
We investigate band selection for hyperspectral image classification. Mutual information (MI) measures the statistical dependence between two random variables. By modeling the reference map as one of the two random variables, MI can, therefore, be used to select the bands that are more useful for image classification. A new method is proposed to estimate the MI using an optimally constructed reference map, reducing reliance on ground-truth information. To reduce the interferences from noise and clutters, the reference map is constructed by averaging a subset of spectral bands that are chosen with the best capability to approximate the ground truth. To automatically find these bands, we develop a searching strategy consisting of differentiable MI, gradient ascending algorithm, and random-start optimization. Experiments on AVIRIS 92AV3C dataset and Pavia University scene dataset show that the proposed method outperformed the benchmark methods. In AVIRIS 92AV3C dataset, up to 55% of bands can be removed without significant loss of classification accuracy, compared to the 40% from that using the reference map accompanied with the dataset. Meanwhile, its performance is much more robust to accuracy degradation when bands are cut off beyond 60%, revealing a better agreement in the MI calculation. In Pavia University scene dataset, using 45 bands achieved 86.18% classification accuracy, which is only 1.5% lower than that using all the 103 bands.
An improved method for nonlinear parameter estimation: a case study of the Rössler model
NASA Astrophysics Data System (ADS)
He, Wen-Ping; Wang, Liu; Jiang, Yun-Di; Wan, Shi-Quan
2016-08-01
Parameter estimation is an important research topic in nonlinear dynamics. Based on the evolutionary algorithm (EA), Wang et al. (2014) present a new scheme for nonlinear parameter estimation and numerical tests indicate that the estimation precision is satisfactory. However, the convergence rate of the EA is relatively slow when multiple unknown parameters in a multidimensional dynamical system are estimated simultaneously. To solve this problem, an improved method for parameter estimation of nonlinear dynamical equations is provided in the present paper. The main idea of the improved scheme is to use all of the known time series for all of the components in some dynamical equations to estimate the parameters in single component one by one, instead of estimating all of the parameters in all of the components simultaneously. Thus, we can estimate all of the parameters stage by stage. The performance of the improved method was tested using a classic chaotic system—Rössler model. The numerical tests show that the amended parameter estimation scheme can greatly improve the searching efficiency and that there is a significant increase in the convergence rate of the EA, particularly for multiparameter estimation in multidimensional dynamical equations. Moreover, the results indicate that the accuracy of parameter estimation and the CPU time consumed by the presented method have no obvious dependence on the sample size.
Estimating the difference limen in 2AFC tasks: pitfalls and improved estimators.
Ulrich, Rolf; Vorberg, Dirk
2009-08-01
Discrimination performance is often assessed by measuring the difference limen (DL; or just noticeable difference) in a two-alternative forced choice (2AFC) task. Here, we show that the DL estimated from 2AFC percentage-correct data is likely to systematically under- or overestimate true discrimination performance if order effects are present. We show how pitfalls with the 2AFC task may be avoided and suggest a novel approach for analyzing 2AFC data.
An Improved Source-Scanning Algorithm for Locating Earthquake Clusters or Aftershock Sequences
NASA Astrophysics Data System (ADS)
Liao, Y.; Kao, H.; Hsu, S.
2010-12-01
The Source-scanning Algorithm (SSA) was originally introduced in 2004 to locate non-volcanic tremors. Its application was later expanded to the identification of earthquake rupture planes and the near-real-time detection and monitoring of landslides and mud/debris flows. In this study, we further improve SSA for the purpose of locating earthquake clusters or aftershock sequences when only a limited number of waveform observations are available. The main improvements include the application of a ground motion analyzer to separate P and S waves, the automatic determination of resolution based on the grid size and time step of the scanning process, and a modified brightness function to utilize constraints from multiple phases. Specifically, the improved SSA (named as ISSA) addresses two major issues related to locating earthquake clusters/aftershocks. The first one is the massive amount of both time and labour to locate a large number of seismic events manually. And the second one is to efficiently and correctly identify the same phase across the entire recording array when multiple events occur closely in time and space. To test the robustness of ISSA, we generate synthetic waveforms consisting of 3 separated events such that individual P and S phases arrive at different stations in different order, thus making correct phase picking nearly impossible. Using these very complicated waveforms as the input, the ISSA scans all model space for possible combination of time and location for the existence of seismic sources. The scanning results successfully associate various phases from each event at all stations, and correctly recover the input. To further demonstrate the advantage of ISSA, we apply it to the waveform data collected by a temporary OBS array for the aftershock sequence of an offshore earthquake southwest of Taiwan. The overall signal-to-noise ratio is inadequate for locating small events; and the precise arrival times of P and S phases are difficult to
Improving modeled snow albedo estimates during the spring melt season
NASA Astrophysics Data System (ADS)
Malik, M. Jahanzeb; Velde, Rogier; Vekerdy, Zoltan; Su, Zhongbo
2014-06-01
Snow albedo influences snow-covered land energy and water budgets and is thus an important variable for energy and water fluxes calculations. Here, we quantify the performance of the three existing snow albedo parameterizations under alpine, tundra, and prairie snow conditions when implemented in the Noah land surface model (LSM)—Noah's default and ones from the Biosphere-Atmosphere Transfer Scheme (BATS) and the Canadian Land Surface Scheme (CLASS) LSMs. The Noah LSM is forced with and its output is evaluated using in situ measurements from seven sites in U.S. and France. Comparison of the snow albedo simulations with the in situ measurements reveals that the three parameterizations overestimate snow albedo during springtime. An alternative snow albedo parameterization is introduced that adopts the shape of the variogram for the optically thick snowpacks and decreases the albedo further for optically thin conditions by mixing the snow with the land surface (background) albedo as a function of snow depth. In comparison with the in situ measurements, the new parameterization improves albedo simulation of the alpine and tundra snowpacks and positively impacts the simulation of snow depth, snowmelt rate, and upward shortwave radiation. An improved model performance with the variogram-shaped parameterization can, however, not be unambiguously detected for prairie snowpacks, which may be attributed to uncertainties associated with the simulation of snow density. An assessment of the model performance for the Upper Colorado River Basin highlights that with the variogram-shaped parameterization Noah simulates more evapotranspiration and larger runoff peaks in Spring, whereas the Summer runoff is lower.
Janssen, Ronald J; Jylänki, Pasi; van Gerven, Marcel A J
2016-01-01
We have proposed a Bayesian approach for functional parcellation of whole-brain FMRI measurements which we call Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR). We use distance-dependent Chinese restaurant processes (dd-CRPs) to define a flexible prior which partitions the voxel measurements into clusters whose number and shapes are unknown a priori. With dd-CRPs we can conveniently implement spatial constraints to ensure that our parcellations remain spatially contiguous and thereby physiologically meaningful. In the present work, we extend CAESAR by using Gaussian process (GP) priors to model the temporally smooth haemodynamic signals that give rise to the measured FMRI data. A challenge for GP inference in our setting is the cubic scaling with respect to the number of time points, which can become computationally prohibitive with FMRI measurements, potentially consisting of long time series. As a solution we describe an efficient implementation that is practically as fast as the corresponding time-independent non-GP model with typically-sized FMRI data sets. We also employ a population Monte-Carlo algorithm that can significantly speed up convergence compared to traditional single-chain methods. First we illustrate the benefits of CAESAR and the GP priors with simulated experiments. Next, we demonstrate our approach by parcellating resting state FMRI data measured from twenty participants as taken from the Human Connectome Project data repository. Results show that CAESAR affords highly robust and scalable whole-brain clustering of FMRI timecourses.
Improving the text classification using clustering and a novel HMM to reduce the dimensionality.
Seara Vieira, A; Borrajo, L; Iglesias, E L
2016-11-01
In text classification problems, the representation of a document has a strong impact on the performance of learning systems. The high dimensionality of the classical structured representations can lead to burdensome computations due to the great size of real-world data. Consequently, there is a need for reducing the quantity of handled information to improve the classification process. In this paper, we propose a method to reduce the dimensionality of a classical text representation based on a clustering technique to group documents, and a previously developed Hidden Markov Model to represent them. We have applied tests with the k-NN and SVM classifiers on the OHSUMED and TREC benchmark text corpora using the proposed dimensionality reduction technique. The experimental results obtained are very satisfactory compared to commonly used techniques like InfoGain and the statistical tests performed demonstrate the suitability of the proposed technique for the preprocessing step in a text classification task.
Clustering methods for removing outliers from vision-based range estimates
NASA Technical Reports Server (NTRS)
Hussien, B.; Suorsa, R.
1992-01-01
The present approach to the automation of helicopter low-altitude flight uses one or more passive imaging sensors to extract environmental obstacle information; this is then processed via computer-vision techniques to yield a time-varying map of range to obstacles in the sensor's field of view along the vehicle's flight path. Attention is given to two related techniques which can eliminate outliers from a sparse range map, clustering sparse range-map information into different spatial classes that rely on a segmented and labeled image to aid in spatial classification within the image plane.
Novel angle estimation for bistatic MIMO radar using an improved MUSIC
NASA Astrophysics Data System (ADS)
Li, Jianfeng; Zhang, Xiaofei; Chen, Han
2014-09-01
In this article, we study the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar and propose an improved multiple signal classification (MUSIC) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. The proposed algorithm obtains initial estimations of angles obtained from the signal subspace and uses the local one-dimensional peak searches to achieve the joint estimations of DOD and DOA. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and is almost the same as that of two-dimensional MUSIC. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired DOD and DOA estimations, and avoid two-dimensional peak searching. The simulation results verify the effectiveness and improvement of the algorithm.
Rejani, R; Rao, K V; Osman, M; Srinivasa Rao, Ch; Reddy, K Sammi; Chary, G R; Pushpanjali; Samuel, Josily
2016-03-01
The ungauged wet semi-arid watershed cluster, Seethagondi, lies in the Adilabad district of Telangana in India and is prone to severe erosion and water scarcity. The runoff and soil loss data at watershed, catchment, and field level are necessary for planning soil and water conservation interventions. In this study, an attempt was made to develop a spatial soil loss estimation model for Seethagondi cluster using RUSLE coupled with ARCGIS and was used to estimate the soil loss spatially and temporally. The daily rainfall data of Aphrodite for the period from 1951 to 2007 was used, and the annual rainfall varied from 508 to 1351 mm with a mean annual rainfall of 950 mm and a mean erosivity of 6789 MJ mm ha(-1) h(-1) year(-1). Considerable variation in land use land cover especially in crop land and fallow land was observed during normal and drought years, and corresponding variation in the erosivity, C factor, and soil loss was also noted. The mean value of C factor derived from NDVI for crop land was 0.42 and 0.22 in normal year and drought years, respectively. The topography is undulating and major portion of the cluster has slope less than 10°, and 85.3% of the cluster has soil loss below 20 t ha(-1) year(-1). The soil loss from crop land varied from 2.9 to 3.6 t ha(-1) year(-1) in low rainfall years to 31.8 to 34.7 t ha(-1) year(-1) in high rainfall years with a mean annual soil loss of 12.2 t ha(-1) year(-1). The soil loss from crop land was higher in the month of August with an annual soil loss of 13.1 and 2.9 t ha(-1) year(-1) in normal and drought year, respectively. Based on the soil loss in a normal year, the interventions recommended for 85.3% of area of the watershed includes agronomic measures such as contour cultivation, graded bunds, strip cropping, mixed cropping, crop rotations, mulching, summer plowing, vegetative bunds, agri-horticultural system, and management practices such as broad bed furrow, raised sunken beds, and harvesting available water
Measuring slope to improve energy expenditure estimates during field-based activities.
Duncan, Glen E; Lester, Jonathan; Migotsky, Sean; Higgins, Lisa; Borriello, Gaetano
2013-03-01
This technical note describes methods to improve activity energy expenditure estimates by using a multi-sensor board (MSB) to measure slope. Ten adults walked over a 4-km (2.5-mile) course wearing an MSB and mobile calorimeter. Energy expenditure was estimated using accelerometry alone (base) and 4 methods to measure slope. The barometer and global positioning system methods improved accuracy by 11% from the base (p < 0.05) to 86% overall. Measuring slope using the MSB improves energy expenditure estimates during field-based activities.
Estimates of the national benefits and costs of improving ambient air quality
Brady, G.L.; Bower, B.T.; Lakhani, H.A.
1983-04-01
This paper examines the estimates of national benefits and national costs of ambient air quality improvement in the United States for the period 1970 to 1978. Analysis must be at the micro-level for both receptors of pollution and the dischargers of residuals. Section 2 discusses techniques for estimating the national benefits from improving ambient air quality. The literature on national benefits to health (mortality and morbidity) and non-health (avoiding damages to materials, plants, crops, etc.) is critically reviewed in this section. For the period 1970 to 1978, the value of these benefits ranged from about $5 billion to $51 billion, with a point estimate of about $22 billion. The national cost estimates by the Council on Environmental Quality, Bureau of Economic Analysis, and McGraw-Hill are provided in section 2. Cost estimates must include not only the end-of-pipe treatment measures, but also the alternatives: changes in product specification, product mix, processes, etc. These types of responses are not generally considered in estimates of national costs. For the period 1970 to 1978, estimates provided in section 3 of national costs of improving ambient air quality ranged from $8 to $9 billion in 1978 dollars. Section 4 concludes that the national benefits for improving ambient air quality exceed the national costs for the average and the high values of benefits, but not for the low estimates. Section 5 discusses the requirements for establishing a national regional computational framework for estimating national benefits and national costs. 49 references, 2 tables
Wu, Hao-Yi; Rozo, Eduardo; Wechsler, Risa H.; /KIPAC, Menlo Park /SLAC /CCAPP, Columbus /KICP, Chicago /KIPAC, Menlo Park /SLAC
2010-06-02
The precision of cosmological parameters derived from galaxy cluster surveys is limited by uncertainty in relating observable signals to cluster mass. We demonstrate that a small mass-calibration follow-up program can significantly reduce this uncertainty and improve parameter constraints, particularly when the follow-up targets are judiciously chosen. To this end, we apply a simulated annealing algorithm to maximize the dark energy information at fixed observational cost, and find that optimal follow-up strategies can reduce the observational cost required to achieve a specified precision by up to an order of magnitude. Considering clusters selected from optical imaging in the Dark Energy Survey, we find that approximately 200 low-redshift X-ray clusters or massive Sunyaev-Zel'dovich clusters can improve the dark energy figure of merit by 50%, provided that the follow-up mass measurements involve no systematic error. In practice, the actual improvement depends on (1) the uncertainty in the systematic error in follow-up mass measurements, which needs to be controlled at the 5% level to avoid severe degradation of the results; and (2) the scatter in the optical richness-mass distribution, which needs to be made as tight as possible to improve the efficacy of follow-up observations.
Estimating Accuracy of Land-Cover Composition From Two-Stage Clustering Sampling
Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), ...
Liu, Xiaoqiu; Lewis, James J.; Zhang, Hui; Lu, Wei; Zhang, Shun; Zheng, Guilan; Bai, Liqiong; Li, Jun; Li, Xue; Chen, Hongguang; Liu, Mingming; Chen, Rong; Chi, Junying; Lu, Jian; Huan, Shitong; Cheng, Shiming; Wang, Lixia; Jiang, Shiwen; Chin, Daniel P.; Fielding, Katherine L.
2015-01-01
Background Mobile text messaging and medication monitors (medication monitor boxes) have the potential to improve adherence to tuberculosis (TB) treatment and reduce the need for directly observed treatment (DOT), but to our knowledge they have not been properly evaluated in TB patients. We assessed the effectiveness of text messaging and medication monitors to improve medication adherence in TB patients. Methods and Findings In a pragmatic cluster-randomised trial, 36 districts/counties (each with at least 300 active pulmonary TB patients registered in 2009) within the provinces of Heilongjiang, Jiangsu, Hunan, and Chongqing, China, were randomised using stratification and restriction to one of four case-management approaches in which patients received reminders via text messages, a medication monitor, combined, or neither (control). Patients in the intervention arms received reminders to take their drugs and reminders for monthly follow-up visits, and the managing doctor was recommended to switch patients with adherence problems to more intensive management or DOT. In all arms, patients took medications out of a medication monitor box, which recorded when the box was opened, but the box gave reminders only in the medication monitor and combined arms. Patients were followed up for 6 mo. The primary endpoint was the percentage of patient-months on TB treatment where at least 20% of doses were missed as measured by pill count and failure to open the medication monitor box. Secondary endpoints included additional adherence and standard treatment outcome measures. Interventions were not masked to study staff and patients. From 1 June 2011 to 7 March 2012, 4,292 new pulmonary TB patients were enrolled across the 36 clusters. A total of 119 patients (by arm: 33 control, 33 text messaging, 23 medication monitor, 30 combined) withdrew from the study in the first month because they were reassessed as not having TB by their managing doctor (61 patients) or were switched to
Jonsen, Ian
2016-01-01
State-space models provide a powerful way to scale up inference of movement behaviours from individuals to populations when the inference is made across multiple individuals. Here, I show how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes. I use simulated movement paths with known behavioural states to compare estimation error between nonhierarchical and joint estimation formulations of an otherwise identical state-space model. Behavioural state estimation error was strongly affected by the degree of similarity between movement patterns characterising the behavioural states, with less error when movements were strongly dissimilar between states. The joint estimation model improved behavioural state estimation relative to the nonhierarchical model for simulated data with heavy-tailed Argos location errors. When applied to Argos telemetry datasets from 10 Weddell seals, the nonhierarchical model estimated highly uncertain behavioural state switching probabilities for most individuals whereas the joint estimation model yielded substantially less uncertainty. The joint estimation model better resolved the behavioural state sequences across all seals. Hierarchical or joint estimation models should be the preferred choice for estimating behavioural states from animal movement data, especially when location data are error-prone. PMID:26853261
ASTM clustering for improving coal analysis by near-infrared spectroscopy.
Andrés, J M; Bona, M T
2006-11-15
Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.
Zarchi, Kian; Haugaard, Vibeke B; Dufour, Deirdre N; Jemec, Gregor B E
2015-03-01
Telemedicine is widely considered as an efficient approach to manage the growing problem of chronic wounds. However, to date, there is no convincing evidence to support the clinical efficacy of telemedicine in wound management. In this prospective cluster controlled study, we tested the hypothesis that advice on wound management provided by a team of wound-care specialists through telemedicine would significantly improve the likelihood of wound healing compared with the best available conventional practice. A total of 90 chronic wound patients in home care met all study criteria and were included: 50 in the telemedicine group and 40 in the conventional group. Patients with pressure ulcers, surgical wounds, and cancer wounds were excluded. During the 1-year follow-up, complete wound healing was achieved in 35 patients (70%) in the telemedicine group compared with 18 patients (45%) in the conventional group. After adjusting for important covariates, offering advice on wound management through telemedicine was associated with significantly increased healing compared with the best available conventional practice (telemedicine vs. conventional practice: adjusted hazard ratio 2.19; 95% confidence interval: 1.15-4.17; P=0.017). This study strongly supports the use of telemedicine to connect home-care nurses to a team of wound experts in order to improve the management of chronic wounds.
High, F. W.; Stalder, B.; Song, J.; Ade, P. A. R.; Aird, K. A.; Allam, S. S.; Buckley-Geer, E. J.; Armstrong, R.; Barkhouse, W. A.; Benson, B. A.; Bertin, E.; Bhattacharya, S.; Bleem, L. E.; Carlstrom, J. E.; Chang, C. L.; Crawford, T. M.; Crites, A. T.; Brodwin, M.; Challis, P.; De Haan, T.
2010-11-10
We present redshifts and optical richness properties of 21 galaxy clusters uniformly selected by their Sunyaev-Zel'dovich (SZ) signature. These clusters, plus an additional, unconfirmed candidate, were detected in a 178 deg{sup 2} area surveyed by the South Pole Telescope (SPT) in 2008. Using griz imaging from the Blanco Cosmology Survey and from pointed Magellan telescope observations, as well as spectroscopy using Magellan facilities, we confirm the existence of clustered red-sequence galaxies, report red-sequence photometric redshifts, present spectroscopic redshifts for a subsample, and derive R{sub 200} radii and M{sub 200} masses from optical richness. The clusters span redshifts from 0.15 to greater than 1, with a median redshift of 0.74; three clusters are estimated to be at z>1. Redshifts inferred from mean red-sequence colors exhibit 2% rms scatter in {sigma}{sub z}/(1 + z) with respect to the spectroscopic subsample for z < 1. We show that the M{sub 200} cluster masses derived from optical richness correlate with masses derived from SPT data and agree with previously derived scaling relations to within the uncertainties. Optical and infrared imaging is an efficient means of cluster identification and redshift estimation in large SZ surveys, and exploiting the same data for richness measurements, as we have done, will be useful for constraining cluster masses and radii for large samples in cosmological analysis.
Nguyen, Vinh Hao; Suh, Young Soo
2007-01-01
This paper is concerned with improving performance of a state estimation problem over a network in which a send-on-delta (SOD) transmission method is used. The SOD method requires that a sensor node transmit data to the estimator node only if its measurement value changes more than a given specified δ value. This method has been explored and applied by researchers because of its efficiency in the network bandwidth improvement. However, when this method is used, it is not ensured that the estimator node receives data from the sensor nodes regularly at every estimation period. Therefore, we propose a method to reduce estimation error in case of no sensor data reception. When the estimator node does not receive data from the sensor node, the sensor value is known to be in a (−δi,+δi) interval from the last transmitted sensor value. This implicit information has been used to improve estimation performance in previous studies. The main contribution of this paper is to propose an algorithm, where the sensor value interval is reduced to (−δi/2,+δi/2) in certain situations. Thus, the proposed algorithm improves the overall estimation performance without any changes in the send-on-delta algorithms of the sensor nodes. Through numerical simulations, we demonstrate the feasibility and the usefulness of the proposed method.
"Battleship Numberline": A Digital Game for Improving Estimation Accuracy on Fraction Number Lines
ERIC Educational Resources Information Center
Lomas, Derek; Ching, Dixie; Stampfer, Eliane; Sandoval, Melanie; Koedinger, Ken
2011-01-01
Given the strong relationship between number line estimation accuracy and math achievement, might a computer-based number line game help improve math achievement? In one study by Rittle-Johnson, Siegler and Alibali (2001), a simple digital game called "Catch the Monster" provided practice in estimating the location of decimals on a…
Using Local Matching to Improve Estimates of Program Impact: Evidence from Project STAR
ERIC Educational Resources Information Center
Jones, Nathan; Steiner, Peter; Cook, Tom
2011-01-01
In this study the authors test whether matching using intact local groups improves causal estimates over those produced using propensity score matching at the student level. Like the recent analysis of Wilde and Hollister (2007), they draw on data from Project STAR to estimate the effect of small class sizes on student achievement. They propose a…
2014-03-27
DIRECT EMISSIVITY MEASUREMENTS OF PAINTED METALS FOR IMPROVED TEMPERATURE ESTIMATION DURING LASER DAMAGE TESTING THESIS Sean M. Baumann, Civilian...radiance measurement, and fitted spectral radiance results, of one pixel on the back surface of a painted metal sample, far from laser burn-through hole...parabolic mirror NET noise-equivalent temperature xv DIRECT EMISSIVITY MEASUREMENTS OF PAINTED METALS FOR IMPROVED TEMPERATURE ESTIMATION DURING LASER DAMAGE
Evaluation of an intervention to improve blood culture practices: a cluster randomised trial.
Pavese, P; Maillet, M; Vitrat-Hincky, V; Recule, C; Vittoz, J-P; Guyomard, A; Seigneurin, A; François, P
2014-12-01
This study aimed to evaluate an intervention to improve blood culture practices. A cluster randomised trial in two parallel groups was performed at the Grenoble University Hospital, France. In October 2009, the results of a practices audit and the guidelines for the optimal use of blood cultures were disseminated to clinical departments. We compared two types of information dissemination: simple presentation or presentation associated with an infectious diseases (ID) specialist intervention. The principal endpoint was blood culture performance measured by the rate of patients having one positive blood culture and the rate of positive blood cultures. The cases of 130 patients in the "ID" group and 119 patients in the "simple presentation" group were audited during the second audit in April 2010. The rate of patients with one positive blood culture increased in both groups (13.62 % vs 9.89 % for the ID group, p = 0.002, 15.90 % vs 13.47 % for the simple presentation group, p = 0.009). The rate of positive blood cultures improved in both groups (6.68 % vs 5.96 % for the ID group, p = 0.003, 6.52 % vs 6.21 % for the simple presentation group, p = 0.017). The blood culture indication was significantly less often specified in the request form in the simple presentation group, while it remained stable in the ID group (p = 0.04). The rate of positive blood cultures and the rate of patients having one positive blood culture improved in both groups. The ID specialist intervention did not have more of an impact on practices than a simple presentation of audit feedback and guidelines.
Improving hot region prediction by parameter optimization of density clustering in PPI.
Hu, Jing; Zhang, Xiaolong
2016-11-01
This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius.
NASA Astrophysics Data System (ADS)
Troiani, Francesco; Piacentini, Daniela; Seta Marta, Della
2016-04-01
analysis conducted on 52 clusters of high and very high Gi* values indicate that mass movement of slope material represents the dominant process producing over-steeped long-profiles along connected streams, whereas the litho-structure accounts for the main anomalies along disconnected steams. Tectonic structures generally provide to the largest clusters. Our results demonstrate that SL-HCA maps have the same potential of lithologically-filtered SL maps for detecting knickzones due to hillslope processes and/or tectonic structures. The reduced-complexity model derived from SL-HCA approach highly improve the readability of the morphometric outcomes, thus the interpretation at a regional scale of the geological-geomorphological meaning of over-steeped segments on long-profiles. SL-HCA maps are useful to investigate and better interpret knickzones within regions poorly covered by geological data and where field surveys are difficult to be performed.
Systems analysis and improvement to optimize pMTCT (SAIA): a cluster randomized trial
2014-01-01
Background Despite significant increases in global health investment and the availability of low-cost, efficacious interventions to prevent mother-to-child HIV transmission (pMTCT) in low- and middle-income countries with high HIV burden, the translation of scientific advances into effective delivery strategies has been slow, uneven and incomplete. As a result, pediatric HIV infection remains largely uncontrolled. A five-step, facility-level systems analysis and improvement intervention (SAIA) was designed to maximize effectiveness of pMTCT service provision by improving understanding of inefficiencies (step one: cascade analysis), guiding identification and prioritization of low-cost workflow modifications (step two: value stream mapping), and iteratively testing and redesigning these modifications (steps three through five). This protocol describes the SAIA intervention and methods to evaluate the intervention’s impact on reducing drop-offs along the pMTCT cascade. Methods This study employs a two-arm, longitudinal cluster randomized trial design. The unit of randomization is the health facility. A total of 90 facilities were identified in Côte d’Ivoire, Kenya and Mozambique (30 per country). A subset was randomly selected and assigned to intervention and comparison arms, stratified by country and service volume, resulting in 18 intervention and 18 comparison facilities across all three countries, with six intervention and six comparison facilities per country. The SAIA intervention will be implemented for six months in the 18 intervention facilities. Primary trial outcomes are designed to assess improvements in the pMTCT service cascade, and include the percentage of pregnant women being tested for HIV at the first antenatal care visit, the percentage of HIV-infected pregnant women receiving adequate prophylaxis or combination antiretroviral therapy in pregnancy, and the percentage of newborns exposed to HIV in pregnancy receiving an HIV diagnosis eight
Estimates of the national benefits and costs of improving ambient air quality
Brady, G.L.; Bower, B.T.; Lakhani, H.A.
1983-04-01
This paper examines the estimates of national benefits and national costs of ambient air quality improvement in the US for the period 1970 to 1978. Analysis must be at the micro-level for both receptors of pollution and the dischargers of residuals. Section 2 discusses techniques for estimating the national benefits from improving ambient air quality. The literature on national benefits to health (mortality and morbidity) and non-health (avoiding damages to materials, plants, crops, etc.) is critically reviewed in this section. For the period 1970 to 1978, the value of these benefits ranged from about $5 billion to $51 billion, with a point estimate of about $22 billion. The national cost estimates by the Council on Environmental Quality, Bureau of Economic Analysis, and McGraw-Hill are provided in section 2. Cost estimates must include not only the end-of-pipe treatment measures, but also the alternatives: changes in product specification, product mix, processes, etc. These types of responses are not generally considered in estimates of national costs of improving ambient air quality ranged from $8 to $9 billion in 1978 dollars. Section 4 concludes that the national benefits for improving ambient air quality exceed the national costs for the average and the high values of benefits, but not for the low estimates.
ERIC Educational Resources Information Center
Maskiewicz, April Cordero; Griscom, Heather Peckham; Welch, Nicole Turrill
2012-01-01
In this study, we used targeted active-learning activities to help students improve their ways of reasoning about carbon flow in ecosystems. The results of a validated ecology conceptual inventory (diagnostic question clusters [DQCs]) provided us with information about students' understanding of and reasoning about transformation of inorganic and…
Evaluating and improving the cluster variation method entropy functional for Ising alloys
NASA Astrophysics Data System (ADS)
Ferreira, Luiz G.; Wolverton, C.; Zunger, Alex
1998-02-01
The success of the "cluster variation method" (CVM) in reproducing quite accurately the free energies of Monte Carlo (MC) calculations on Ising models is explained in terms of identifying a cancellation of errors: We show that the CVM produces correlation functions that are too close to zero, which leads to an overestimation of the exact energy, E, and at the same time, to an underestimation of -TS, so the free energy F=E-TS is more accurate than either of its parts. This insight explains a problem with "hybrid methods" using MC correlation functions in the CVM entropy expression: They give exact energies E and do not give significantly improved -TS relative to CVM, so they do not benefit from the above noted cancellation of errors. Additionally, hybrid methods suffer from the difficulty of adequately accounting for both ordered and disordered phases in a consistent way. A different technique, the "entropic Monte Carlo" (EMC), is shown here to provide a means for critically evaluating the CVM entropy. Inspired by EMC results, we find a universal and simple correlation to the CVM entropy which produces individual components of the free energy with MC accuracy, but is computationally much less expensive than either MC thermodynamic integration or EMC.
Ferri, Marica; De Luca, Assunta; Rossi, Paolo Giorgi; Lori, Giuliano; Guasticchi, Gabriella
2005-01-01
Background Early interventions proved to be able to improve prognosis in acute stroke patients. Prompt identification of symptoms, organised timely and efficient transportation towards appropriate facilities, become essential part of effective treatment. The implementation of an evidence based pre-hospital stroke care pathway may be a method for achieving the organizational standards required to grant appropriate care. We performed a systematic search for studies evaluating the effect of pre-hospital and emergency interventions for suspected stroke patients and we found that there seems to be only a few studies on the emergency field and none about implementation of clinical pathways. We will test the hypothesis that the adoption of emergency clinical pathway improves early diagnosis and referral in suspected stroke patients. We designed a cluster randomised controlled trial (C-RCT), the most powerful study design to assess the impact of complex interventions. The study was registered in the Current Controlled Trials Register: ISRCTN41456865 – Implementation of pre-hospital emergency pathway for stroke – a cluster randomised trial. Methods/design Two-arm cluster-randomised trial (C-RCT). 16 emergency services and 14 emergency rooms were randomised either to arm 1 (comprising a training module and administration of the guideline), or to arm 2 (no intervention, current practice). Arm 1 participants (152 physicians, 280 nurses, 50 drivers) attended an interactive two sessions course with continuous medical education CME credits on the contents of the clinical pathway. We estimated that around 750 patients will be met by the services in the 6 months of observation. This duration allows recruiting a sample of patients sufficient to observe a 30% improvement in the proportion of appropriate diagnoses. Data collection will be performed using current information systems. Process outcomes will be measured at the cluster level six months after the intervention. We will
2010-01-01
Background Improving nutrition knowledge among children may help them to make healthier food choices. The aim of this study was to assess the effectiveness and acceptability of a novel educational intervention to increase nutrition knowledge among primary school children. Methods We developed a card game 'Top Grub' and a 'healthy eating' curriculum for use in primary schools. Thirty-eight state primary schools comprising 2519 children in years 5 and 6 (aged 9-11 years) were recruited in a pragmatic cluster randomised controlled trial. The main outcome measures were change in nutrition knowledge scores, attitudes to healthy eating and acceptability of the intervention by children and teachers. Results Twelve intervention and 13 control schools (comprising 1133 children) completed the trial. The main reason for non-completion was time pressure of the school curriculum. Mean total nutrition knowledge score increased by 1.1 in intervention (baseline to follow-up: 28.3 to 29.2) and 0.3 in control schools (27.3 to 27.6). Total nutrition knowledge score at follow-up, adjusted for baseline score, deprivation, and school size, was higher in intervention than in control schools (mean difference = 1.1; 95% CI: 0.05 to 2.16; p = 0.042). At follow-up, more children in the intervention schools said they 'are currently eating a healthy diet' (39.6%) or 'would try to eat a healthy diet' (35.7%) than in control schools (34.4% and 31.7% respectively; chi-square test p < 0.001). Most children (75.5%) enjoyed playing the game and teachers considered it a useful resource. Conclusions The 'Top Grub' card game facilitated the enjoyable delivery of nutrition education in a sample of UK primary school age children. Further studies should determine whether improvements in nutrition knowledge are sustained and lead to changes in dietary behaviour. PMID:20219104
Improved method for estimating tree crown diameter using high-resolution airborne data
NASA Astrophysics Data System (ADS)
Brovkina, Olga; Latypov, Iscander Sh.; Cienciala, Emil; Fabianek, Tomas
2016-04-01
Automatic mapping of tree crown size (radius, diameter, or width) from remote sensing can provide a major benefit for practical and scientific purposes, but requires the development of accurate methods. This study presents an improved method for average tree crown diameter estimation at a forest plot level from high-resolution airborne data. The improved method consists of the combination of a window binarization procedure and a granulometric algorithm, and avoids the complicated crown delineation procedure that is currently used to estimate crown size. The systematic error in average crown diameter estimates is corrected with the improved method. The improved method is tested with coniferous, beech, and mixed-species forest plots based on airborne images of various spatial resolutions. The absolute (quantitative) accuracy of the improved crown diameter estimates is comparable or higher for both monospecies plots and mixed-species plots than the current methods. The ability of the improved method to produce good estimates for average crown diameters for monoculture and mixed species, to use remote sensing data of various spatial resolution and to operate in automatic mode promisingly suggests its applicability to a wide range of forest systems.
NASA Astrophysics Data System (ADS)
Gholizadeh, Hamed
2013-09-01
Hyperspectral remote sensing is capable of providing large numbers of spectral bands. The vast amount of data volume presents challenging problems for information processing, such as heavy computational burden. In this paper, the impact of dimension reduction on hyperspectral data clustering is investigated from two viewpoints: 1) computational complexity; and 2) clustering performance. Clustering is one of the most useful tasks in data mining process. So, investigating the impact of dimension reduction on hyperspectral data clustering is justifiable. The proposed approach is based on thresholding the band correlation matrix and selecting the least correlated bands. Selected bands are then used to cluster the hyperspectral image. Experimental results on a real-world hyperspectral remote sensing data proved that the proposed approach will decrease computational complexity and lead to better clustering results. For evaluating the clustering performance, the Calinski-Harabasz, Davies-Bouldin and Krzanowski-Lai indices are used. These indices evaluate the clustering results using quantities and features inherent in the dataset. In other words, they do not need any external information.
NASA Astrophysics Data System (ADS)
Wu, Tin-Yu; Chang, Tse; Chu, Teng-Hao
2017-02-01
Many data mining adopts the form of Artificial Neural Network (ANN) to solve many problems, many problems will be involved in the process of training Artificial Neural Network, such as the number of samples with volume label, the time and performance of training, the number of hidden layers and Transfer function, if the compared data results are not expected, it cannot be known clearly that which dimension causes the deviation, the main reason is that Artificial Neural Network trains compared results through the form of modifying weight, and it is not a kind of training to improve the original algorithm for the extraction algorithm of image, but tend to obtain correct value aimed at the result plus the weigh; in terms of these problems, this paper will mainly put forward a method to assist in the image data analysis of Artificial Neural Network; normally, a parameter will be set as the value to extract feature vector during processing the image, which will be considered by us as weight, the experiment will use the value extracted from feature point of Speeded Up Robust Features (SURF) Image as the basis for training, SURF itself can extract different feature points according to extracted values, we will make initial semi-supervised clustering according to these values, and use Modified K - on his Neighbors (MFKNN) as training and classification, the matching mode of unknown images is not one-to-one complete comparison, but only compare group Centroid, its main purpose is to save its efficiency and speed up, and its retrieved data results will be observed and analyzed eventually; the method is mainly to make clustering and classification with the use of the nature of image feature point to give values to groups with high error rate to produce new feature points and put them into Input Layer of Artificial Neural Network for training, and finally comparative analysis is made with Back-Propagation Neural Network (BPN) of Genetic Algorithm-Artificial Neural Network
Fialkov, Alexander B; Amirav, Aviv
2003-01-01
Upon the supersonic expansion of helium mixed with vapor from an organic solvent (e.g. methanol), various clusters of the solvent with the sample molecules can be formed. As a result of 70 eV electron ionization of these clusters, cluster chemical ionization (cluster CI) mass spectra are obtained. These spectra are characterized by the combination of EI mass spectra of vibrationally cold molecules in the supersonic molecular beam (cold EI) with CI-like appearance of abundant protonated molecules, together with satellite peaks corresponding to protonated or non-protonated clusters of sample compounds with 1-3 solvent molecules. Like CI, cluster CI preferably occurs for polar compounds with high proton affinity. However, in contrast to conventional CI, for non-polar compounds or those with reduced proton affinity the cluster CI mass spectrum converges to that of cold EI. The appearance of a protonated molecule and its solvent cluster peaks, plus the lack of protonation and cluster satellites for prominent EI fragments, enable the unambiguous identification of the molecular ion. In turn, the insertion of the proper molecular ion into the NIST library search of the cold EI mass spectra eliminates those candidates with incorrect molecular mass and thus significantly increases the confidence level in sample identification. Furthermore, molecular mass identification is of prime importance for the analysis of unknown compounds that are absent in the library. Examples are given with emphasis on the cluster CI analysis of carbamate pesticides, high explosives and unknown samples, to demonstrate the usefulness of Supersonic GC/MS (GC/MS with supersonic molecular beam) in the analysis of these thermally labile compounds. Cluster CI is shown to be a practical ionization method, due to its ease-of-use and fast instrumental conversion between EI and cluster CI, which involves the opening of only one valve located at the make-up gas path. The ease-of-use of cluster CI is analogous
Improving the S-Shape Solar Radiation Estimation Method for Supporting Crop Models
Fodor, Nándor
2012-01-01
In line with the critical comments formulated in relation to the S-shape global solar radiation estimation method, the original formula was improved via a 5-step procedure. The improved method was compared to four-reference methods on a large North-American database. According to the investigated error indicators, the final 7-parameter S-shape method has the same or even better estimation efficiency than the original formula. The improved formula is able to provide radiation estimates with a particularly low error pattern index (PIdoy) which is especially important concerning the usability of the estimated radiation values in crop models. Using site-specific calibration, the radiation estimates of the improved S-shape method caused an average of 2.72 ± 1.02 (α = 0.05) relative error in the calculated biomass. Using only readily available site specific metadata the radiation estimates caused less than 5% relative error in the crop model calculations when they were used for locations in the middle, plain territories of the USA. PMID:22645451
Walters, William A.; Lennon, Niall J.; Bochicchio, James; Krohn, Andrew; Pennanen, Taina
2016-01-01
ABSTRACT While high-throughput sequencing methods are revolutionizing fungal ecology, recovering accurate estimates of species richness and abundance has proven elusive. We sought to design internal transcribed spacer (ITS) primers and an Illumina protocol that would maximize coverage of the kingdom Fungi while minimizing nontarget eukaryotes. We inspected alignments of the 5.8S and large subunit (LSU) ribosomal genes and evaluated potential primers using PrimerProspector. We tested the resulting primers using tiered-abundance mock communities and five previously characterized soil samples. We recovered operational taxonomic units (OTUs) belonging to all 8 members in both mock communities, despite DNA abundances spanning 3 orders of magnitude. The expected and observed read counts were strongly correlated (r = 0.94 to 0.97). However, several taxa were consistently over- or underrepresented, likely due to variation in rRNA gene copy numbers. The Illumina data resulted in clustering of soil samples identical to that obtained with Sanger sequence clone library data using different primers. Furthermore, the two methods produced distance matrices with a Mantel correlation of 0.92. Nonfungal sequences comprised less than 0.5% of the soil data set, with most attributable to vascular plants. Our results suggest that high-throughput methods can produce fairly accurate estimates of fungal abundances in complex communities. Further improvements might be achieved through corrections for rRNA copy number and utilization of standardized mock communities. IMPORTANCE Fungi play numerous important roles in the environment. Improvements in sequencing methods are providing revolutionary insights into fungal biodiversity, yet accurate estimates of the number of fungal species (i.e., richness) and their relative abundances in an environmental sample (e.g., soil, roots, water, etc.) remain difficult to obtain. We present improved methods for high-throughput Illumina sequencing of the
NASA Astrophysics Data System (ADS)
Giannantonio, Tommaso; Ross, Ashley J.; Percival, Will J.; Crittenden, Robert; Bacher, David; Kilbinger, Martin; Nichol, Robert; Weller, Jochen
2014-01-01
We present the strongest robust constraints on primordial non-Gaussianity (PNG) from currently available galaxy surveys, combining large-scale clustering measurements and their cross correlations with the cosmic microwave background. We update the data sets used by Giannantonio et al. (2012), and broaden that analysis to include the full set of two-point correlation functions between all surveys. In order to obtain the most reliable constraints on PNG, we advocate the use of the cross correlations between the catalogs as a robust estimator and we perform an extended analysis of the possible systematics to reduce their impact on the results. To minimize the impact of stellar contamination in our luminous red galaxy sample, we use the recent Baryon Oscillations Spectroscopic Survey catalog of Ross et al. (2011). We also find evidence for a new systematic in the NVSS radio galaxy survey similar to, but smaller than, the known declination-dependent issue; this is difficult to remove without affecting the inferred PNG signal, and thus we do not include the NVSS autocorrelation function in our analyses. We find no evidence of primordial non-Gaussianity; for the local-type configuration we obtain for the skewness parameter -36
NASA Astrophysics Data System (ADS)
Chen, Y.; Ho, C.; Chang, L.
2011-12-01
In previous decades, the climate change caused by global warming increases the occurrence frequency of extreme hydrological events. Water supply shortages caused by extreme events create great challenges for water resource management. To evaluate future climate variations, general circulation models (GCMs) are the most wildly known tools which shows possible weather conditions under pre-defined CO2 emission scenarios announced by IPCC. Because the study area of GCMs is the entire earth, the grid sizes of GCMs are much larger than the basin scale. To overcome the gap, a statistic downscaling technique can transform the regional scale weather factors into basin scale precipitations. The statistic downscaling technique can be divided into three categories include transfer function, weather generator and weather type. The first two categories describe the relationships between the weather factors and precipitations respectively based on deterministic algorithms, such as linear or nonlinear regression and ANN, and stochastic approaches, such as Markov chain theory and statistical distributions. In the weather type, the method has ability to cluster weather factors, which are high dimensional and continuous variables, into weather types, which are limited number of discrete states. In this study, the proposed downscaling model integrates the weather type, using the K-means clustering algorithm, and the weather generator, using the kernel density estimation. The study area is Shihmen basin in northern of Taiwan. In this study, the research process contains two steps, a calibration step and a synthesis step. Three sub-steps were used in the calibration step. First, weather factors, such as pressures, humidities and wind speeds, obtained from NCEP and the precipitations observed from rainfall stations were collected for downscaling. Second, the K-means clustering grouped the weather factors into four weather types. Third, the Markov chain transition matrixes and the
An improved Combes-Thomas estimate of magnetic Schrödinger operators
NASA Astrophysics Data System (ADS)
Shen, Zhongwei
2014-10-01
In the present paper, we prove an improved Combes-Thomas estimate, viz. the Combes-Thomas estimate in trace-class norms, for magnetic Schrödinger operators under general assumptions. In particular, we allow for unbounded potentials. We also show that for any function in the Schwartz space on the reals the operator kernel decays, in trace-class norms, faster than any polynomial.
Pollutant discharges to coastal areas: Improving upstream source estimates. Final report
Rohmann, S.O.
1989-10-01
The report describes a project NOAA's Strategic Environmental Assessments Division began to improve the estimates of pollutant discharges carried into coastal areas by rivers and streams. These estimates, termed discharges from upstream sources, take into account all pollution discharged by industries, sewage treatment plants, farms, cities, and other pollution-generating operations, as well as natural phenomena such as erosion and weathering which occur inland or upstream of the coastal US.
Improved initialisation of model-based clustering using Gaussian hierarchical partitions
Scrucca, Luca; Raftery, Adrian E.
2015-01-01
Initialisation of the EM algorithm in model-based clustering is often crucial. Various starting points in the parameter space often lead to different local maxima of the likelihood function and, so to different clustering partitions. Among the several approaches available in the literature, model-based agglomerative hierarchical clustering is used to provide initial partitions in the popular mclust R package. This choice is computationally convenient and often yields good clustering partitions. However, in certain circumstances, poor initial partitions may cause the EM algorithm to converge to a local maximum of the likelihood function. We propose several simple and fast refinements based on data transformations and illustrate them through data examples. PMID:26949421
Tarone, Aaron M; Foran, David R
2011-01-01
Forensic entomologists use size and developmental stage to estimate blow fly age, and from those, a postmortem interval. Since such estimates are generally accurate but often lack precision, particularly in the older developmental stages, alternative aging methods would be advantageous. Presented here is a means of incorporating developmentally regulated gene expression levels into traditional stage and size data, with a goal of more precisely estimating developmental age of immature Lucilia sericata. Generalized additive models of development showed improved statistical support compared to models that did not include gene expression data, resulting in an increase in estimate precision, especially for postfeeding third instars and pupae. The models were then used to make blind estimates of development for 86 immature L. sericata raised on rat carcasses. Overall, inclusion of gene expression data resulted in increased precision in aging blow flies.
Improvement of ocean state estimation by assimilating mapped Argo drift data.
Masuda, Shuhei; Sugiura, Nozomi; Osafune, Satoshi; Doi, Toshimasa
2014-01-01
We investigated the impact of assimilating a mapped dataset of subsurface ocean currents into an ocean state estimation. We carried out two global ocean state estimations from 2000 to 2007 using the K7 four-dimensional variational data synthesis system, one of which included an additional map of climatological geostrophic currents estimated from the global set of Argo floats. We assessed the representativeness of the volume transport in the two exercises. The assimilation of Argo ocean current data at only one level, 1000 dbar depth, had subtle impacts on the estimated volume transports, which were strongest in the subtropical North Pacific. The corrections at 10(°)N, where the impact was most notable, arose through the nearly complete offset of wind stress curl by the data synthesis system in conjunction with the first mode baroclinic Rossby wave adjustment. Our results imply that subsurface current data can be effective for improving the estimation of global oceanic circulation by a data synthesis.
Improving quality of sample entropy estimation for continuous distribution probability functions
NASA Astrophysics Data System (ADS)
Miśkiewicz, Janusz
2016-05-01
Entropy is a one of the key parameters characterizing state of system in statistical physics. Although, the entropy is defined for systems described by discrete and continuous probability distribution function (PDF), in numerous applications the sample entropy is estimated by a histogram, which, in fact, denotes that the continuous PDF is represented by a set of probabilities. Such a procedure may lead to ambiguities and even misinterpretation of the results. Within this paper, two possible general algorithms based on continuous PDF estimation are discussed in the application to the Shannon and Tsallis entropies. It is shown that the proposed algorithms may improve entropy estimation, particularly in the case of small data sets.
NASA Astrophysics Data System (ADS)
Olson, W. S.; Grecu, M.; Munchak, S. J.; Kuo, K. S.; Johnson, B. T.; Haddad, Z. S.; Tian, L.; Liao, L.; Kelley, B. L.; Ringerud, S.
2015-12-01
In recent satellite missions, spaceborne radar observations, sometimes in combination with passive microwave radiometer measurements, are being used to estimate vertical profiles of precipitation rates. Launched in 2014, the Global Precipitation Measurement (GPM) mission core satellite observatory features a dual-frequency radar operating at 13.6 and 35.5 GHz (Ku and Ka bands) and a microwave radiometer with thirteen channels from 10 - 183 GHz. The use of combined radar and radiometer observations should yield the most accurate estimates of precipitation profiles from space, and these estimates will ultimately serve as a crucial reference for cross-calibrating passive microwave precipitation estimates from the GPM radiometer constellation. And through the microwave radiometer estimates, the combined algorithm calibration will ultimately be propagated to GPM infrared-microwave multisatellite estimates of surface rainfall. The GPM combined precipitation estimation algorithm performs initial estimates (an "ensemble") of precipitation profiles based upon an observed Ku-band reflectivity profile and different a priori assumptions concerning the size distributions of the precipitation particles and the profiles of cloud water and water vapor in the atmospheric column. The initial ensemble of profiles is then updated using a filter that embodies the physics relating precipitation to the observed Ka reflectivity profile, Ku and Ka path-integrated attenuation (derived from radar surface backscatter measurements), and microwave radiances. The final, filtered ensemble of profiles is consistent with all the available radar-radiometer data and a priori information. Since the GPM launch, the combined radar-radiometer algorithm has been improved to more specifically account for the effects of radar non-uniform beamfilling, multiple-scattering of radar pulses, the different resolutions of the radar and radiometer observations, interrelated radar and passive microwave surface
Comerford, Julia M.; Moustakas, Leonidas A.; Natarajan, Priyamvada
2010-05-20
Scaling relations of observed galaxy cluster properties are useful tools for constraining cosmological parameters as well as cluster formation histories. One of the key cosmological parameters, {sigma}{sub 8}, is constrained using observed clusters of galaxies, although current estimates of {sigma}{sub 8} from the scaling relations of dynamically relaxed galaxy clusters are limited by the large scatter in the observed cluster mass-temperature (M-T) relation. With a sample of eight strong lensing clusters at 0.3 < z < 0.8, we find that the observed cluster concentration-mass relation can be used to reduce the M-T scatter by a factor of 6. Typically only relaxed clusters are used to estimate {sigma}{sub 8}, but combining the cluster concentration-mass relation with the M-T relation enables the inclusion of unrelaxed clusters as well. Thus, the resultant gains in the accuracy of {sigma}{sub 8} measurements from clusters are twofold: the errors on {sigma}{sub 8} are reduced and the cluster sample size is increased. Therefore, the statistics on {sigma}{sub 8} determination from clusters are greatly improved by the inclusion of unrelaxed clusters. Exploring cluster scaling relations further, we find that the correlation between brightest cluster galaxy (BCG) luminosity and cluster mass offers insight into the assembly histories of clusters. We find preliminary evidence for a steeper BCG luminosity-cluster mass relation for strong lensing clusters than the general cluster population, hinting that strong lensing clusters may have had more active merging histories.
Estimation of root zone storage capacity at the catchment scale using improved Mass Curve Technique
NASA Astrophysics Data System (ADS)
Zhao, Jie; Xu, Zongxue; Singh, Vijay P.
2016-09-01
The root zone storage capacity (Sr) greatly influences runoff generation, soil water movement, and vegetation growth and is hence an important variable for ecological and hydrological modelling. However, due to the great heterogeneity in soil texture and structure, there seems to be no effective approach to monitor or estimate Sr at the catchment scale presently. To fill the gap, in this study the Mass Curve Technique (MCT) was improved by incorporating a snowmelt module for the estimation of Sr at the catchment scale in different climatic regions. The ;range of perturbation; method was also used to generate different scenarios for determining the sensitivity of the improved MCT-derived Sr to its influencing factors after the evaluation of plausibility of Sr derived from the improved MCT. Results can be showed as: (i) Sr estimates of different catchments varied greatly from ∼10 mm to ∼200 mm with the changes of climatic conditions and underlying surface characteristics. (ii) The improved MCT is a simple but powerful tool for the Sr estimation in different climatic regions of China, and incorporation of more catchments into Sr comparisons can further improve our knowledge on the variability of Sr. (iii) Variation of Sr values is an integrated consequence of variations in rainfall, snowmelt water and evapotranspiration. Sr values are most sensitive to variations in evapotranspiration of ecosystems. Besides, Sr values with a longer return period are more stable than those with a shorter return period when affected by fluctuations in its influencing factors.
Use of spot measurements to improve the estimation of low streamflow statistics.
NASA Astrophysics Data System (ADS)
Kroll, C. N.; Stagnitta, T. J.; Vogel, R. M.
2015-12-01
Despite substantial efforts to improve the modeling and prediction of low streamflows at ungauged river sites, most models of low streamflow statistics create estimators with large errors. Often this is because the hydrogeologic characteristics of a watershed, which can strongly impact low streamflows, are difficult to characterize. One solution is to take a nominal number of streamflow measurements at an ungauged site to either estimate improved hydrogeologic indices or correlate with concurrent streamflow measurements at a nearby gauged river site. Past results have indicated that baseflow correlation performs better than regional regression when 4 or more streamflow measurements are available, even when the regional regression models are augmented by improved hydrogeologic indices. Here we revisit this issue within the 19,800 square mile Apalachicola-Chattahoochee-Flint watershed, a USGS WaterSMART region spanning Geogia, southeastern Alabama, and northwestern Florida. This study area is of particular interest because numerous watershed modeling analyses have previously been performed using gauged river sites within this basin. Initial results indicate that baseflow correlation can produce improved estimators when spot-measurements are available, but selection of an appropriate donor site is problematic, especially in regions with a small number of gauged river sites. Estimation of hydrogeologic indices do improve regional regression models, but these models are generally outperformed by baseflow correlation.
The Role of Satellite Imagery to Improve Pastureland Estimates in South America
NASA Astrophysics Data System (ADS)
Graesser, J.
2015-12-01
Agriculture has changed substantially across the globe over the past half century. While much work has been done to improve spatial-temporal estimates of agricultural changes, we still know more about the extent of row-crop agriculture than livestock-grazed land. The gap between cropland and pastureland estimates exists largely because it is challenging to characterize natural versus grazed grasslands from a remote sensing perspective. However, the impasse of pastureland estimates is set to break, with an increasing number of spaceborne sensors and freely available satellite data. The Landsat satellite archive in particular provides researchers with immense amounts of data to improve pastureland information. Here we focus on South America, where pastureland expansion has been scrutinized for the past few decades. We explore the challenges of estimating pastureland using temporal Landsat imagery and focus on key agricultural countries, regions, and ecosystems. We focus on the suggested shift of pastureland from the Argentine Pampas to northern Argentina, and the mixing of small-scale and large-scale ranching in eastern Paraguay and how it could impact the Chaco forest to the west. Further, the Beni Savannahs of northern Bolivia and the Colombian Llanos—both grassland and savannah regions historically used for livestock grazing—have been hinted at as future areas for cropland expansion. There are certainly environmental concerns with pastureland expansion into forests; but what are the environmental implications when well-managed pasture systems are converted to intensive soybean or palm oil plantation? Tropical, grazed grasslands are important habitats for biodiversity, and pasturelands can mitigate soil erosion when well managed. Thus, we must improve estimates of grazed land before we can make informed policy and conservation decisions. This talk presents insights into pastureland estimates in South America and discusses the feasibility to improve current
Improving the realism of hydrologic model functioning through multivariate parameter estimation
NASA Astrophysics Data System (ADS)
Rakovec, O.; Kumar, R.; Attinger, S.; Samaniego, L.
2016-10-01
Increased availability and quality of near real-time observations provide the opportunity to improve understanding of predictive skills of hydrologic models. Recent studies have shown the limited capability of river discharge data alone to adequately constrain different components of distributed model parameterizations. In this study, the GRACE satellite-based total water storage (TWS) anomaly is used to complement the discharge data with the aim to improve the fidelity of mesoscale hydrologic model (mHM) through multivariate parameter estimation. The study is conducted on 83 European basins covering a wide range of hydroclimatic regimes. The model parameterization complemented with the TWS anomalies leads to statistically significant improvements in (1) discharge simulations during low-flow period, and (2) evapotranspiration estimates which are evaluated against independent data (FLUXNET). Overall, there is no significant deterioration in model performance for the discharge simulations when complemented by information from the TWS anomalies. However, considerable changes in the partitioning of precipitation into runoff components are noticed by in-/exclusion of TWS during the parameter estimation. Introducing monthly averaged TWS data only improves the dynamics of streamflow on monthly or longer time scales, which mostly addresses the dynamical behavior of the base flow reservoir. A cross-evaluation test carried out to assess the transferability of the calibrated parameters to other locations further confirms the benefit of complementary TWS data. In particular, the evapotranspiration estimates show more robust performance when TWS data are incorporated during the parameter estimation, in comparison with the benchmark model constrained against discharge only. This study highlights the value for incorporating multiple data sources during parameter estimation to improve the overall realism of hydrologic models and their applications over large domains.
Uncertainty in Population Estimates for Endangered Animals and Improving the Recovery Process
Haines, Aaron M.; Zak, Matthew; Hammond, Katie; Scott, J. Michael; Goble, Dale D.; Rachlow, Janet L.
2013-01-01
Simple Summary The objective of our study was to evaluate the mention of uncertainty (i.e., variance) associated with population size estimates within U.S. recovery plans for endangered animals. To do this we reviewed all finalized recovery plans for listed terrestrial vertebrate species. We found that more recent recovery plans reported more estimates of population size and uncertainty. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty. We recommend that updated recovery plans combine uncertainty of population size estimates with a minimum detectable difference to aid in successful recovery. Abstract United States recovery plans contain biological information for a species listed under the Endangered Species Act and specify recovery criteria to provide basis for species recovery. The objective of our study was to evaluate whether recovery plans provide uncertainty (e.g., variance) with estimates of population size. We reviewed all finalized recovery plans for listed terrestrial vertebrate species to record the following data: (1) if a current population size was given, (2) if a measure of uncertainty or variance was associated with current estimates of population size and (3) if population size was stipulated for recovery. We found that 59% of completed recovery plans specified a current population size, 14.5% specified a variance for the current population size estimate and 43% specified population size as a recovery criterion. More recent recovery plans reported more estimates of current population size, uncertainty and population size as a recovery criterion. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty compared to reptiles and amphibians. We suggest the use of calculating minimum detectable differences to improve confidence when delisting endangered animals and we identified incentives for individuals to get involved in recovery planning to improve access to
An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance
NASA Technical Reports Server (NTRS)
Keel, Byron M.
1996-01-01
from a closed system is shown to improve through the application of additional autocorrelation lags in an overdetermined system. This improvement is greater in the narrowband spectrum region where the information is spread over more lags of the autocorrelation function. The number of lags needed in the overdetermined system is a function of the spectral width, the number of terms in the series expansion, the number of samples used in estimating the autocorrelation function, and the signal-to-noise ratio. The overdetermined system provides a robustness to the chosen variance estimator by expanding the region of spectral widths and signal-to-noise ratios over which the estimator can perform as compared to the closed system.
Improved battery parameter estimation method considering operating scenarios for HEV/EV applications
Yang, Jufeng; Xia, Bing; Shang, Yunlong; Huang, Wenxin; Mi, Chris
2016-12-22
This study presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.
Improved battery parameter estimation method considering operating scenarios for HEV/EV applications
Yang, Jufeng; Xia, Bing; Shang, Yunlong; ...
2016-12-22
This study presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted datasetmore » is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.« less
Tu, Xiaoguang; Gao, Jingjing; Zhu, Chongjing; Cheng, Jie-Zhi; Ma, Zheng; Dai, Xin; Xie, Mei
2016-12-01
Though numerous segmentation algorithms have been proposed to segment brain tissue from magnetic resonance (MR) images, few of them consider combining the tissue segmentation and bias field correction into a unified framework while simultaneously removing the noise. In this paper, we present a new unified MR image segmentation algorithm whereby tissue segmentation, bias correction and noise reduction are integrated within the same energy model. Our method is presented by a total variation term introduced to the coherent local intensity clustering criterion function. To solve the nonconvex problem with respect to membership functions, we add auxiliary variables in the energy function such as Chambolle's fast dual projection method can be used and the optimal segmentation and bias field estimation can be achieved simultaneously throughout the reciprocal iteration. Experimental results show that the proposed method has a salient advantage over the other three baseline methods on either tissue segmentation or bias correction, and the noise is significantly reduced via its applications on highly noise-corrupted images. Moreover, benefiting from the fast convergence of the proposed solution, our method is less time-consuming and robust to parameter setting.
Technology Transfer Automated Retrieval System (TEKTRAN)
An Ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorpo...
NASA Astrophysics Data System (ADS)
Kato, Takeyoshi; Suzuoki, Yasuo
The fluctuation of the total power output of clustered PV systems would be smaller than that of single PV system because of the time difference in the power output fluctuation among PV systems at different locations. This effect, so called smoothing-effect, must be taken into account properly when the impact of clustered PV systems on electric power system is assessed. If the average power output of clustered PV systems can be estimated from the power output of single PV system, it is very useful and helpful for the impact assessment. In this study, we propose a simple method to estimate the total power output fluctuation of clustered PV systems. In the proposed method, a smoothing effect is assumed to be caused as a result of two factors, i.e. time difference of overhead clouds passing among PV systems and the random change in the size and/or shape of clouds. The first one is formulated as a low-pass filter, assuming that output fluctuation is transmitted to the same direction as the wind direction at the constant speed. The second one is taken into account by using a Fourier transform surrogate data. The parameters in the proposed method were selected, so that the estimated fluctuation can be similar with that of ensemble average fluctuation of data observed at 5 points used as a training data set. Then, by using the selected parameters, the fluctuation property was estimated for other data set. The results show that the proposed method is useful for estimating the total power output fluctuation of clustered PV systems.
NASA Astrophysics Data System (ADS)
Hasan, Mohammad Mahadi; Sharma, Ashish; Mariethoz, Gregoire; Johnson, Fiona; Seed, Alan
2016-11-01
While the value of correcting raw radar rainfall estimates using simultaneous ground rainfall observations is well known, approaches that use the complete record of both gauge and radar measurements to provide improved rainfall estimates are much less common. We present here two new approaches for estimating radar rainfall that are designed to address known limitations in radar rainfall products by using a relatively long history of radar reflectivity and ground rainfall observations. The first of these two approaches is a radar rainfall estimation algorithm that is nonparametric by construction. Compared to the traditional gauge adjusted parametric relationship between reflectivity (Z) and ground rainfall (R), the suggested new approach is based on a nonparametric radar rainfall estimation method (NPR) derived using the conditional probability distribution of reflectivity and gauge rainfall. The NPR method is applied to the densely gauged Sydney Terrey Hills radar network, where it reduces the RMSE in rainfall estimates by 10%, with improvements observed at 90% of the gauges. The second of the two approaches is a method to merge radar and spatially interpolated gauge measurements. The two sources of information are combined using a dynamic combinatorial algorithm with weights that vary in both space and time. The weight for any specific period is calculated based on the error covariance matrix that is formulated from the radar and spatially interpolated rainfall errors of similar reflectivity periods in a cross-validation setting. The combination method reduces the RMSE by about 20% compared to the traditional Z-R relationship method, and improves estimates compared to spatially interpolated point measurements in sparsely gauged areas.
Improving Ocean Angular Momentum Estimates Using a Model Constrained by Data
NASA Technical Reports Server (NTRS)
Ponte, Rui M.; Stammer, Detlef; Wunsch, Carl
2001-01-01
Ocean angular momentum (OAM) calculations using forward model runs without any data constraints have, recently revealed the effects of OAM variability on the Earth's rotation. Here we use an ocean model and its adjoint to estimate OAM values by constraining the model to available oceanic data. The optimization procedure yields substantial changes in OAM, related to adjustments in both motion and mass fields, as well as in the wind stress torques acting on the ocean. Constrained and unconstrained OAM values are discussed in the context of closing the planet's angular momentum budget. The estimation procedure, yields noticeable improvements in the agreement with the observed Earth rotation parameters, particularly at the seasonal timescale. The comparison with Earth rotation measurements provides an independent consistency check on the estimated ocean state and underlines the importance of ocean state estimation for quantitative. studies of the variable large-scale oceanic mass and circulation fields, including studies of OAM.
Parameter estimation for chaotic systems based on improved boundary chicken swarm optimization
NASA Astrophysics Data System (ADS)
Chen, Shaolong; Yan, Renhuan
2016-10-01
Estimating unknown parameters for chaotic system is a key problem in the field of chaos control and synchronization. Through constructing an appropriate fitness function, parameter estimation of chaotic system could be converted to a multidimensional parameter optimization problem. In this paper, a new method base on improved boundary chicken swarm optimization (IBCSO) algorithm is proposed for solving the problem of parameter estimation in chaotic system. However, to the best of our knowledge, there is no published research work on chicken swarm optimization for parameters estimation of chaotic system. Computer simulation based on Lorenz system and comparisons with chicken swarm optimization, particle swarm optimization, and genetic algorithm shows the effectiveness and feasibility of the proposed method.
Using known map category marginal frequencies to improve estimates of thematic map accuracy
NASA Technical Reports Server (NTRS)
Card, D. H.
1982-01-01
By means of two simple sampling plans suggested in the accuracy-assessment literature, it is shown how one can use knowledge of map-category relative sizes to improve estimates of various probabilities. The fact that maximum likelihood estimates of cell probabilities for the simple random sampling and map category-stratified sampling were identical has permitted a unified treatment of the contingency-table analysis. A rigorous analysis of the effect of sampling independently within map categories is made possible by results for the stratified case. It is noted that such matters as optimal sample size selection for the achievement of a desired level of precision in various estimators are irrelevant, since the estimators derived are valid irrespective of how sample sizes are chosen.
The Use of Radar to Improve Rainfall Estimation over the Tennessee and San Joaquin River Valleys
NASA Technical Reports Server (NTRS)
Petersen, Walter A.; Gatlin, Patrick N.; Felix, Mariana; Carey, Lawrence D.
2010-01-01
This slide presentation provides an overview of the collaborative radar rainfall project between the Tennessee Valley Authority (TVA), the Von Braun Center for Science & Innovation (VCSI), NASA MSFC and UAHuntsville. Two systems were used in this project, Advanced Radar for Meteorological & Operational Research (ARMOR) Rainfall Estimation Processing System (AREPS), a demonstration project of real-time radar rainfall using a research radar and NEXRAD Rainfall Estimation Processing System (NREPS). The objectives, methodology, some results and validation, operational experience and lessons learned are reviewed. The presentation. Another project that is using radar to improve rainfall estimations is in California, specifically the San Joaquin River Valley. This is part of a overall project to develop a integrated tool to assist water management within the San Joaquin River Valley. This involves integrating several components: (1) Radar precipitation estimates, (2) Distributed hydro model, (3) Snowfall measurements and Surface temperature / moisture measurements. NREPS was selected to provide precipitation component.
Improving Google Flu Trends Estimates for the United States through Transformation
Martin, Leah J.; Xu, Biying; Yasui, Yutaka
2014-01-01
Google Flu Trends (GFT) uses Internet search queries in an effort to provide early warning of increases in influenza-like illness (ILI). In the United States, GFT estimates the percentage of physician visits related to ILI (%ILINet) reported by the Centers for Disease Control and Prevention (CDC). However, during the 2012–13 influenza season, GFT overestimated %ILINet by an appreciable amount and estimated the peak in incidence three weeks late. Using data from 2010–14, we investigated the relationship between GFT estimates (%GFT) and %ILINet. Based on the relationship between the relative change in %GFT and the relative change in %ILINet, we transformed %GFT estimates to better correspond with %ILINet values. In 2010–13, our transformed %GFT estimates were within ±10% of %ILINet values for 17 of the 29 weeks that %ILINet was above the seasonal baseline value determined by the CDC; in contrast, the original %GFT estimates were within ±10% of %ILINet values for only two of these 29 weeks. Relative to the %ILINet peak in 2012–13, the peak in our transformed %GFT estimates was 2% lower and one week later, whereas the peak in the original %GFT estimates was 74% higher and three weeks later. The same transformation improved %GFT estimates using the recalibrated 2013 GFT model in early 2013–14. Our transformed %GFT estimates can be calculated approximately one week before %ILINet values are reported by the CDC and the transformation equation was stable over the time period investigated (2010–13). We anticipate our results will facilitate future use of GFT. PMID:25551391
Multiple data sources improve DNA-based mark-recapture population estimates of grizzly bears.
Boulanger, John; Kendall, Katherine C; Stetz, Jeffrey B; Roon, David A; Waits, Lisette P; Paetkau, David
2008-04-01
A fundamental challenge to estimating population size with mark-recapture methods is heterogeneous capture probabilities and subsequent bias of population estimates. Confronting this problem usually requires substantial sampling effort that can be difficult to achieve for some species, such as carnivores. We developed a methodology that uses two data sources to deal with heterogeneity and applied this to DNA mark-recapture data from grizzly bears (Ursus arctos). We improved population estimates by incorporating additional DNA "captures" of grizzly bears obtained by collecting hair from unbaited bear rub trees concurrently with baited, grid-based, hair snag sampling. We consider a Lincoln-Petersen estimator with hair snag captures as the initial session and rub tree captures as the recapture session and develop an estimator in program MARK that treats hair snag and rub tree samples as successive sessions. Using empirical data from a large-scale project in the greater Glacier National Park, Montana, USA, area and simulation modeling we evaluate these methods and compare the results to hair-snag-only estimates. Empirical results indicate that, compared with hair-snag-only data, the joint hair-snag-rub-tree methods produce similar but more precise estimates if capture and recapture rates are reasonably high for both methods. Simulation results suggest that estimators are potentially affected by correlation of capture probabilities between sample types in the presence of heterogeneity. Overall, closed population Huggins-Pledger estimators showed the highest precision and were most robust to sparse data, heterogeneity, and capture probability correlation among sampling types. Results also indicate that these estimators can be used when a segment of the population has zero capture probability for one of the methods. We propose that this general methodology may be useful for other species in which mark-recapture data are available from multiple sources.
Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis
Wang, Feng; Huisman, Jaco; Stevels, Ab; Baldé, Cornelis Peter
2013-11-15
Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e
Acquaviva, Viviana; Raichoor, Anand
2015-05-01
We seek to improve the accuracy of joint galaxy photometric redshift estimation and spectral energy distribution (SED) fitting. By simulating different sources of uncorrected systematic errors, we demonstrate that if the uncertainties in the photometric redshifts are estimated correctly, so are those on the other SED fitting parameters, such as stellar mass, stellar age, and dust reddening. Furthermore, we find that if the redshift uncertainties are over(under)-estimated, the uncertainties in SED parameters tend to be over(under)-estimated by similar amounts. These results hold even in the presence of severe systematics and provide, for the first time, a mechanism to validate the uncertainties on these parameters via comparison with spectroscopic redshifts. We propose a new technique (annealing) to re-calibrate the joint uncertainties in the photo-z and SED fitting parameters without compromising the performance of the SED fitting + photo-z estimation. This procedure provides a consistent estimation of the multi-dimensional probability distribution function in SED fitting + z parameter space, including all correlations. While the performance of joint SED fitting and photo-z estimation might be hindered by template incompleteness, we demonstrate that the latter is “flagged” by a large fraction of outliers in redshift, and that significant improvements can be achieved by using flexible stellar populations synthesis models and more realistic star formation histories. In all cases, we find that the median stellar age is better recovered than the time elapsed from the onset of star formation. Finally, we show that using a photometric redshift code such as EAZY to obtain redshift probability distributions that are then used as priors for SED fitting codes leads to only a modest bias in the SED fitting parameters and is thus a viable alternative to the simultaneous estimation of SED parameters and photometric redshifts.
Shuaib, Muhammad; Becker, Stan; Rahman, Md. Mokhlesur; Peters, David H.
2011-01-01
Due to an urgent need for information on the coverage of health service for women and children after the fall of Taliban regime in Afghanistan, a multiple indicator cluster survey (MICS) was conducted in 2003 using the outdated 1979 census as the sampling frame. When 2004 pre-census data became available, population-sampling weights were generated based on the survey-sampling scheme. Using these weights, the population estimates for seven maternal and child healthcare-coverage indicators were generated and compared with the unweighted MICS 2003 estimates. The use of sample weights provided unbiased estimates of population parameters. Results of the comparison of weighted and unweighted estimates showed some wide differences for individual provincial estimates and confidence intervals. However, the mean, median and absolute mean of the differences between weighted and unweighted estimates and their confidence intervals were close to zero for all indicators at the national level. Ranking of the five highest and the five lowest provinces on weighted and unweighted estimates also yielded similar results. The general consistency of results suggests that outdated sampling frames can be appropriate for use in similar situations to obtain initial estimates from household surveys to guide policy and programming directions. However, the power to detect change from these estimates is lower than originally planned, requiring a greater tolerance for error when the data are used as a baseline for evaluation. The generalizability of using outdated sampling frames in similar settings is qualified by the specific characteristics of the MICS 2003—low replacement rate of clusters and zero probability of inclusion of clusters created after the 1979 census. PMID:21957678
Potharla, Vishwakanth Y.; Wang, Cheng; Cheng, Yi-Qiang
2014-01-01
Spiruchostatins A and B are members of the FK228-family of natural products with potent histone deacetylase inhibitory activities and antineoplastic activities. However, their production in the wild-type strain of Pseudomonas sp. Q71576 is low. To improve the yield, the spiruchostatin biosynthetic gene cluster (spi) was first identified by rapid genome sequencing and characterized by genetic mutations. This spi gene cluster encodes a hybrid biosynthetic pathway similar to that encoded by the FK228 biosynthetic gene cluster (dep) in Chromobacterium violaceum No. 968. Each gene cluster contains a pathway regulatory gene (spiR vs. depR) but these two genes encode transcriptional activators of different classes. Overexpression of native spiR or heterologous depR in the wild-type strain of Pseudomonas sp. Q71576 resulted in 268% or 1,285% increase of the combined titer of spiruchostatins A and B, respectively. RT-PCR analysis indicates that overexpression of heterologous depR upregulates the expression of native spiR. PMID:24973954
Clustering LC Classification Numbers in an Online Catalog for Improved Browsability.
ERIC Educational Resources Information Center
Huestis, Jeffrey C.
1988-01-01
Describes the role of browsing in online searching and difficulties encountered in attempting to browse call number indexes. The discussion covers the benefits of clustering class number ranges and strategies for overcoming the major problems related to Library of Congress classification use in an online catalog. (11 references) (CLB)
Improved factor analysis of dynamic PET images to estimate arterial input function and tissue curves
NASA Astrophysics Data System (ADS)
Boutchko, Rostyslav; Mitra, Debasis; Pan, Hui; Jagust, William; Gullberg, Grant T.
2015-03-01
Factor analysis of dynamic structures (FADS) is a methodology of extracting time-activity curves (TACs) for corresponding different tissue types from noisy dynamic images. The challenges of FADS include long computation time and sensitivity to the initial guess, resulting in convergence to local minima far from the true solution. We propose a method of accelerating and stabilizing FADS application to sequences of dynamic PET images by adding preliminary cluster analysis of the time activity curves for individual voxels. We treat the temporal variation of individual voxel concentrations as a set of time-series and use a partial clustering analysis to identify the types of voxel TACs that are most functionally distinct from each other. These TACs provide a good initial guess for the temporal factors for subsequent FADS processing. Applying this approach to a set of single slices of dynamic 11C-PIB images of the brain allows identification of the arterial input function and two different tissue TACs that are likely to correspond to the specific and non-specific tracer binding-tissue types. These results enable us to perform direct classification of tissues based on their pharmacokinetic properties in dynamic PET without relying on a compartment-based kinetic model, without identification of the reference region, or without using any external methods of estimating the arterial input function, as needed in some techniques.
Kappa statistic for clustered matched-pair data.
Yang, Zhao; Zhou, Ming
2014-07-10
Kappa statistic is widely used to assess the agreement between two procedures in the independent matched-pair data. For matched-pair data collected in clusters, on the basis of the delta method and sampling techniques, we propose a nonparametric variance estimator for the kappa statistic without within-cluster correlation structure or distributional assumptions. The results of an extensive Monte Carlo simulation study demonstrate that the proposed kappa statistic provides consistent estimation and the proposed variance estimator behaves reasonably well for at least a moderately large number of clusters (e.g., K ≥50). Compared with the variance estimator ignoring dependence within a cluster, the proposed variance estimator performs better in maintaining the nominal coverage probability when the intra-cluster correlation is fair (ρ ≥0.3), with more pronounced improvement when ρ is further increased. To illustrate the practical application of the proposed estimator, we analyze two real data examples of clustered matched-pair data.
Kang, Yunhee; Kim, Sungtae; Sinamo, Sisay; Christian, Parul
2017-01-01
Few trials have shown that promoting complementary feeding among young children is effective in improving child linear growth in resource-challenged settings. We designed a community-based participatory nutrition promotion (CPNP) programme adapting a Positive Deviance/Hearth approach that engaged mothers in 2-week nutrition sessions using the principles of 'learning by doing' around child feeding. We aimed to test the effectiveness of the CPNP for improving child growth in rural Ethiopia. A cluster randomized trial was implemented by adding the CPNP to the existing government nutrition programmes (six clusters) vs. government programmes only (six clusters). A total of 1790 children aged 6 to 12 months (876 in the intervention and 914 in the control areas) were enrolled and assessed on anthropometry every 3 months for a year. Multi-level mixed-effect regression analysis of longitudinal outcome data (n = 1475) examined the programme impact on growth, adjusting for clustering and enrollment characteristics. Compared with children 6 to 24 months of age in the control area, those in the intervention area had a greater increase in z scores for length-for-age [difference (diff): 0.021 z score/month, 95% CI: 0.008, 0.034] and weight-for-length (diff: 0.042 z score/month, 95% CI: 0.024, 0.059). At the end of the 12-month follow-up, children in the intervention area showed an 8.1% (P = 0.02) and 6.3% (P = 0.046) lower prevalence of stunting and underweight, respectively, after controlling for differences in the prevalence at enrollment, compared with the control group. A novel CPNP programme was effective in improving child growth and reducing undernutrition in this setting. © 2016 John Wiley & Sons Ltd.
NASA Astrophysics Data System (ADS)
Liu, Dawei; Li, Lin; Sun, Ying
2015-06-01
An improved Hapke's radiative transfer model (RTM) is presented to estimate mineral abundance for both immature and mature lunar soils from the Lunar Soil Characterization Consortium (LSCC) dataset. Fundamental to this improved Hapke's model is the application of an alternative equation to describe the effects of larger size submicroscopic metallic iron (SMFe) (>50 nm) in the interior of agglutinitic glass that mainly darken the host material, contrasting to the darkening and reddening effects of smaller size SMFe (<50 nm) residing in the rims of mineral grains. Results from applying a nonlinear inversion procedure to the improved Hapke's RTM show that the average mass fraction of smaller and larger size SMFe in lunar soils was estimated to be 0.30% and 0.31% respectively, and the particle size distribution of soil samples is all within their measured range. Based on the derived mass fraction of SMFe and particle size of the soil samples, abundances of end-member components composing lunar soil samples were derived via minimizing the difference between measured and calculated spectra. The root mean square error (RMSE) between the fitted and measured spectra is lower than 0.01 for highland samples and 0.005 for mare samples. This improved Hapke's model accurately estimates abundances of agglutinitic glass (R-squared = 0.88), pyroxene (R-squared = 0.69) and plagioclase (R-squared = 0.95) for all 57 samples used in this study including both immature and mature lunar soils. However, the improved Hapke's RTM shows poor performance for quantifying abundances of olivine, ilmenite and volcanic glass. Improving the model performance for estimation of these three end-member components is the central focus for our future work.
Zheng, Xiujuan; Tian, Guangjian; Huang, Sung-Cheng; Feng, Dagan
2011-01-01
Tracer kinetic modeling with dynamic Positron Emission Tomography (PET) requires a plasma time-activity curve (PTAC) as an input function. Several image-derived input function (IDIF) methods that rely on drawing the region-of-interest (ROI) in large vascular structures have been proposed to overcome the problems caused by the invasive approach to obtaining the PTAC, especially for small animal studies. However, the manual placement of ROIs for estimating IDIF is subjective and labor-intensive, making it an undesirable and unreliable process. In this paper, we propose a novel hybrid clustering method (HCM) that objectively delineates ROIs in dynamic PET images for the estimation of IDIFs, and demonstrate its application to the mouse PET studies acquired with [18F]Fluoro-2-deoxy-2-D-glucose (FDG). We begin our HCM using K-means clustering for background removal. We then model the time-activity curves using polynomial regression mixture models in curve clustering for heart structure detection. The hierarchical clustering is finally applied for ROI refinements. The HCM achieved accurate ROI delineation in both computer simulations and experimental mouse studies. In the mouse studies the predicted IDIF had a high correlation with the gold standard, the PTAC derived from the invasive blood samples. The results indicate that the proposed HCM has a great potential in ROI delineation for automatic estimation of IDIF in dynamic FDG-PET studies. PMID:20952342
Li, Ying; Wang, Hong; Li, Xiao Bing
2015-01-01
Vegetation is an important part of ecosystem and estimation of fractional vegetation cover is of significant meaning to monitoring of vegetation growth in a certain region. With Landsat TM images and HJ-1B images as data source, an improved selective endmember linear spectral mixture model (SELSMM) was put forward in this research to estimate the fractional vegetation cover in Huangfuchuan watershed in China. We compared the result with the vegetation coverage estimated with linear spectral mixture model (LSMM) and conducted accuracy test on the two results with field survey data to study the effectiveness of different models in estimation of vegetation coverage. Results indicated that: (1) the RMSE of the estimation result of SELSMM based on TM images is the lowest, which is 0.044. The RMSEs of the estimation results of LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.052, 0.077 and 0.082, which are all higher than that of SELSMM based on TM images; (2) the R2 of SELSMM based on TM images, LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.668, 0.531, 0.342 and 0.336. Among these models, SELSMM based on TM images has the highest estimation accuracy and also the highest correlation with measured vegetation coverage. Of the two methods tested, SELSMM is superior to LSMM in estimation of vegetation coverage and it is also better at unmixing mixed pixels of TM images than pixels of HJ-1B images. So, the SELSMM based on TM images is comparatively accurate and reliable in the research of regional fractional vegetation cover estimation.
Using flow cytometry to estimate pollen DNA content: improved methodology and applications
Kron, Paul; Husband, Brian C.
2012-01-01
Background and Aims Flow cytometry has been used to measure nuclear DNA content in pollen, mostly to understand pollen development and detect unreduced gametes. Published data have not always met the high-quality standards required for some applications, in part due to difficulties inherent in the extraction of nuclei. Here we describe a simple and relatively novel method for extracting pollen nuclei, involving the bursting of pollen through a nylon mesh, compare it with other methods and demonstrate its broad applicability and utility. Methods The method was tested across 80 species, 64 genera and 33 families, and the data were evaluated using established criteria for estimating genome size and analysing cell cycle. Filter bursting was directly compared with chopping in five species, yields were compared with published values for sonicated samples, and the method was applied by comparing genome size estimates for leaf and pollen nuclei in six species. Key Results Data quality met generally applied standards for estimating genome size in 81 % of species and the higher best practice standards for cell cycle analysis in 51 %. In 41 % of species we met the most stringent criterion of screening 10 000 pollen grains per sample. In direct comparison with two chopping techniques, our method produced better quality histograms with consistently higher nuclei yields, and yields were higher than previously published results for sonication. In three binucleate and three trinucleate species we found that pollen-based genome size estimates differed from leaf tissue estimates by 1·5 % or less when 1C pollen nuclei were used, while estimates from 2C generative nuclei differed from leaf estimates by up to 2·5 %. Conclusions The high success rate, ease of use and wide applicability of the filter bursting method show that this method can facilitate the use of pollen for estimating genome size and dramatically improve unreduced pollen production estimation with flow cytometry. PMID
Improvements to lawn and garden equipment emissions estimates for Baltimore, Maryland.
Reid, Stephen B; Pollard, Erin K; Sullivan, Dana Coe; Shaw, Stephanie L
2010-12-01
Lawn and garden equipment are a significant source of emissions of volatile organic compounds (VOCs) and other pollutants in suburban and urban areas. Emission estimates for this source category are typically prepared using default equipment populations and activity data contained in emissions models such as the U.S. Environmental Protection Agency's (EPA) NONROAD model or the California Air Resources Board's (CARB) OFFROAD model. Although such default data may represent national or state averages, these data are unlikely to reflect regional or local differences in equipment usage patterns because of variations in climate, lot sizes, and other variables. To assess potential errors in lawn and garden equipment emission estimates produced by the NONROAD model and to demonstrate methods that can be used by local planning agencies to improve those emission estimates, this study used bottom-up data collection techniques in the Baltimore metropolitan area to develop local equipment population, activity, and temporal data for lawn and garden equipment in the area. Results of this study show that emission estimates of VOCs, particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen oxides (NO(x)) for the Baltimore area that are based on local data collected through surveys of residential and commercial lawn and garden equipment users are 24-56% lower than estimates produced using NONROAD default data, largely because of a difference in equipment populations for high-usage commercial applications. Survey-derived emission estimates of PM and VOCs are 24 and 26% lower than NONROAD default estimates, respectively, whereas survey-derived emission estimates for CO, CO2, and NO(x) are more than 40% lower than NONROAD default estimates. In addition, study results show that the temporal allocation factors applied to residential lawn and garden equipment in the NONROAD model underestimated weekend activity levels by 30% compared with survey-derived temporal
Westgate, Philip M
2014-06-15
Generalized estimating equations are commonly used to analyze correlated data. Choosing an appropriate working correlation structure for the data is important, as the efficiency of generalized estimating equations depends on how closely this structure approximates the true structure. Therefore, most studies have proposed multiple criteria to select the working correlation structure, although some of these criteria have neither been compared nor extensively studied. To ease the correlation selection process, we propose a criterion that utilizes the trace of the empirical covariance matrix. Furthermore, use of the unstructured working correlation can potentially improve estimation precision and therefore should be considered when data arise from a balanced longitudinal study. However, most previous studies have not allowed the unstructured working correlation to be selected as it estimates more nuisance correlation parameters than other structures such as AR-1 or exchangeable. Therefore, we propose appropriate penalties for the selection criteria that can be imposed upon the unstructured working correlation. Via simulation in multiple scenarios and in application to a longitudinal study, we show that the trace of the empirical covariance matrix works very well relative to existing criteria. We further show that allowing criteria to select the unstructured working correlation when utilizing the penalties can substantially improve parameter estimation.
Uncertainty in Population Estimates for Endangered Animals and Improving the Recovery Process.
Haines, Aaron M; Zak, Matthew; Hammond, Katie; Scott, J Michael; Goble, Dale D; Rachlow, Janet L
2013-08-13
United States recovery plans contain biological information for a species listed under the Endangered Species Act and specify recovery criteria to provide basis for species recovery. The objective of our study was to evaluate whether recovery plans provide uncertainty (e.g., variance) with estimates of population size. We reviewed all finalized recovery plans for listed terrestrial vertebrate species to record the following data: (1) if a current population size was given, (2) if a measure of uncertainty or variance was associated with current estimates of population size and (3) if population size was stipulated for recovery. We found that 59% of completed recovery plans specified a current population size, 14.5% specified a variance for the current population size estimate and 43% specified population size as a recovery criterion. More recent recovery plans reported more estimates of current population size, uncertainty and population size as a recovery criterion. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty compared to reptiles and amphibians. We suggest the use of calculating minimum detectable differences to improve confidence when delisting endangered animals and we identified incentives for individuals to get involved in recovery planning to improve access to quantitative data.
Makeyev, Oleksandr; Besio, Walter G.
2016-01-01
Noninvasive concentric ring electrodes are a promising alternative to conventional disc electrodes. Currently, the superiority of tripolar concentric ring electrodes over disc electrodes, in particular, in accuracy of Laplacian estimation, has been demonstrated in a range of applications. In our recent work, we have shown that accuracy of Laplacian estimation can be improved with multipolar concentric ring electrodes using a general approach to estimation of the Laplacian for an (n + 1)-polar electrode with n rings using the (4n + 1)-point method for n ≥ 2. This paper takes the next step toward further improving the Laplacian estimate by proposing novel variable inter-ring distances concentric ring electrodes. Derived using a modified (4n + 1)-point method, linearly increasing and decreasing inter-ring distances tripolar (n = 2) and quadripolar (n = 3) electrode configurations are compared to their constant inter-ring distances counterparts. Finite element method modeling and analytic results are consistent and suggest that increasing inter-ring distances electrode configurations may decrease the truncation error resulting in more accurate Laplacian estimates compared to respective constant inter-ring distances configurations. For currently used tripolar electrode configuration, the truncation error may be decreased more than two-fold, while for the quadripolar configuration more than a six-fold decrease is expected. PMID:27294933
NASA Astrophysics Data System (ADS)
Schmidt, Samuel J.; Thorman, Paul
2013-05-01
Wide, deep photometric surveys require robust photometric redshift estimates (photo-zs) for studies of large-scale structure. These estimates depend critically on accurate photometry. We describe the improvements to the photometric calibration and the photo-z estimates in the Deep Lens Survey (DLS) from correcting three of the inputs to the photo-z calculation: the system response as a function of wavelength, the spectral energy distribution templates and template prior probabilities as a function of magnitude. We model the system response with a physical model of the MOSAIC camera's CCD, which corrects a 0.1 mag discrepancy in the colours of type M2 and later stars relative to the Sloan Digital Sky Survey z photometry. We provide our estimated z response function for the use of other surveys that used MOSAIC before its recent detector upgrade. The improved throughput curve, template set and Bayesian prior lead to general improvement in the predicted photometric redshifts, including a 20 per cent reduction in photo-z scatter at low redshift and a reduction of the bias by a factor of more than 2 at high redshift. This paper serves as both a photo-z data release description for DLS and a guide for testing the quality of photometry and resulting photo-zs generally.
Space-based NOx emission estimates over remote regions improved in DECSO
NASA Astrophysics Data System (ADS)
Ding, Jieying; van der A, Ronald Johannes; Mijling, Bas; Felicitas Levelt, Pieternel
2017-03-01
We improve the emission estimate algorithm DECSO (Daily Emission estimates Constrained by Satellite Observations) to better detect NOx emissions over remote areas. The new version is referred to as DECSO v5. The error covariance of the sensitivity of NO2 column observations to gridded NOx emissions has been better characterized. This reduces the background noise of emission estimates by a factor of 10. An emission update constraint has been added to avoid unrealistic day-to-day fluctuations of emissions. We estimate total NOx emissions, which include biogenic emissions that often drive the seasonal cycle of the NOx emissions. We demonstrate the improvements implemented in DECSO v5 for the domain of East Asia in the year 2012 and 2013. The emissions derived by DECSO v5 are in good agreement with other inventories like MIX. In addition, the improved algorithm is able to better capture the seasonality of NOx emissions and for the first time it reveals ship tracks near the Chinese coasts that are otherwise hidden by the outflow of NO2 from the Chinese mainland. The precision of monthly emissions derived by DECSO v5 for each grid cell is about 20 %.
Estimation of local fleet characteristics data for improved emission inventory development
Heiken, J.; Pollack, A.; Austin, B.
1996-12-31
Considerable effort in recent years has been focused on the improvement of on-road mobile source emission factors with much less attention paid to the refinement of activity and fleet characteristics estimates. Current emissions modeling practices commonly use emission factor model defaults or statewide averages for fleet and activity data. As part of the US EPA`s Emission Inventory Improvement Program (EIIP), ENVIRON developed methodologies to derive locality-specific fleet characteristics data from existing data sources in order to improve local emission inventory estimates. Data sources examined included remote sensing studies and inspection and maintenance (I/M) program data. In this paper, we focus on two specific examples: (1) the calculation of mileage accumulation rates from Arizona I/M program data, and (2) the calculation of registration distribution from a Sacramento remote sensing database. In both examples, differences exist between the calculated distributions and those currently used for air quality modeling, resulting in significant impacts on the estimated mobile source emissions inventory. For example, use of the automobile registration distribution data derived from the Sacramento Pilot I/M Program remote sensing database results in an increase in estimated automobile TOG, CO and NO{sub x} of 15, 24 and 17 percent, respectively, when used in place of the default registration distribution in the current California Air Resources Board MVEI7G emissions model.
Improving PAGER's real-time earthquake casualty and loss estimation toolkit: a challenge
Jaiswal, K.S.; Wald, D.J.
2012-01-01
We describe the on-going developments of PAGER’s loss estimation models, and discuss value-added web content that can be generated related to exposure, damage and loss outputs for a variety of PAGER users. These developments include identifying vulnerable building types in any given area, estimating earthquake-induced damage and loss statistics by building type, and developing visualization aids that help locate areas of concern for improving post-earthquake response efforts. While detailed exposure and damage information is highly useful and desirable, significant improvements are still necessary in order to improve underlying building stock and vulnerability data at a global scale. Existing efforts with the GEM’s GED4GEM and GVC consortia will help achieve some of these objectives. This will benefit PAGER especially in regions where PAGER’s empirical model is less-well constrained; there, the semi-empirical and analytical models will provide robust estimates of damage and losses. Finally, we outline some of the challenges associated with rapid casualty and loss estimation that we experienced while responding to recent large earthquakes worldwide.
Performance of velocity vector estimation using an improved dynamic beamforming setup
NASA Astrophysics Data System (ADS)
Munk, Peter; Jensen, Joergen A.
2001-05-01
Estimation of velocity vectors using transverse spatial modulation has previously been presented. Initially, the velocity estimation was improved using an approximated dynamic beamformer setup instead of a static combined with a new velocity estimation scheme. A new beamformer setup for dynamic control of the acoustic field, based on the Pulsed Plane Wave Decomposition (PPWD), is presented. The PPWD gives an unambiguous relation between a given acoustic field and the time functions needed on an array transducer for transmission. Applying this method for the receive beamformation results in a setup of the beamformer with different filters for each channel for each estimation depth. The method of the PPWD is illustrated by analytical expressions of the decomposed acoustic field and these results are used for simulation. Results of velocity estimates using the new setup are given on the basis of simulated and experimental data. The simulation setup is an attempt to approximate the situation present when performing a scanning of the carotid artery with a linear array. Measurement of the flow perpendicular to the emission direction is possible using the approach of transverse spatial modulation. This is most often the case in a scanning of the carotid artery, where the situation is handled by an angled Doppler setup in the present ultrasound scanners. The modulation period of 2 mm is controlled for a range of 20-40 mm which covers the typical range of the carotid artery. A 6 MHz array on a 128-channel system is simulated. The flow setup in the simulation is based on a vessel with a parabolic flow profile for a 60 and 90-degree flow angle. The experimental results are based on the backscattered signal from a sponge mounted in a stepping device. The bias and std. Dev. Of the velocity estimate are calculated for four different flow angles (50,60,75 and 90 degrees). The velocity vector is calculated using the improved 2D estimation approach at a range of depths.
Improved emission estimation techniques for computerized networks: a user's manual. Final report
Herlihy, E.T.; Byun, J.H.; Horodniceanu, M.; Falcocchio, J.C.
1982-09-01
This report summarizes the software revisions and program set-ups for implementing an improved method to estimate transient operation VMT on UTPS highway networks. Utilizing existing framework of UTPS, this report provides a description of the software that permits the analyst to more accurately estimate and test the sensitivity of a revised application of emission rates to specific components of the typical vehicle trip. These include subroutine documentation and deck set-ups for UROAD, HNET, UMATRIX AND MOBILE1 computer runs required in the procedure. This application, as set up, provides for transient mode VMT calculations for all VMT that is within 505 seconds of the origin zone or trip start.
Improving the estimation of flavonoid intake for study of health outcomes
Dwyer, Johanna T.; Jacques, Paul F.; McCullough, Marjorie L.
2015-01-01
Imprecision in estimating intakes of non-nutrient bioactive compounds such as flavonoids is a challenge in epidemiologic studies of health outcomes. The sources of this imprecision, using flavonoids as an example, include the variability of bioactive compounds in foods due to differences in growing conditions and processing, the challenges in laboratory quantification of flavonoids in foods, the incompleteness of flavonoid food composition tables, and the lack of adequate dietary assessment instruments. Steps to improve databases of bioactive compounds and to increase the accuracy and precision of the estimation of bioactive compound intakes in studies of health benefits and outcomes are suggested. PMID:26084477
Does Ocean Color Data Assimilation Improve Estimates of Global Ocean Inorganic Carbon?
NASA Technical Reports Server (NTRS)
Gregg, Watson
2012-01-01
Ocean color data assimilation has been shown to dramatically improve chlorophyll abundances and distributions globally and regionally in the oceans. Chlorophyll is a proxy for phytoplankton biomass (which is explicitly defined in a model), and is related to the inorganic carbon cycle through the interactions of the organic carbon (particulate and dissolved) and through primary production where inorganic carbon is directly taken out of the system. Does ocean color data assimilation, whose effects on estimates of chlorophyll are demonstrable, trickle through the simulated ocean carbon system to produce improved estimates of inorganic carbon? Our emphasis here is dissolved inorganic carbon, pC02, and the air-sea flux. We use a sequential data assimilation method that assimilates chlorophyll directly and indirectly changes nutrient concentrations in a multi-variate approach. The results are decidedly mixed. Dissolved organic carbon estimates from the assimilation model are not meaningfully different from free-run, or unassimilated results, and comparisons with in situ data are similar. pC02 estimates are generally worse after data assimilation, with global estimates diverging 6.4% from in situ data, while free-run estimates are only 4.7% higher. Basin correlations are, however, slightly improved: r increase from 0.78 to 0.79, and slope closer to unity at 0.94 compared to 0.86. In contrast, air-sea flux of C02 is noticeably improved after data assimilation. Global differences decline from -0.635 mol/m2/y (stronger model sink from the atmosphere) to -0.202 mol/m2/y. Basin correlations are slightly improved from r=O.77 to r=0.78, with slope closer to unity (from 0.93 to 0.99). The Equatorial Atlantic appears as a slight sink in the free-run, but is correctly represented as a moderate source in the assimilation model. However, the assimilation model shows the Antarctic to be a source, rather than a modest sink and the North Indian basin is represented incorrectly as a sink
Estimating the Effect of School Water, Sanitation, and Hygiene Improvements on Pupil Health Outcomes
Garn, Joshua V.; Brumback, Babette A.; Drews-Botsch, Carolyn D.; Lash, Timothy L.; Kramer, Michael R.
2016-01-01
Background: We conducted a cluster-randomized water, sanitation, and hygiene trial in 185 schools in Nyanza province, Kenya. The trial, however, had imperfect school-level adherence at many schools. The primary goal of this study was to estimate the causal effects of school-level adherence to interventions on pupil diarrhea and soil-transmitted helminth infection. Methods: Schools were divided into water availability groups, which were then randomized separately into either water, sanitation, and hygiene intervention arms or a control arm. School-level adherence to the intervention was defined by the number of intervention components—water, latrines, soap—that had been adequately implemented. The outcomes of interest were pupil diarrhea and soil-transmitted helminth infection. We used a weighted generalized structural nested model to calculate prevalence ratio. Results: In the water-scarce group, there was evidence of a reduced prevalence of diarrhea among pupils attending schools that adhered to two or to three intervention components (prevalence ratio = 0.28, 95% confidence interval: 0.10, 0.75), compared with what the prevalence would have been had the same schools instead adhered to zero components or one. In the water-available group, there was no evidence of reduced diarrhea with better adherence. For the soil-transmitted helminth infection and intensity outcomes, we often observed point estimates in the preventive direction with increasing intervention adherence, but primarily among girls, and the confidence intervals were often very wide. Conclusions: Our instrumental variable point estimates sometimes suggested protective effects with increased water, sanitation, and hygiene intervention adherence, although many of the estimates were imprecise. PMID:27276028
Development of a mixed pixel filter for improved dimension estimation using AMCW laser scanner
NASA Astrophysics Data System (ADS)
Wang, Qian; Sohn, Hoon; Cheng, Jack C. P.
2016-09-01
Accurate dimension estimation is desired in many fields, but the traditional dimension estimation methods are time-consuming and labor-intensive. In the recent decades, 3D laser scanners have become popular for dimension estimation due to their high measurement speed and accuracy. Nonetheless, scan data obtained by amplitude-modulated continuous-wave (AMCW) laser scanners suffer from erroneous data called mixed pixels, which can influence the accuracy of dimension estimation. This study develops a mixed pixel filter for improved dimension estimation using AMCW laser scanners. The distance measurement of mixed pixels is firstly formulated based on the working principle of laser scanners. Then, a mixed pixel filter that can minimize the classification errors between valid points and mixed pixels is developed. Validation experiments were conducted to verify the formulation of the distance measurement of mixed pixels and to examine the performance of the proposed mixed pixel filter. Experimental results show that, for a specimen with dimensions of 840 mm × 300 mm, the overall errors of the dimensions estimated after applying the proposed filter are 1.9 mm and 1.0 mm for two different scanning resolutions, respectively. These errors are much smaller than the errors (4.8 mm and 3.5 mm) obtained by the scanner's built-in filter.
Application of copulas to improve covariance estimation for partial least squares.
D'Angelo, Gina M; Weissfeld, Lisa A
2013-02-20
Dimension reduction techniques, such as partial least squares, are useful for computing summary measures and examining relationships in complex settings. Partial least squares requires an estimate of the covariance matrix as a first step in the analysis, making this estimate critical to the results. In addition, the covariance matrix also forms the basis for other techniques in multivariate analysis, such as principal component analysis and independent component analysis. This paper has been motivated by an example from an imaging study in Alzheimer's disease where there is complete separation between Alzheimer's and control subjects for one of the imaging modalities. This separation occurs in one block of variables and does not occur with the second block of variables resulting in inaccurate estimates of the covariance. We propose the use of a copula to obtain estimates of the covariance in this setting, where one set of variables comes from a mixture distribution. Simulation studies show that the proposed estimator is an improvement over the standard estimators of covariance. We illustrate the methods from the motivating example from a study in the area of Alzheimer's disease.
Improved dichotomous search frequency offset estimator for burst-mode continuous phase modulation
NASA Astrophysics Data System (ADS)
Zhai, Wen-Chao; Li, Zan; Si, Jiang-Bo; Bai, Jun
2015-11-01
A data-aided technique for carrier frequency offset estimation with continuous phase modulation (CPM) in burst-mode transmission is presented. The proposed technique first exploits a special pilot sequence, or training sequence, to form a sinusoidal waveform. Then, an improved dichotomous search frequency offset estimator is introduced to determine the frequency offset using the sinusoid. Theoretical analysis and simulation results indicate that our estimator is noteworthy in the following aspects. First, the estimator can operate independently of timing recovery. Second, it has relatively low outlier, i.e., the minimum signal-to-noise ratio (SNR) required to guarantee estimation accuracy. Finally, the most important property is that our estimator is complexity-reduced compared to the existing dichotomous search methods: it eliminates the need for fast Fourier transform (FFT) and modulation removal, and exhibits faster convergence rate without accuracy degradation. Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Programs Foundation of the Ministry of Education, China (Grant No. 20110203110011), and the Programme of Introducing Talents of Discipline to Universities, China (Grant No. B08038).
Improved FRFT-based method for estimating the physical parameters from Newton's rings
NASA Astrophysics Data System (ADS)
Wu, Jin-Min; Lu, Ming-Feng; Tao, Ran; Zhang, Feng; Li, Yang
2017-04-01
Newton's rings are often encountered in interferometry, and in analyzing them, we can estimate the physical parameters, such as curvature radius and the rings' center. The fractional Fourier transform (FRFT) is capable of estimating these physical parameters from the rings despite noise and obstacles, but there is still a small deviation between the estimated coordinates of the rings' center and the actual values. The least-squares fitting method is popularly used for its accuracy but it is easily affected by the initial values. Nevertheless, with the estimated results from the FRFT, it is easy to meet the requirements of initial values. In this paper, the proposed method combines the advantages of the fractional Fourier transform (FRFT) with the least-squares fitting method in analyzing Newton's rings fringe patterns. Its performance is assessed by analyzing simulated and actual Newton's rings images. The experimental results show that the proposed method is capable of estimating the parameters in the presence of noise and obstacles. Under the same conditions, the estimation results are better than those obtained with the original FRFT-based method, especially for the rings' center. Some applications are shown to illustrate that the improved FRFT-based method is an important technique for interferometric measurements.
Improved rapid magnitude estimation for a community-based, low-cost MEMS accelerometer network
Chung, Angela I.; Cochran, Elizabeth S.; Kaiser, Anna E.; Christensen, Carl M.; Yildirim, Battalgazi; Lawrence, Jesse F.
2015-01-01
Immediately following the Mw 7.2 Darfield, New Zealand, earthquake, over 180 Quake‐Catcher Network (QCN) low‐cost micro‐electro‐mechanical systems accelerometers were deployed in the Canterbury region. Using data recorded by this dense network from 2010 to 2013, we significantly improved the QCN rapid magnitude estimation relationship. The previous scaling relationship (Lawrence et al., 2014) did not accurately estimate the magnitudes of nearby (<35 km) events. The new scaling relationship estimates earthquake magnitudes within 1 magnitude unit of the GNS Science GeoNet earthquake catalog magnitudes for 99% of the events tested, within 0.5 magnitude units for 90% of the events, and within 0.25 magnitude units for 57% of the events. These magnitudes are reliably estimated within 3 s of the initial trigger recorded on at least seven stations. In this report, we present the methods used to calculate a new scaling relationship and demonstrate the accuracy of the revised magnitude estimates using a program that is able to retrospectively estimate event magnitudes using archived data.
Ji, Tieming; Liu, Peng; Nettleton, Dan
2012-01-01
Statistical inference for microarray experiments usually involves the estimation of error variance for each gene. Because the sample size available for each gene is often low, the usual unbiased estimator of the error variance can be unreliable. Shrinkage methods, including empirical Bayes approaches that borrow information across genes to produce more stable estimates, have been developed in recent years. Because the same microarray platform is often used for at least several experiments to study similar biological systems, there is an opportunity to improve variance estimation further by borrowing information not only across genes but also across experiments. We propose a lognormal model for error variances that involves random gene effects and random experiment effects. Based on the model, we develop an empirical Bayes estimator of the error variance for each combination of gene and experiment and call this estimator BAGE because information is Borrowed Across Genes and Experiments. A permutation strategy is used to make inference about the differential expression status of each gene. Simulation studies with data generated from different probability models and real microarray data show that our method outperforms existing approaches.
Smith, Edward M; Littrell, Jack; Olivier, Michael
2007-12-01
High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.
Semi-Supervised Data Summarization: Using Spectral Libraries to Improve Hyperspectral Clustering
NASA Technical Reports Server (NTRS)
Wagstaff, K. L.; Shu, H. P.; Mazzoni, D.; Castano, R.
2005-01-01
Hyperspectral imagers produce very large images, with each pixel recorded at hundreds or thousands of different wavelengths. The ability to automatically generate summaries of these data sets enables several important applications, such as quickly browsing through a large image repository or determining the best use of a limited bandwidth link (e.g., determining which images are most critical for full transmission). Clustering algorithms can be used to generate these summaries, but traditional clustering methods make decisions based only on the information contained in the data set. In contrast, we present a new method that additionally leverages existing spectral libraries to identify materials that are likely to be present in the image target area. We find that this approach simultaneously reduces runtime and produces summaries that are more relevant to science goals.
Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability.
Uziela, Karolis; Honkela, Antti
2015-01-01
Rapidly growing public gene expression databases contain a wealth of data for building an unprecedentedly detailed picture of human biology and disease. This data comes from many diverse measurement platforms that make integrating it all difficult. Although RNA-sequencing (RNA-seq) is attracting the most attention, at present, the rate of new microarray studies submitted to public databases far exceeds the rate of new RNA-seq studies. There is clearly a need for methods that make it easier to combine data from different technologies. In this paper, we propose a new method for processing RNA-seq data that yields gene expression estimates that are much more similar to corresponding estimates from microarray data, hence greatly improving cross-platform comparability. The method we call PREBS is based on estimating the expression from RNA-seq reads overlapping the microarray probe regions, and processing these estimates with standard microarray summarisation algorithms. Using paired microarray and RNA-seq samples from TCGA LAML data set we show that PREBS expression estimates derived from RNA-seq are more similar to microarray-based expression estimates than those from other RNA-seq processing methods. In an experiment to retrieve paired microarray samples from a database using an RNA-seq query sample, gene signatures defined based on PREBS expression estimates were found to be much more accurate than those from other methods. PREBS also allows new ways of using RNA-seq data, such as expression estimation for microarray probe sets. An implementation of the proposed method is available in the Bioconductor package "prebs."
NASA Astrophysics Data System (ADS)
Bašic, Ivan; Lučié, Bono; Nikolić, Sonja; Papeš-Šokčević, Lidija; Nadramija, Damir
2009-08-01
For selected data set published by Russom et al. (Environ. Toxicol. Chem. 16, 948-967 (1997)) containing 704 organic molecules with measured acute aquatic toxicity data (96-h LC50 tests) we calculated data set of more than 1400 molecular descriptors by the Dragon 5.0 program. After we excluded descriptors that have almost constant values, and those having very low correlation with the logarithm of LC50 values on the training set, 623 descriptors remained and were used in the modeling process. Data set of molecules was randomly partitioned into the training and test set containing 560 and 144 molecules, respectively. We developed and compared two kinds of ensemble of both linear and nonlinear multi-regression models (1) normal ensembles and (2) ensembles obtained by the clustering of molecules according to their similarity (clustered ensembles). Clustering of molecules was performed by calculating their Euclidian distances in normalized descriptor space. In this method, the final model was developed only on those molecules from the training set that are close (measured using Euclidian distance in normalized descriptor space) to the selected molecule from the test set. Although results obtained by normal ensembles are very good (e.g. nonlinear ensemble of 8-descriptor models: r2 = 0.82, s = 0.54 (training set), stest = 0.80), significant improvement is obtained by taking into account clustering of molecules in development of ensembles of linear models (e.g. 200 10-descriptor models in ensemble: r2 = 0.87, strain = 0.45 (training set), stest = 0.76; or for 200 simpler models having 7-descriptor models in ensemble r2 = 0.83, Strain = 0.53 (training set), stest = 0.77). These results clearly indicate that the use of information about similarity between molecules can improve structure-toxicity models, and we also expect that this could be valid generally.
Improving Land Cover Product-Based Estimates of the Extent of Fragmented Cover Types
NASA Technical Reports Server (NTRS)
Hlavka, Christine A.; Dungan, Jennifer
2002-01-01
The effects of changing land use/land cover on regional and global climate ecosystems depends on accurate estimates of the extent of critical land cover types such as Arctic wetlands and fire scars in boreal forests. To address this information requirement, land cover products at coarse spatial resolution such as Advanced Very High Resolution Radiometer (AVHRR) -based maps and the MODIS Land Cover Product are being produced. The accuracy of the extent of highly fragmented cover types such as fire scars and ponds is in doubt because much (the numerous scars and ponds smaller than the pixel size) is missed. A promising method for improving areal estimates involves modeling the observed distribution of the fragment sizes as a type of truncated distribution, then estimating the sum of unobserved sizes in the lower, truncated tail and adding it to the sum of observed fragment sizes. The method has been tested with both simulated and actual cover products.
NASA Technical Reports Server (NTRS)
Allord, G. J. (Principal Investigator); Scarpace, F. L.
1981-01-01
Estimates of low flow and flood frequency in several southwestern Wisconsin basins were improved by determining land cover from LANDSAT imagery. With the use of estimates of land cover in multiple-regression techniques, the standard error of estimate (SE) for the least annual 7-day low flow for 2- and 10-year recurrence intervals of ungaged sites were lowered by 9% each. The SE of flood frequency in the 'Driftless Area' of Wisconsin for 10-, 50-, and 100-year recurrence intervals were lowered by 14%. Four of nine basin characteristics determined from satellite imagery were significant variables in the multiple-regression techniques, whereas only 1 of the 12 characteristics determined from topographic maps was significant. The percentages of land cover categories in each basin were determined by merging basin boundaries, digitized from quadrangles, with a classified LANDSAT scene. Both the basin boundary X-Y polygon coordinates and the satellite coordinates were converted to latitude-longitude for merging compatibility.
Improving estimates of oil pollution to the sea from land-based sources.
Saito, Laurel; Rosen, Michael R; Roesner, Larry; Howard, Nalin
2010-07-01
This paper presents improvements to calculation methods used in the National Research Council's (NRC) Oil in the Sea reports from 2003 and 1985 to estimate land-based contributions of petroleum hydrocarbons to the sea from North America. Using procedures similar to the 2003 report, but with more robust methods for handling non-detections, estimated land-based contributions for 1977 and 2000 were over 50% lower than the best 1996 estimate in the NRC report. The largest loads were from the northeastern United States and the Gulf of Mexico region for both the 2003 report and updated calculations. Calculations involved many sources of uncertainty, including lack of available data, variable methods of measuring and reporting data, and variable methods of reporting values below detection limits. This updated analysis of land-based loads of petroleum hydrocarbons to the sea highlights the continued need for more monitoring and research on inputs, fates and effects of these sources.
MacKinnon, Robert J.; Kuhlman, Kristopher L
2016-05-01
We present a method of control variates for calculating improved estimates for mean performance quantities of interest, E(PQI) , computed from Monte Carlo probabilistic simulations. An example of a PQI is the concentration of a contaminant at a particular location in a problem domain computed from simulations of transport in porous media. To simplify the presentation, the method is described in the setting of a one- dimensional elliptical model problem involving a single uncertain parameter represented by a probability distribution. The approach can be easily implemented for more complex problems involving multiple uncertain parameters and in particular for application to probabilistic performance assessment of deep geologic nuclear waste repository systems. Numerical results indicate the method can produce estimates of E(PQI)having superior accuracy on coarser meshes and reduce the required number of simulations needed to achieve an acceptable estimate.
Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Zhang, He
2016-11-20
Angular velocity information is a requisite for a spacecraft guidance, navigation, and control system. In this paper, an approach for angular velocity estimation based merely on star vector measurement with an improved current statistical model Kalman filter is proposed. High-precision angular velocity estimation can be achieved under dynamic conditions. The amount of calculation is also reduced compared to a Kalman filter. Different trajectories are simulated to test this approach, and experiments with real starry sky observation are implemented for further confirmation. The estimation accuracy is proved to be better than 10^{-4} rad/s under various conditions. Both the simulation and the experiment demonstrate that the described approach is effective and shows an excellent performance under both static and dynamic conditions.
The importance of crown dimensions to improve tropical tree biomass estimates.
Goodman, Rosa C; Phillips, Oliver L; Baker, Timothy R
2014-06-01
Tropical forests play a vital role in the global carbon cycle, but the amount of carbon they contain and its spatial distribution remain uncertain. Recent studies suggest that once tree height is accounted for in biomass calculations, in addition to diameter and wood density, carbon stock estimates are reduced in many areas. However, it is possible that larger crown sizes might offset the reduction in biomass estimates in some forests where tree heights are lower because even comparatively short trees develop large, well-lit crowns in or above the forest canopy. While current allometric models and theory focus on diameter, wood density, and height, the influence of crown size and structure has not been well studied. To test the extent to which accounting for crown parameters can improve biomass estimates, we harvested and weighed 51 trees (11-169 cm diameter) in southwestern Amazonia where no direct biomass measurements have been made. The trees in our study had nearly half of total aboveground biomass in the branches (44% +/- 2% [mean +/- SE]), demonstrating the importance of accounting for tree crowns. Consistent with our predictions, key pantropical equations that include height, but do not account for crown dimensions, underestimated the sum total biomass of all 51 trees by 11% to 14%, primarily due to substantial underestimates of many of the largest trees. In our models, including crown radius greatly improves performance and reduces error, especially for the largest trees. In addition, over the full data set, crown radius explained more variation in aboveground biomass (10.5%) than height (6.0%). Crown form is also important: Trees with a monopodial architectural type are estimated to have 21-44% less mass than trees with other growth patterns. Our analysis suggests that accounting for crown allometry would substantially improve the accuracy of tropical estimates of tree biomass and its distribution in primary and degraded forests.
NASA Astrophysics Data System (ADS)
Padgett, J. S.; Engelhart, S. E.; Hemphill-Haley, E.; Kelsey, H. M.; Witter, R. C.
2015-12-01
Geological estimates of subsidence from past earthquakes help to constrain Cascadia subduction zone (CSZ) earthquake rupture models. To improve subsidence estimates for past earthquakes along the southern CSZ, we apply transfer function analysis on microfossils from 3 intertidal marshes in northern Humboldt Bay, California, ~60 km north of the Mendocino Triple Junction. The transfer function method uses elevation-dependent intertidal foraminiferal and diatom assemblages to reconstruct relative sea-level (RSL) change indicated by shifts in microfossil assemblages. We interpret stratigraphic evidence associated with sudden shifts in microfossils to reflect sudden RSL rise due to subsidence during past CSZ earthquakes. Laterally extensive (>5 km) and sharp mud-over-peat contacts beneath marshes at Jacoby Creek, Mad River Slough, and McDaniel Slough demonstrate widespread earthquake subsidence in northern Humboldt Bay. C-14 ages of plant macrofossils taken from above and below three contacts that correlate across all three sites, provide estimates of the times of subsidence at ~250 yr BP, ~1300 yr BP and ~1700 yr BP. Two further contacts observed at only two sites provide evidence for subsidence during possible CSZ earthquakes at ~900 yr BP and ~1100 yr BP. Our study contributes 20 AMS radiocarbon ages, of identifiable plant macrofossils, that improve estimates of the timing of past earthquakes along the southern CSZ. We anticipate that our results will provide more accurate and precise reconstructions of RSL change induced by southern CSZ earthquakes. Prior to our work, studies in northern Humboldt Bay provided subsidence estimates with vertical uncertainties >±0.5 m; too imprecise to adequately constrain earthquake rupture models. Our method, applied recently in coastal Oregon, has shown that subsidence during past CSZ earthquakes can be reconstructed with a precision of ±0.3m and substantially improves constraints on rupture models used for seismic hazard
NASA Astrophysics Data System (ADS)
Wang, Chao; Ji, Ming; Zhang, Ying; Jiang, Wentao; Lu, Xiaoyan; Wang, Jiaoying; Yang, Heng
2016-01-01
The electronic image stabilization technology based on improved optical-flow motion vector estimation technique can effectively improve the non normal shift, such as jitter, rotation and so on. Firstly, the ORB features are extracted from the image, a set of regions are built on these features; Secondly, the optical-flow vector is computed in the feature regions, in order to reduce the computational complexity, the multi resolution strategy of Pyramid is used to calculate the motion vector of the frame; Finally, qualitative and quantitative analysis of the effect of the algorithm is carried out. The results show that the proposed algorithm has better stability compared with image stabilization based on the traditional optical-flow motion vector estimation method.
NASA Astrophysics Data System (ADS)
Brena, A.; Kendall, A. D.; Hyndman, D. W.
2013-12-01
Large-scale agroecosystems are major providers of agricultural commodities and an important component of the world's food supply. In agroecosystems that depend mainly in groundwater, it is well known that their long-term sustainability can be at risk because of water management strategies and climatic trends. The water balance of groundwater-dependent agroecosystems such as the High Plains aquifer (HPA) are often dominated by pumping and irrigation, which enhance hydrological processes such as evapotranspiration, return flow and recharge in cropland areas. This work provides and validates new quantitative groundwater estimation methods for the HPA that combine satellite-based estimates of terrestrial water storage (GRACE), hydrological data assimilation products (NLDAS-2) and in situ measurements of groundwater levels and irrigation rates. The combined data can be used to elucidate the controls of irrigation on the water balance components of agroecosystems, such as crop evapotranspiration, soil moisture deficit and recharge. Our work covers a decade of continuous observations and model estimates from 2003 to 2013, which includes a significant drought since 2011. This study aims to: (1) test the sensitivity of groundwater storage to soil moisture and irrigation, (2) improve estimates of irrigation and soil moisture deficits (3) infer mean values of groundwater recharge across the HPA. The results show (1) significant improvements in GRACE-derived aquifer storage changes using methods that incorporate irrigation and soil moisture deficit data, (2) an acceptable correlation between the observed and estimated aquifer storage time series for the analyzed period, and (3) empirically-estimated annual rates of groundwater recharge that are consistent with previous geochemical and modeling studies. We suggest testing these correction methods in other large-scale agroecosystems with intensive groundwater pumping and irrigation rates.
Xu, Da; Ryan, Kathy L; Rickards, Caroline A; Zhang, Guanqun; Convertino, Victor A; Mukkamala, Ramakrishna
2011-10-01
We investigated the system identification approach for potentially improved estimation of pulse transit time (PTT), a popular arterial stiffness marker. In this approach, proximal and distal arterial waveforms are measured and respectively regarded as the input and output of a system. Next, the system impulse response is identified from all samples of the measured input and output. Finally, the time delay of the impulse response is detected as the PTT estimate. Unlike conventional foot-to-foot detection techniques, this approach is designed to provide an artifact robust estimate of the true PTT in the absence of wave reflection. The approach is also applicable to arbitrary types of arterial waveforms. We specifically applied a parametric system identification technique to noninvasive impedance cardiography (ICG) and peripheral arterial blood pressure waveforms from 15 humans subjected to lower-body negative pressure. We assessed the technique through the correlation coefficient (r) between its 1/PTT estimates and measured diastolic pressure (DP) per subject and the root mean squared error (RMSE) of the DP predicted from these estimates and measured DP. The technique achieved average r and RMSE values of 0.81 ± 0.16 and 4.3 ± 1.3 mmHg. For comparison, the corresponding values were 0.59 ± 0.37 (P < 0.05) and 5.9 ± 2.5 (P < 0.01) mmHg for the conventional technique applied to the same waveforms and 0.28 ± 0.40 (P < 0.001) and 7.2 ± 1.8 (P < 0.001) mmHg for the conventional technique with the ECG waveform substituted for the ICG waveform. These results demonstrate, perhaps for the first time, that the system identification approach can indeed improve PTT estimation.
NASA Astrophysics Data System (ADS)
Madani, Nima; Kimball, John S.; Affleck, David L. R.; Kattge, Jens; Graham, Jon; Bodegom, Peter M.; Reich, Peter B.; Running, Steven W.
2014-09-01
A common assumption of remote sensing-based light use efficiency (LUE) models for estimating vegetation gross primary productivity (GPP) is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed constant biome maximum light use efficiency parameter (LUEmax) defines the maximum photosynthetic carbon conversion rate under these conditions and is a large source of model uncertainty. Here we used tower eddy covariance measurement-based carbon (CO2) fluxes for spatial estimation of optimal LUE (LUEopt) across North America. LUEopt was estimated at 62 Flux Network sites using tower daily carbon fluxes and meteorology, and satellite observed fractional photosynthetically active radiation from the Moderate Resolution Imaging Spectroradiometer. A geostatistical model was fitted to 45 flux tower-derived LUEopt data points using independent geospatial environmental variables, including global plant traits, soil moisture, terrain aspect, land cover type, and percent tree cover, and validated at 17 independent tower sites. Estimated LUEopt shows large spatial variability within and among different land cover classes indicated from the sparse tower network. Leaf nitrogen content and soil moisture regime are major factors explaining LUEopt patterns. GPP derived from estimated LUEopt shows significant correlation improvement against tower GPP records (R2 = 76.9%; mean root-mean-square error (RMSE) = 257 g C m-2 yr-1), relative to alternative GPP estimates derived using biome-specific LUEmax constants (R2 = 34.0%; RMSE = 439 g C m-2 yr-1). GPP determined from the LUEopt map also explains a 49.4% greater proportion of tower GPP variability at the independent validation sites and shows promise for improving understanding of LUE patterns and environmental controls and enhancing regional GPP monitoring from satellite remote sensing.
NASA Astrophysics Data System (ADS)
Shafian, S.; Maas, S. J.; Rajan, N.
2014-12-01
Water resources and agricultural applications require knowledge of crop water use (CWU) over a range of spatial and temporal scales. Due to the spatial density of meteorological stations, the resolution of CWU estimates based on these data is fairly coarse and not particularly suitable or reliable for water resources planning, irrigation scheduling and decision making. Various methods have been developed for quantifying CWU of agricultural crops. In this study, an improved version of the spectral crop coefficient which includes the effects of stomatal closure is applied. Raw digital count (DC) data in the red, near-infrared, and thermal infrared (TIR) spectral bands of Landsat-7 and Landsat-8 imaging sensors are used to construct the TIR-ground cover (GC) pixel data distribution and estimate the effects of stomatal closure. CWU is then estimated by combining results of the spectral crop coefficient approach and the stomatal closer effect. To test this approach, evapotranspiration was measured in 5 agricultural fields in the semi-arid Texas High Plains during the 2013 and 2014 growing seasons and compared to corresponding estimated values of CWU determined using this approach. The results showed that the estimated CWU from this approach was strongly correlated (R2 = 0.79) with observed evapotranspiration. In addition, the results showed that considering the stomatal closer effect in the proposed approach can improve the accuracy of the spectral crop coefficient method. These results suggest that the proposed approach is suitable for operational estimation of evapotranspiration and irrigation scheduling where irrigation is used to replace the daily CWU of a crop.
Experimental verification of an interpolation algorithm for improved estimates of animal position.
Schell, Chad; Jaffe, Jules S
2004-07-01
This article presents experimental verification of an interpolation algorithm that was previously proposed in Jaffe [J. Acoust. Soc. Am. 105, 3168-3175 (1999)]. The goal of the algorithm is to improve estimates of both target position and target strength by minimizing a least-squares residual between noise-corrupted target measurement data and the output of a model of the sonar's amplitude response to a target at a set of known locations. Although this positional estimator was shown to be a maximum likelihood estimator, in principle, experimental verification was desired because of interest in understanding its true performance. Here, the accuracy of the algorithm is investigated by analyzing the correspondence between a target's true position and the algorithm's estimate. True target position was measured by precise translation of a small test target (bead) or from the analysis of images of fish from a coregistered optical imaging system. Results with the stationary spherical test bead in a high signal-to-noise environment indicate that a large increase in resolution is possible, while results with commercial aquarium fish indicate a smaller increase is obtainable. However, in both experiments the algorithm provides improved estimates of target position over those obtained by simply accepting the angular positions of the sonar beam with maximum output as target position. In addition, increased accuracy in target strength estimation is possible by considering the effects of the sonar beam patterns relative to the interpolated position. A benefit of the algorithm is that it can be applied "ex post facto" to existing data sets from commercial multibeam sonar systems when only the beam intensities have been stored after suitable calibration.
Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing
Chen, Geng; Zhang, Pei; Li, Ke; Wee, Chong-Yaw; Wu, Yafeng; Shen, Dinggang; Yap, Pew-Thian
2016-01-01
Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations. PMID:27892534
Improved estimates of global ocean circulation, heat transport and mixing from hydrographic data.
Ganachaud, A; Wunsch, C
2000-11-23
Through its ability to transport large amounts of heat, fresh water and nutrients, the ocean is an essential regulator of climate. The pathways and mechanisms of this transport and its stability are critical issues in understanding the present state of climate and the possibilities of future changes. Recently, global high-quality hydrographic data have been gathered in the World Ocean Circulation Experiment (WOCE), to obtain an accurate picture of the present circulation. Here we combine the new data from high-resolution trans-oceanic sections and current meters with climatological wind fields, biogeochemical balances and improved a priori error estimates in an inverse model, to improve estimates of the global circulation and heat fluxes. Our solution resolves globally vertical mixing across surfaces of equal density, with coefficients in the range (3-12) x 10(-4) m2 s(-1). Net deep-water production rates amount to (15 +/- 12) x 10(6) m3 s(-1) in the North Atlantic Ocean and (21 +/- 6) x 10(6) m3 s(-1) in the Southern Ocean. Our estimates provide a new reference state for future climate studies with rigorous estimates of the uncertainties.
An improved method for Q-factor estimates based on the frequency-weighted-exponential function
NASA Astrophysics Data System (ADS)
Li, Chuanhui; Liu, Xuewei
2016-11-01
The frequency-weighted-exponential (FWE) function was designed to fit asymmetric amplitude spectra by two parameters: symmetry index and bandwidth factor. It was applied to Q-factor estimates by fitting the amplitude spectra of source and attenuated wavelet. This method for Q-factor estimates was called the FWE method. The accuracy of the Q-factor estimates by the FWE method depends on the similarity between the modeled FWE functions and the amplitude spectra of source and attenuated wavelet. However, the amplitude spectra of source and attenuated wavelet are poorly fitted when the FWE function are modeled by measuring the symmetry index and bandwidth factor by their definitions. Hence we perform an improvement to the FWE method, where two FWE functions are employed to fit the amplitude spectra of source and attenuated wavelet by the Least Square Method to obtain the optimal symmetry index and bandwidth factor. The improved FWE method enhances the accuracy of the Q-factor estimates, and it also maintains the advantages of good applicability and tolerance to random noise of the original FWE method.
Improved estimates of capital formation in the National Health Expenditure Accounts.
Sensenig, Arthur L; Donahoe, Gerald F
2006-01-01
The National Health Expenditure Accounts (NHEA) were revised with the release of the 2004 estimates. The largest revision was the incorporation of a more comprehensive measure of investment in medical sector capital. The revision raised total health expenditures' share of gross domestic product (GDP) from 15.4 to 15.8 percent in 2003. The improved measure encompasses investment in moveable equipment and software, as well as expenditures for the construction of structures used by the medical sector.
Improving the Cost Estimation of Space Systems. Past Lessons and Future Recommendations
2008-01-01
Books & Publications Make a charitable contribution Support RAND Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for... make the acquisition of NSS systems more realistic in terms of estimated costs. In turn, the former commander of Air Force Space Command (AFSPC), Gen...the technical assessments should be improved by collecting and making available more relevant data and increasing visibility into contractor’s
Estimating Typhoon Rainfall over Sea from SSM/I Satellite Data Using an Improved Genetic Programming
NASA Astrophysics Data System (ADS)
Yeh, K.; Wei, H.; Chen, L.; Liu, G.
2010-12-01
Estimating Typhoon Rainfall over Sea from SSM/I Satellite Data Using an Improved Genetic Programming Keh-Chia Yeha, Hsiao-Ping Weia,d, Li Chenb, and Gin-Rong Liuc a Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan, 300, R.O.C. b Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsinchu, Taiwan, 300, R.O.C. c Center for Space and Remote Sensing Research, National Central University, Tao-Yuan, Taiwan, 320, R.O.C. d National Science and Technology Center for Disaster Reduction, Taipei County, Taiwan, 231, R.O.C. Abstract This paper proposes an improved multi-run genetic programming (GP) and applies it to predict the rainfall using meteorological satellite data. GP is a well-known evolutionary programming and data mining method, used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. This study makes an improvement to enhance escape ability from local optimums during the optimization procedure. The GP continuously runs several times by replacing the terminal nodes at the next run with the best solution at the current run. The current novel model improves GP, obtaining a highly nonlinear mathematical equation to estimate the rainfall. In the case study, this improved GP described above combining with SSM/I satellite data is employed to establish a suitable method for estimating rainfall at sea surface during typhoon periods. These estimated rainfalls are then verified with the data from four rainfall stations located at Peng-Jia-Yu, Don-Gji-Dao, Lan-Yu, and Green Island, which are four small islands around Taiwan. From the results, the improved GP can generate sophisticated and accurate nonlinear mathematical equation through two-run learning procedures which outperforms the traditional multiple linear regression, empirical equations and back-propagated network
NASA Astrophysics Data System (ADS)
Wang, Rong; Chen, Jing M.; Pavlic, Goran; Arain, Altaf
2016-09-01
Winter leaf area index (LAI) of evergreen coniferous forests exerts strong control on the interception of snow, snowmelt and energy balance. Simulation of winter LAI and associated winter processes in land surface models is challenging. Retrieving winter LAI from remote sensing data is difficult due to cloud contamination, poor illumination, lower solar elevation and higher radiation reflection by snow background. Underestimated winter LAI in evergreen coniferous forests is one of the major issues limiting the application of current remote sensing LAI products. It has not been fully addressed in past studies in the literature. In this study, we used needle lifespan to correct winter LAI in a remote sensing product developed by the University of Toronto. For the validation purpose, the corrected winter LAI was then used to calculate land surface albedo at five FLUXNET coniferous forests in Canada. The RMSE and bias values for estimated albedo were 0.05 and 0.011, respectively, for all sites. The albedo map over coniferous forests across Canada produced with corrected winter LAI showed much better agreement with the GLASS (Global LAnd Surface Satellites) albedo product than the one produced with uncorrected winter LAI. The results revealed that the corrected winter LAI yielded much greater accuracy in simulating land surface albedo, making the new LAI product an improvement over the original one. Our study will help to increase the usability of remote sensing LAI products in land surface energy budget modeling.
Improving estimation of microseismic focal mechanisms using a high-resolution velocity model
NASA Astrophysics Data System (ADS)
Chen, T.; Chen, Y.; Lin, Y.; Huang, L.
2015-12-01
Injection and migration of CO2 during the geological carbon sequestration change the pore pressure and stress distribution in the reservoir. The change in stress may induce brittle failure on fractures, causing microseismic events. Focal mechanisms of induced microseismic events are useful for understanding stress evolution in the reservoir. An accurate estimation of microseismic focal mechanism depends on the accuracy of velocity models. In this work, we study the improvement on estimation of microseismic focal mechanisms using a high-resolution velocity model. We obtain the velocity model using a velocity inversion algorithm with a modified total-variation scheme rather than the commonly used Tikhonov regularization technique. We demonstrate with synthetic microseismic data that the velocity inversion method with a modified total-variation regularization scheme improves velocity inversion, and the improved velocity models enhance the accuracy of estimated focal mechanisms of microseismic events. We apply the new methodology to microseismic data acquired at a CO2-EOR (enhanced oil recovery) site at Aneth, Utah.
NASA Astrophysics Data System (ADS)
Žugec, P.; Bosnar, D.; Colonna, N.; Gunsing, F.
2016-08-01
The relation between the neutron background in neutron capture measurements and the neutron sensitivity related to the experimental setup is examined. It is pointed out that a proper estimate of the neutron background may only be obtained by means of dedicated simulations taking into account the full framework of the neutron-induced reactions and their complete temporal evolution. No other presently available method seems to provide reliable results, in particular under the capture resonances. An improved neutron background estimation technique is proposed, the main improvement regarding the treatment of the neutron sensitivity, taking into account the temporal evolution of the neutron-induced reactions. The technique is complemented by an advanced data analysis procedure based on relativistic kinematics of neutron scattering. The analysis procedure allows for the calculation of the neutron background in capture measurements, without requiring the time-consuming simulations to be adapted to each particular sample. A suggestion is made on how to improve the neutron background estimates if neutron background simulations are not available.
NASA Astrophysics Data System (ADS)
Cui, Weiguang; Power, Chris; Biffi, Veronica; Borgani, Stefano; Murante, Giuseppe; Fabjan, Dunja; Knebe, Alexander; Lewis, Geraint F.; Poole, Greg B.
2016-03-01
Galaxy clusters are an established and powerful test-bed for theories of both galaxy evolution and cosmology. Accurate interpretation of cluster observations often requires robust identification of the location of the centre. Using a statistical sample of clusters drawn from a suite of cosmological simulations in which we have explored a range of galaxy formation models, we investigate how the location of this centre is affected by the choice of observable - stars, hot gas, or the full mass distribution as can be probed by the gravitational potential. We explore several measures of cluster centre: the minimum of the gravitational potential, which would expect to define the centre if the cluster is in dynamical equilibrium; the peak of the density; the centre of brightest cluster galaxy (BCG); and the peak and centroid of X-ray luminosity. We find that the centre of BCG correlates more strongly with the minimum of the gravitational potential than the X-ray defined centres, while active galactic nuclei feedback acts to significantly enhance the offset between the peak X-ray luminosity and minimum gravitational potential. These results highlight the importance of centre identification when interpreting clusters observations, in particular when comparing theoretical predictions and observational data.
NASA Astrophysics Data System (ADS)
Singh, Gurmeet; Nandi, Apurba; Gadre, Shridhar R.
2016-03-01
A pragmatic method based on the molecular tailoring approach (MTA) for estimating the complete basis set (CBS) limit at Møller-Plesset second order perturbation (MP2) theory accurately for large molecular clusters with limited computational resources is developed. It is applied to water clusters, (H2O)n (n = 7, 8, 10, 16, 17, and 25) optimized employing aug-cc-pVDZ (aVDZ) basis-set. Binding energies (BEs) of these clusters are estimated at the MP2/aug-cc-pVNZ (aVNZ) [N = T, Q, and 5 (whenever possible)] levels of theory employing grafted MTA (GMTA) methodology and are found to lie within 0.2 kcal/mol of the corresponding full calculation MP2 BE, wherever available. The results are extrapolated to CBS limit using a three point formula. The GMTA-MP2 calculations are feasible on off-the-shelf hardware and show around 50%-65% saving of computational time. The methodology has a potential for application to molecular clusters containing ˜100 atoms.
Robineau, Olivier; Frange, Pierre; Barin, Francis; Cazein, Françoise; Girard, Pierre-Marie; Chaix, Marie-Laure; Kreplak, Georges; Boelle, Pierre-Yves; Morand-Joubert, Laurence
2015-01-01
Objectives To relate socio-demographic and virological information to phylogenetic clustering in HIV infected patients in a limited geographical area and to evaluate the role of recently infected individuals in the spread of HIV. Methods HIV-1 pol sequences from newly diagnosed and treatment-naive patients receiving follow-up between 2008 and 2011 by physicians belonging to a health network in Paris were used to build a phylogenetic tree using neighbour-joining analysis. Time since infection was estimated by immunoassay to define recently infected patients (very early infected presenters, VEP). Data on socio-demographic, clinical and biological features in clustered and non-clustered patients were compared. Chains of infection structure was also analysed. Results 547 patients were included, 49 chains of infection containing 108 (20%) patients were identified by phylogenetic analysis. analysis. Eighty individuals formed pairs and 28 individuals were belonging to larger clusters. The median time between two successive HIV diagnoses in the same chain of infection was 248 days [CI = 176–320]. 34.7% of individuals were considered as VEP, and 27% of them were included in chains of infection. Multivariable analysis showed that belonging to a cluster was more frequent in VEP and those under 30 years old (OR: 3.65, 95 CI 1.49–8.95, p = 0.005 and OR: 2.42, 95% CI 1.05–5.85, p = 0.04 respectively). The prevalence of drug resistance was not associated with belonging to a pair or a cluster. Within chains, VEP were not grouped together more than chance predicted (p = 0.97). Conclusions Most newly diagnosed patients did not belong to a chain of infection, confirming the importance of undiagnosed or untreated HIV infected individuals in transmission. Furthermore, clusters involving both recently infected individuals and longstanding infected individuals support a substantial role in transmission of the latter before diagnosis. PMID:26267615
NASA Astrophysics Data System (ADS)
Zhang, Qian; Ball, William P.
2017-04-01
Regression-based approaches are often employed to estimate riverine constituent concentrations and fluxes based on typically sparse concentration observations. One such approach is the recently developed WRTDS (;Weighted Regressions on Time, Discharge, and Season;) method, which has been shown to provide more accurate estimates than prior approaches in a wide range of applications. Centered on WRTDS, this work was aimed at developing improved models for constituent concentration and flux estimation by accounting for antecedent discharge conditions. Twelve modified models were developed and tested, each of which contains one additional flow variable to represent antecedent conditions and which can be directly derived from the daily discharge record. High-resolution (∼daily) data at nine diverse monitoring sites were used to evaluate the relative merits of the models for estimation of six constituents - chloride (Cl), nitrate-plus-nitrite (NOx), total Kjeldahl nitrogen (TKN), total phosphorus (TP), soluble reactive phosphorus (SRP), and suspended sediment (SS). For each site-constituent combination, 30 concentration subsets were generated from the original data through Monte Carlo subsampling and then used to evaluate model performance. For the subsampling, three sampling strategies were adopted: (A) 1 random sample each month (12/year), (B) 12 random monthly samples plus additional 8 random samples per year (20/year), and (C) flow-stratified sampling with 12 regular (non-storm) and 8 storm samples per year (20/year). Results reveal that estimation performance varies with both model choice and sampling strategy. In terms of model choice, the modified models show general improvement over the original model under all three sampling strategies. Major improvements were achieved for NOx by the long-term flow-anomaly model and for Cl by the ADF (average discounted flow) model and the short-term flow-anomaly model. Moderate improvements were achieved for SS, TP, and TKN
NASA Astrophysics Data System (ADS)
Ebrahimian, Ali; Wilson, Bruce N.; Gulliver, John S.
2016-05-01
Impervious surfaces are useful indicators of the urbanization impacts on water resources. Effective impervious area (EIA), which is the portion of total impervious area (TIA) that is hydraulically connected to the drainage system, is a better catchment parameter in the determination of actual urban runoff. Development of reliable methods for quantifying EIA rather than TIA is currently one of the knowledge gaps in the rainfall-runoff modeling context. The objective of this study is to improve the rainfall-runoff data analysis method for estimating EIA fraction in urban catchments by eliminating the subjective part of the existing method and by reducing the uncertainty of EIA estimates. First, the theoretical framework is generalized using a general linear least square model and using a general criterion for categorizing runoff events. Issues with the existing method that reduce the precision of the EIA fraction estimates are then identified and discussed. Two improved methods, based on ordinary least square (OLS) and weighted least square (WLS) estimates, are proposed to address these issues. The proposed weighted least squares method is then applied to eleven urban catchments in Europe, Canada, and Australia. The results are compared to map measured directly connected impervious area (DCIA) and are shown to be consistent with DCIA values. In addition, both of the improved methods are applied to nine urban catchments in Minnesota, USA. Both methods were successful in removing the subjective component inherent in the analysis of rainfall-runoff data of the current method. The WLS method is more robust than the OLS method and generates results that are different and more precise than the OLS method in the presence of heteroscedastic residuals in our rainfall-runoff data.
Improving radar estimates of rainfall using an input subset of artificial neural networks
NASA Astrophysics Data System (ADS)
Yang, Tsun-Hua; Feng, Lei; Chang, Lung-Yao
2016-04-01
An input subset including average radar reflectivity (Zave) and its standard deviation (SD) is proposed to improve radar estimates of rainfall based on a radial basis function (RBF) neural network. The RBF derives a relationship from a historical input subset, called a training dataset, consisting of radar measurements such as reflectivity (Z) aloft and associated rainfall observation (R) on the ground. The unknown rainfall rate can then be predicted over the derived relationship with known radar measurements. The selection of the input subset has a significant impact on the prediction performance. This study simplified the selection of input subsets and studied its improvement in rainfall estimation. The proposed subset includes: (1) the Zave of the observed Z within a given distance from the ground observation to represent the intensity of a storm system and (2) the SD of the observed Z to describe the spatial variability. Using three historical rainfall events in 1999 near Darwin, Australia, the performance evaluation is conducted using three approaches: an empirical Z-R relation, RBF with Z, and RBF with Zave and SD. The results showed that the RBF with both Zave and SD achieved better rainfall estimations than the RBF using only Z. Two performance measures were used: (1) the Pearson correlation coefficient improved from 0.15 to 0.58 and (2) the average root-mean-square error decreased from 14.14 mm to 11.43 mm. The proposed model and findings can be used for further applications involving the use of neural networks for radar estimates of rainfall.
NASA Astrophysics Data System (ADS)
Chen, Huilin; Montzka, Steve; Andrews, Arlyn; Sweeney, Colm; Jacobson, Andy; Miller, Ben; Masarie, Ken; Jung, Martin; Gerbig, Christoph; Campbell, Elliott; Abu-Naser, Mohammad; Berry, Joe; Baker, Ian; Tans, Pieter
2013-04-01
Understanding the responses of gross primary production (GPP) to climate change is essential for improving our prediction of climate change. To this end, it is important to accurately partition net ecosystem exchange of carbon into GPP and respiration. Recent studies suggest that carbonyl sulfide is a useful tracer to provide a constraint on GPP, based on the fact that both COS and CO2 are simultaneously taken up by plants and the quantitative correlation between GPP and COS plant uptake. We will present an assessment of North American GPP estimates from the Simple Biosphere (SiB) model, the Carnegie-Ames-Stanford Approach (CASA) model, and the MPI-BGC model through atmospheric transport simulations of COS in a receptor oriented framework. The newly upgraded Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) will be employed to compute the influence functions, i.e. footprints, to link the surface fluxes to the concentration changes at the receptor observations. The HYSPLIT is driven by the 3-hourly archived NAM 12km meteorological data from NOAA NCEP. The background concentrations are calculated using empirical curtains along the west coast of North America that have been created by interpolating in time and space the observations at the NOAA/ESRL marine boundary layer stations and from aircraft vertical profiles. The plant uptake of COS is derived from GPP estimates of biospheric models. The soil uptake and anthropogenic emissions are from Kettle et al. 2002. In addition, we have developed a new soil flux map of COS based on observations of molecular hydrogen (H2), which shares a common soil uptake term but lacks a vegetative sink. We will also improve the GPP estimates by assimilating atmospheric observations of COS in the receptor oriented framework, and then present the assessment of the improved GPP estimates against variations of climate variables such as temperature and precipitation.
Mahowald, Natalie
2016-11-29
Soils in natural and managed ecosystems and wetlands are well known sources of methane, nitrous oxides, and reactive nitrogen gases, but the magnitudes of gas flux to the atmosphere are still poorly constrained. Thus, the reasons for the large increases in atmospheric concentrations of methane and nitrous oxide since the preindustrial time period are not well understood. The low atmospheric concentrations of methane and nitrous oxide, despite being more potent greenhouse gases than carbon dioxide, complicate empirical studies to provide explanations. In addition to climate concerns, the emissions of reactive nitrogen gases from soils are important to the changing nitrogen balance in the earth system, subject to human management, and may change substantially in the future. Thus improved modeling of the emission fluxes of these species from the land surface is important. Currently, there are emission modules for methane and some nitrogen species in the Community Earth System Model’s Community Land Model (CLM-ME/N); however, there are large uncertainties and problems in the simulations, resulting in coarse estimates. In this proposal, we seek to improve these emission modules by combining state-of-the-art process modules for emissions, available data, and new optimization methods. In earth science problems, we often have substantial data and knowledge of processes in disparate systems, and thus we need to combine data and a general process level understanding into a model for projections of future climate that are as accurate as possible. The best methodologies for optimization of parameters in earth system models are still being developed. In this proposal we will develop and apply surrogate algorithms that a) were especially developed for computationally expensive simulations like CLM-ME/N models; b) were (in the earlier surrogate optimization Stochastic RBF) demonstrated to perform very well on computationally expensive complex partial differential equations in
Habecker, Patrick; Dombrowski, Kirk; Khan, Bilal
2015-01-01
Researchers interested in studying populations that are difficult to reach through traditional survey methods can now draw on a range of methods to access these populations. Yet many of these methods are more expensive and difficult to implement than studies using conventional sampling frames and trusted sampling methods. The network scale-up method (NSUM) provides a middle ground for researchers who wish to estimate the size of a hidden population, but lack the resources to conduct a more specialized hidden population study. Through this method it is possible to generate population estimates for a wide variety of groups that are perhaps unwilling to self-identify as such (for example, users of illegal drugs or other stigmatized populations) via traditional survey tools such as telephone or mail surveys—by asking a representative sample to estimate the number of people they know who are members of such a “hidden” subpopulation. The original estimator is formulated to minimize the weight a single scaling variable can exert upon the estimates. We argue that this introduces hidden and difficult to predict biases, and instead propose a series of methodological advances on the traditional scale-up estimation procedure, including a new estimator. Additionally, we formalize the incorporation of sample weights into the network scale-up estimation process, and propose a recursive process of back estimation “trimming” to identify and remove poorly performing predictors from the estimation process. To demonstrate these suggestions we use data from a network scale-up mail survey conducted in Nebraska during 2014. We find that using the new estimator and recursive trimming process provides more accurate estimates, especially when used in conjunction with sampling weights. PMID:26630261
NASA Astrophysics Data System (ADS)
Aulenbach, Brent T.
2013-10-01
A regression-model based approach is a commonly used, efficient method for estimating streamwater constituent load when there is a relationship between streamwater constituent concentration and continuous variables such as streamwater discharge, season and time. A subsetting experiment using a 30-year dataset of daily suspended sediment observations from the Mississippi River at Thebes, Illinois, was performed to determine optimal sampling frequency, model calibration period length, and regression model methodology, as well as to determine the effect of serial correlation of model residuals on load estimate precision. Two regression-based methods were used to estimate streamwater loads, the Adjusted Maximum Likelihood Estimator (AMLE), and the composite method, a hybrid load estimation approach. While both methods accurately and precisely estimated loads at the model's calibration period time scale, precisions were progressively worse at shorter reporting periods, from annually to monthly. Serial correlation in model residuals resulted in observed AMLE precision to be significantly worse than the model calculated standard errors of prediction. The composite method effectively improved upon AMLE loads for shorter reporting periods, but required a sampling interval of at least 15-days or shorter, when the serial correlations in the observed load residuals were greater than 0.15. AMLE precision was better at shorter sampling intervals and when using the shortest model calibration periods, such that the regression models better fit the temporal changes in the concentration-discharge relationship. The models with the largest errors typically had poor high flow sampling coverage resulting in unrepresentative models. Increasing sampling frequency and/or targeted high flow sampling are more efficient approaches to ensure sufficient sampling and to avoid poorly performing models, than increasing calibration period length.
Aulenbach, Brent T.
2013-01-01
A regression-model based approach is a commonly used, efficient method for estimating streamwater constituent load when there is a relationship between streamwater constituent concentration and continuous variables such as streamwater discharge, season and time. A subsetting experiment using a 30-year dataset of daily suspended sediment observations from the Mississippi River at Thebes, Illinois, was performed to determine optimal sampling frequency, model calibration period length, and regression model methodology, as well as to determine the effect of serial correlation of model residuals on load estimate precision. Two regression-based methods were used to estimate streamwater loads, the Adjusted Maximum Likelihood Estimator (AMLE), and the composite method, a hybrid load estimation approach. While both methods accurately and precisely estimated loads at the model’s calibration period time scale, precisions were progressively worse at shorter reporting periods, from annually to monthly. Serial correlation in model residuals resulted in observed AMLE precision to be significantly worse than the model calculated standard errors of prediction. The composite method effectively improved upon AMLE loads for shorter reporting periods, but required a sampling interval of at least 15-days or shorter, when the serial correlations in the observed load residuals were greater than 0.15. AMLE precision was better at shorter sampling intervals and when using the shortest model calibration periods, such that the regression models better fit the temporal changes in the concentration–discharge relationship. The models with the largest errors typically had poor high flow sampling coverage resulting in unrepresentative models. Increasing sampling frequency and/or targeted high flow sampling are more efficient approaches to ensure sufficient sampling and to avoid poorly performing models, than increasing calibration period length.
An estimation method of MR signal parameters for improved image reconstruction in unilateral scanner
NASA Astrophysics Data System (ADS)
Bergman, Elad; Yeredor, Arie; Nevo, Uri
2013-12-01
Unilateral NMR devices are used in various applications including non-destructive testing and well logging, but are not used routinely for imaging. This is mainly due to the inhomogeneous magnetic field (B0) in these scanners. This inhomogeneity results in low sensitivity and further forces the use of the slow single point imaging scan scheme. Improving the measurement sensitivity is therefore an important factor as it can improve image quality and reduce imaging times. Short imaging times can facilitate the use of this affordable and portable technology for various imaging applications. This work presents a statistical signal-processing method, designed to fit the unique characteristics of imaging with a unilateral device. The method improves the imaging capabilities by improving the extraction of image information from the noisy data. This is done by the use of redundancy in the acquired MR signal and by the use of the noise characteristics. Both types of data were incorporated into a Weighted Least Squares estimation approach. The method performance was evaluated with a series of imaging acquisitions applied on phantoms. Images were extracted from each measurement with the proposed method and were compared to the conventional image reconstruction. All measurements showed a significant improvement in image quality based on the MSE criterion - with respect to gold standard reference images. An integration of this method with further improvements may lead to a prominent reduction in imaging times aiding the use of such scanners in imaging application.
NASA Astrophysics Data System (ADS)
Behrangi, Ali
In respond to the community demands, combining microwave (MW) and infrared (IR) estimates of precipitation has been an active area of research since past two decades. The anticipated launching of NASA's Global Precipitation Measurement (GPM) mission and the increasing number of spectral bands in recently launched geostationary platforms will provide greater opportunities for investigating new approaches to combine multi-source information towards improved global high resolution precipitation retrievals. After years of the communities' efforts the limitations of the existing techniques are: (1) Drawbacks of IR-only techniques to capture warm rainfall and screen out no-rain thin cirrus clouds; (2) Grid-box- only dependency of many algorithms with not much effort to capture the cloud textures whether in local or cloud patch scale; (3) Assumption of indirect relationship between rain rate and cloud-top temperature that force high intensity precipitation to any cold cloud; (4) Neglecting the dynamics and evolution of cloud in time; (5) Inconsistent combination of MW and IR-based precipitation estimations due to the combination strategies and as a result of above described shortcomings. This PhD dissertation attempts to improve the combination of data from Geostationary Earth Orbit (GEO) and Low-Earth Orbit (LEO) satellites in manners that will allow consistent high resolution integration of the more accurate precipitation estimates, directly observed through LEO's PMW sensors, into the short-term cloud evolution process, which can be inferred from GEO images. A set of novel approaches are introduced to cope with the listed limitations and is consist of the following four consecutive components: (1) starting with the GEO part and by using an artificial-neural network based method it is demonstrated that inclusion of multi-spectral data can ameliorate existing problems associated with IR-only precipitating retrievals; (2) through development of Precipitation Estimation
NASA Astrophysics Data System (ADS)
Juesas, P.; Ramasso, E.
2016-12-01
Condition monitoring aims at ensuring system safety which is a fundamental requirement for industrial applications and that has become an inescapable social demand. This objective is attained by instrumenting the system and developing data analytics methods such as statistical models able to turn data into relevant knowledge. One difficulty is to be able to correctly estimate the parameters of those methods based on time-series data. This paper suggests the use of the Weighted Distribution Theory together with the Expectation-Maximization algorithm to improve parameter estimation in statistical models with latent variables with an application to health monotonic under uncertainty. The improvement of estimates is made possible by incorporating uncertain and possibly noisy prior knowledge on latent variables in a sound manner. The latent variables are exploited to build a degradation model of dynamical system represented as a sequence of discrete states. Examples on Gaussian Mixture Models, Hidden Markov Models (HMM) with discrete and continuous outputs are presented on both simulated data and benchmarks using the turbofan engine datasets. A focus on the application of a discrete HMM to health monitoring under uncertainty allows to emphasize the interest of the proposed approach in presence of different operating conditions and fault modes. It is shown that the proposed model depicts high robustness in presence of noisy and uncertain prior.
Integrating SAS and GIS software to improve habitat-use estimates from radiotelemetry data
Kenow, K.P.; Wright, R.G.; Samuel, M.D.; Rasmussen, P.W.
2001-01-01
Radiotelemetry has been used commonly to remotely determine habitat use by a variety of wildlife species. However, habitat misclassification can occur because the true location of a radiomarked animal can only be estimated. Analytical methods that provide improved estimates of habitat use from radiotelemetry location data using a subsampling approach have been proposed previously. We developed software, based on these methods, to conduct improved habitat-use analyses. A Statistical Analysis System (SAS)-executable file generates a random subsample of points from the error distribution of an estimated animal location and formats the output into ARC/INFO-compatible coordinate and attribute files. An associated ARC/INFO Arc Macro Language (AML) creates a coverage of the random points, determines the habitat type at each random point from an existing habitat coverage, sums the number of subsample points by habitat type for each location, and outputs tile results in ASCII format. The proportion and precision of habitat types used is calculated from the subsample of points generated for each radiotelemetry location. We illustrate the method and software by analysis of radiotelemetry data for a female wild turkey (Meleagris gallopavo).
Improving the Carbon Dioxide Emission Estimates from the Combustion of Fossil Fuels in California
de la Rue du Can, Stephane; Wenzel, Tom; Price, Lynn
2008-08-13
Central to any study of climate change is the development of an emission inventory that identifies and quantifies the State's primary anthropogenic sources and sinks of greenhouse gas (GHG) emissions. CO2 emissions from fossil fuel combustion accounted for 80 percent of California GHG emissions (CARB, 2007a). Even though these CO2 emissions are well characterized in the existing state inventory, there still exist significant sources of uncertainties regarding their accuracy. This report evaluates the CO2 emissions accounting based on the California Energy Balance database (CALEB) developed by Lawrence Berkeley National Laboratory (LBNL), in terms of what improvements are needed and where uncertainties lie. The estimated uncertainty for total CO2 emissions ranges between -21 and +37 million metric tons (Mt), or -6percent and +11percent of total CO2 emissions. The report also identifies where improvements are needed for the upcoming updates of CALEB. However, it is worth noting that the California Air Resources Board (CARB) GHG inventory did not use CALEB data for all combustion estimates. Therefore the range in uncertainty estimated in this report does not apply to the CARB's GHG inventory. As much as possible, additional data sources used by CARB in the development of its GHG inventory are summarized in this report for consideration in future updates to CALEB.
NASA Astrophysics Data System (ADS)
Uppuluri, A. V.; Jost, R. J.; Luecke, C.; White, M. A.
2008-12-01
Analyzing the freeze-thaw dates of high latitude lakes is an important part of climate change studies. Due to the various advantages provided by the use of SAR images, with respect to remote monitoring of small lakes, SAR image analysis is an obvious choice to estimate lake freeze-thaw dates. An important property of SAR images is its texture. The problem of estimating freeze-thaw dates can be restated as a problem of classifying an annual time series of SAR images based on the presence or absence of ice. We analyzed a few algorithms based on texture to improve the estimation of freeze-thaw dates for small lakes using SAR images. We computed the Gray Level Co-occurrence Matrix (GLCM) for each image and extracted ten different texture features from the GLCM. We used these texture features (namely, Energy, Contrast, Correlation, Homogeneity, Entropy, Autocorrelation, Dissimilarity, Cluster Shade, Cluster Prominence and Maximum Probability as previously used in studies related to the texture analysis of SAR sea ice imagery) as input to a group of classification algorithms to find the most accurate classifier and set of texture features that can help to decide the presence or absence of ice on the lake. The accuracy of the estimated freeze-thaw dates is dependent on the accuracy of the classifier. It is considered highly difficult to differentiate between open water (without wind) and the first day of ice formed on the lake (due to the similar mean backscatter values) causing inaccuracy in calculating the freeze date. Similar inaccuracy in calculating the thaw date arise due to the close backscatter values of water (with wind) and later stages of ice on the lake. Our method is promising but requires further research in improving the accuracy of the classifiers and selecting the input features.
Amador Carrascal, Carolina; Chen, Shigao; Manduca, Armando; Greenleaf, James F; Urban, Matthew W
2017-04-01
Quantitative ultrasound elastography is increasingly being used in the assessment of chronic liver disease. Many studies have reported ranges of liver shear wave velocity values for healthy individuals and patients with different stages of liver fibrosis. Nonetheless, ongoing efforts exist to stabilize quantitative ultrasound elastography measurements by assessing factors that influence tissue shear wave velocity values, such as food intake, body mass index, ultrasound scanners, scanning protocols, and ultrasound image quality. Time-to-peak (TTP) methods have been routinely used to measure the shear wave velocity. However, there is still a need for methods that can provide robust shear wave velocity estimation in the presence of noisy motion data. The conventional TTP algorithm is limited to searching for the maximum motion in time profiles at different spatial locations. In this paper, two modified shear wave speed estimation algorithms are proposed. The first method searches for the maximum motion in both space and time [spatiotemporal peak (STP)]; the second method applies an amplitude filter [spatiotemporal thresholding (STTH)] to select points with motion amplitude higher than a threshold for shear wave group velocity estimation. The two proposed methods (STP and STTH) showed higher precision in shear wave velocity estimates compared with TTP in phantom. Moreover, in a cohort of 14 healthy subjects, STP and STTH methods improved both the shear wave velocity measurement precision and the success rate of the measurement compared with conventional TTP.
Miller, David A.; Nichols, J.D.; McClintock, B.T.; Grant, E.H.C.; Bailey, L.L.; Weir, L.A.
2011-01-01
Efforts to draw inferences about species occurrence frequently account for false negatives, the common situation when individuals of a species are not detected even when a site is occupied. However, recent studies suggest the need to also deal with false positives, which occur when species are misidentified so that a species is recorded as detected when a site is unoccupied. Bias in estimators of occupancy, colonization, and extinction can be severe when false positives occur. Accordingly, we propose models that simultaneously account for both types of error. Our approach can be used to improve estimates of occupancy for study designs where a subset of detections is of a type or method for which false positives can be assumed to not occur. We illustrate properties of the estimators with simulations and data for three species of frogs. We show that models that account for possible misidentification have greater support (lower AIC for two species) and can yield substantially different occupancy estimates than those that do not. When the potential for misidentification exists, researchers should consider analytical techniques that can account for this source of error, such as those presented here. ?? 2011 by the Ecological Society of America..
An Improved Approach for Estimating Daily Net Radiation over the Heihe River Basin
Wu, Bingfang; Liu, Shufu; Zhu, Weiwei; Yan, Nana; Xing, Qiang; Tan, Shen
2017-01-01
Net radiation plays an essential role in determining the thermal conditions of the Earth’s surface and is an important parameter for the study of land-surface processes and global climate change. In this paper, an improved satellite-based approach to estimate the daily net radiation is presented, in which sunshine duration were derived from the geostationary meteorological satellite (FY-2D) cloud classification product, the monthly empirical as and bs Angstrom coefficients for net shortwave radiation were calibrated by spatial fitting of the ground data from 1997 to 2006, and the daily net longwave radiation was calibrated with ground data from 2007 to 2010 over the Heihe River Basin in China. The estimated daily net radiation values were validated against ground data for 12 months in 2008 at four stations with different underlying surface types. The average coefficient of determination (R2) was 0.8489, and the averaged Nash-Sutcliffe equation (NSE) was 0.8356. The close agreement between the estimated daily net radiation and observations indicates that the proposed method is promising, especially given the comparison between the spatial distribution and the interpolation of sunshine duration. Potential applications include climate research, energy balance studies and the estimation of global evapotranspiration. PMID:28054976
Improvements in near-surface geophysical applications for hydrogeological parameter estimation
NASA Astrophysics Data System (ADS)
Addison, Adrian Demond
One application of near-surface geophysical techniques is the hydrogeological parameter estimation. Hydrogeological estimated parameters such as volumetric water content, porosity, and hydraulic conductivity are useful in predicting groundwater flow. Therefore, any improvements in the field acquisition and data processing of the geophysical data will provide better results in estimating these parameters. This research examines the difficulties associated with processing and attribute analyses with shallow seismic P-wave reflection data, the application of the empirical mode decomposition (EMD) as a processing tool for ground-penetrating radar (GPR), and the use of GPR as tool in the assessment of bank filtration. Near-surface seismic reflection data are difficult to process because of the lack of reflections in the shot gathers; however, this research demonstrated that the application of certain steps such F-k filtering and velocity analysis can achieve the desired result, a more robust geologic model. The EMD technique was applied (removal of the WOW noise) to processing steps for GPR data in estimating hydrogeological parameters by providing significant stability during the calculation of dielectric constants. GPR techniques are widely known and diverse, but one rather different application of the GPR was to assess the suitability of bank filtration at a site in South Carolina. Finally, a multi-attribute analysis approach, a rather new application for near-surface seismic data, was used in predicting porosity from well logs and seismic data.
An Improved Approach for Estimating Daily Net Radiation over the Heihe River Basin.
Wu, Bingfang; Liu, Shufu; Zhu, Weiwei; Yan, Nana; Xing, Qiang; Tan, Shen
2017-01-04
Net radiation plays an essential role in determining the thermal conditions of the Earth's surface and is an important parameter for the study of land-surface processes and global climate change. In this paper, an improved satellite-based approach to estimate the daily net radiation is presented, in which sunshine duration were derived from the geostationary meteorological satellite (FY-2D) cloud classification product, the monthly empirical as and bs Angstrom coefficients for net shortwave radiation were calibrated by spatial fitting of the ground data from 1997 to 2006, and the daily net longwave radiation was calibrated with ground data from 2007 to 2010 over the Heihe River Basin in China. The estimated daily net radiation values were validated against ground data for 12 months in 2008 at four stations with different underlying surface types. The average coefficient of determination (R²) was 0.8489, and the averaged Nash-Sutcliffe equation (NSE) was 0.8356. The close agreement between the estimated daily net radiation and observations indicates that the proposed method is promising, especially given the comparison between the spatial distribution and the interpolation of sunshine duration. Potential applications include climate research, energy balance studies and the estimation of global evapotranspiration.
Improvement of sub-pixel global motion estimation in UAV image stabilization
NASA Astrophysics Data System (ADS)
Li, Yingjuan; Ji, Ming; He, Junfeng; Zhen, Kang; Yang, Yizhou; Chen, Ying
2016-01-01
Global motion estimation within frames is very important in the UAV(unmanned aerial vehicle) image stabilization system. A fast algorithm based on phase correlation and image down-sampling in sub-pixel was proposed. First, down-sampling of the two frames to quantitatively reduce calculate data. Then, take the method based of phase correlation to realize the global motion estimation in integer-pixel. When it calculated out, chooses the overlapped area of the two frames and interpolated them with zero, then adopts the method based on phase correlation to achieve the global motion estimation in sub-pixel. At last, weighted calculate the result in integer-pixel and the result in sub-pixel, the global motion displacement in sub-pixel of the two images will be calculated out. Experimental results show that, using the proposed algorithm can not only achieve good robustness to the influence of noise, illumination and partially sheltered but also improve the accuracy of motion estimation and efficiency of computing significantly.
Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.
2014-01-01
Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.
Improving waterfowl production estimates: results of a test in the prairie pothole region
Arnold, P.M.; Cowardin, L.M.
1985-01-01
The U.S. Fish and Wildlife Service in an effort to improve and standardize methods for estimating waterfowl production tested a new technique in the four-county Arrowwood Wetland Management District (WMD) for three years (1982-1984). On 14 randomly selected 10.36 km2 plots, upland and wetland habitat was mapped, classified, and digitized. Waterfowl breeding pairs were counted twice each year and the proportion of wetland basins containing water was determined. Pair numbers and habitat conditions were entered into a computer model developed by Northern Prairie Wildlife Research Center. That model estimates production on small federally owned wildlife tracts, federal wetland easements, and private land. Results indicate that production estimates were most accurate for mallards (Anas platyrhynchos), the species for which the computer model and data base were originally designed. Predictions for the pintail (Anas acuta), gadwall (A. strepa), blue-winged teal (A. discors), and northern shoveler (A. clypeata) were believed to be less accurate. Modeling breeding period dynamics of a waterfowl species and making credible production estimates for a geographic area are possible if the data used in the model are adequate. The process of modeling the breeding period of a species aids in locating areas of insufficient biological knowledge. This process will help direct future research efforts and permit more efficient gathering of field data.
2017-01-01
Background Adherence to guidelines pertaining to stroke prevention in patients with atrial fibrillation is poor. Decision support systems have shown promise in increasing guideline adherence. Aims To improve guideline adherence with a non-obtrusive clinical decision support system integrated in the workflow. Secondly, we seek to capture reasons for guideline non-adherence. Design and setting A cluster randomized controlled trial in Dutch general practices. Method A decision support system was developed that implemented properties positively associated with effectiveness: real-time, non-interruptive and based on data from electronic health records. Recommendations were based on the Dutch general practitioners guideline for atrial fibrillation that uses the CHA2DS2-VAsc for stroke risk stratification. Usage data and responses to the recommendations were logged. Effectiveness was measured as adherence to the guideline. We used a chi square to test for group differences and a mixed effects model to correct for clustering and baseline adherence. Results Our analyses included 781 patients. Usage of the system was low (5%) and declined over time. In total, 76 notifications received a response: 58% dismissal and 42% acceptance. At the end of the study, both groups had improved, by 8% and 5% respectively. There was no statistically significant difference between groups (Control: 50%, Intervention: 55% P = 0.23). Clustered analysis revealed similar results. Only one usable reasons for non-adherence was captured. Conclusion Our study could not demonstrate the effectiveness of a decision support system in general practice, which was likely due to lack of use. Our findings should be used to develop next generation decision support systems that are effective in the challenging setting of general practice. PMID:28245247
Manufacturing Improvement Program for the Oil and Gas Industry Supply Chain and Marketing Cluster
Taylor, Robert
2016-09-28
This project supported upgrades for manufacturing companies in the oil and natural gas supply chain in Oklahoma. The goal is to provide assistance that will lead to the improved efficiency advancement of the manufacturing processes currently used by the existing manufacturing clients. The basis for the work is to improve the economic environment for the clients and the communities they serve.
NASA Astrophysics Data System (ADS)
Maier, John S.; Panza, Janice L.; Bootman, Matt
2007-02-01
Composition-tunable nanocrystals are fluorescent nanoparticles with a uniform particle size and with adjustable optical characteristics. When used for optical labeling of biomolecular targets these and other nanotechnology solutions have enabled new approaches which are possible because of the high optical output, narrow spectral signal, consistent quantum efficiency across a broad emission range and long lived fluorescent behavior of the nanocrystals. When coupled with spectral imaging the full potential of multiplexing multiple probes in a complex matrix can be realized. Spectral imaging can be used to improve sensitivity of narrowband fluorophores through application of chemometric image processing techniques used to reduce the influence of autofluorescence background. Composition-tunable nanocrystals can be complexed together to form nanoclusters which have the advantage of significantly stronger signal and therefore a higher sensitivity. These nanoclusters can be targeted in biomolecular systems using standard live-cell labeling and immunohistochemistry based techniques. Composition-tunable nanocrystals and nanoclusters have comparable mass and brightness across a wide emission range. This enables the production of nanocrystal-based probes that have comparable reactivity and sensitivity over a large color range. We present spectral imaging results of antibody targeted nanocrystal cluster labeling of target proteins in cultured cells and a Western blot experiment. The combination of spectral imaging with the use of clusters of nanocrystals further improves the sensitivity over either of the approaches independently.
Improving weather radar estimates of rainfall using feed-forward neural networks.
Teschl, Reinhard; Randeu, Walter L; Teschl, Franz
2007-05-01
In this paper an approach is described to improve weather radar estimates of rainfall based on a neural network technique. Other than rain gauges which measure the rain rate R directly on the ground, the weather radar measures the reflectivity Z aloft and the rain rate has to be determined over a Z-R relationship. Besides the fact that the rain rate has to be estimated from the reflectivity many other sources of possible errors are inherent to the radar system. In other words the radar measurements contain an amount of observation noise which makes it a demanding task to train the network properly. A feed-forward neural network with Z values as input vector was trained to predict the rain rate R on the ground. The results indicate that the model is able to generalize and the determined input-output relationship is also representative for other sites nearby with similar conditions.
NASA Astrophysics Data System (ADS)
Shen, Ting-ao; Li, Hua-nan; Zhang, Qi-xin; Li, Ming
2017-02-01
The convergence rate and the continuous tracking precision are two main problems of the existing adaptive notch filter (ANF) for frequency tracking. To solve the problems, the frequency is detected by interpolation FFT at first, which aims to overcome the convergence rate of the ANF. Then, referring to the idea of negative feedback, an evaluation factor is designed to monitor the ANF parameters and realize continuously high frequency tracking accuracy. According to the principle, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is put forward. Its basic idea, specific measures and implementation steps are described in detail. The proposed algorithm obtains a fast estimation of the signal frequency, higher accuracy and better universality qualities. Simulation results verified the superiority and validity of the proposed algorithm when compared with original algorithms.
Mackie, C D
2014-01-01
Impacts of underground longwall mining on groundwater systems are commonly assessed using numerical groundwater flow models that are capable of forecasting changes to strata pore pressures and rates of groundwater seepage over the mine life. Groundwater ingress to a mining operation is typically estimated using zone budgets to isolate relevant parts of a model that represent specific mining areas, and to aggregate flows at nominated times within specific model stress periods. These rates can be easily misinterpreted if simplistic averaging of daily flow budgets is adopted. Such misinterpretation has significant implications for design of underground dewatering systems for a new mine site or it may lead to model calibration errors where measured mine water seepage rates are used as a primary calibration constraint. Improved estimates of groundwater ingress can be made by generating a cumulative flow history from zone budget data, then differentiating the cumulative flow history using a low order polynomial convolved through the data set.
Mu, Wenying; Cui, Baotong; Li, Wen; Jiang, Zhengxian
2014-07-01
This paper proposes a scheme for non-collocated moving actuating and sensing devices which is unitized for improving performance in distributed parameter systems. By Lyapunov stability theorem, each moving actuator/sensor agent velocity is obtained. To enhance state estimation of a spatially distributes process, two kinds of filters with consensus terms which penalize the disagreement of the estimates are considered. Both filters can result in the well-posedness of the collective dynamics of state errors and can converge to the plant state. Numerical simulations demonstrate that the effectiveness of such a moving actuator-sensor network in enhancing system performance and the consensus filters converge faster to the plant state when consensus terms are included.
van Twillert, Inonge; Bonačić Marinović, Axel A.; van Gaans-van den Brink, Jacqueline A. M.; Kuipers, Betsy; Berbers, Guy A. M.; van der Maas, Nicoline A. T.; Verheij, Theo J. M.; Versteegh, Florens G. A.; Teunis, Peter F. M.; van Els, Cécile A. C. M.
2016-01-01
Bordetella pertussis circulates even in highly vaccinated countries affecting all age groups. Insight into the scale of concealed reinfections is important as they may contribute to transmission. We therefore investigated whether current single-point serodiagnostic methods are suitable to estimate the prevalence of pertussis reinfection. Two methods based on IgG-Ptx plasma levels alone were used to evaluate the proportion of renewed seroconversions in the past year in a cohort of retrospective pertussis cases ≥ 24 months after a proven earlier symptomatic infection. A Dutch population database was used as a baseline. Applying a classical 62.5 IU/ml IgG-Ptx cut-off, we calculated a seroprevalence of 15% in retrospective cases, higher than the 10% observed in the population baseline. However, this method could not discriminate between renewed seroconversion and waning of previously infection-enhanced IgG-Ptx levels. Two-component cluster analysis of the IgG-Ptx datasets of both pertussis cases and the general population revealed a continuum of intermediate IgG-Ptx levels, preventing the establishment of a positive population and the comparison of prevalence by this alternative method. Next, we investigated the complementary serodiagnostic value of IgA-Ptx levels. When modelling datasets including both convalescent and retrospective cases we obtained new cut-offs for both IgG-Ptx and IgA-Ptx that were optimized to evaluate renewed seroconversions in the ex-cases target population. Combining these cut-offs two-dimensionally, we calculated 8.0% reinfections in retrospective cases, being below the baseline seroprevalence. Our study for the first time revealed the shortcomings of using only IgG-Ptx data in conventional serodiagnostic methods to determine pertussis reinfections. Improved results can be obtained with two-dimensional serodiagnostic profiling. The proportion of reinfections thus established suggests a relatively increased period of protection to renewed
van Twillert, Inonge; Bonačić Marinović, Axel A; van Gaans-van den Brink, Jacqueline A M; Kuipers, Betsy; Berbers, Guy A M; van der Maas, Nicoline A T; Verheij, Theo J M; Versteegh, Florens G A; Teunis, Peter F M; van Els, Cécile A C M
2016-01-01
Bordetella pertussis circulates even in highly vaccinated countries affecting all age groups. Insight into the scale of concealed reinfections is important as they may contribute to transmission. We therefore investigated whether current single-point serodiagnostic methods are suitable to estimate the prevalence of pertussis reinfection. Two methods based on IgG-Ptx plasma levels alone were used to evaluate the proportion of renewed seroconversions in the past year in a cohort of retrospective pertussis cases ≥ 24 months after a proven earlier symptomatic infection. A Dutch population database was used as a baseline. Applying a classical 62.5 IU/ml IgG-Ptx cut-off, we calculated a seroprevalence of 15% in retrospective cases, higher than the 10% observed in the population baseline. However, this method could not discriminate between renewed seroconversion and waning of previously infection-enhanced IgG-Ptx levels. Two-component cluster analysis of the IgG-Ptx datasets of both pertussis cases and the general population revealed a continuum of intermediate IgG-Ptx levels, preventing the establishment of a positive population and the comparison of prevalence by this alternative method. Next, we investigated the complementary serodiagnostic value of IgA-Ptx levels. When modelling datasets including both convalescent and retrospective cases we obtained new cut-offs for both IgG-Ptx and IgA-Ptx that were optimized to evaluate renewed seroconversions in the ex-cases target population. Combining these cut-offs two-dimensionally, we calculated 8.0% reinfections in retrospective cases, being below the baseline seroprevalence. Our study for the first time revealed the shortcomings of using only IgG-Ptx data in conventional serodiagnostic methods to determine pertussis reinfections. Improved results can be obtained with two-dimensional serodiagnostic profiling. The proportion of reinfections thus established suggests a relatively increased period of protection to renewed
NASA Technical Reports Server (NTRS)
Ramapriyan, Hampapuram; Peng, Ge; Moroni, David; Shie, Chung-Lin
2016-01-01
Quality of products is always of concern to users regardless of the type of products. The focus of this paper is on the quality of Earth science data products. There are four different aspects of quality - scientific, product, stewardship and service. All these aspects taken together constitute Information Quality. With increasing requirement on ensuring and improving information quality, there has been considerable work related to information quality during the last several years. Given this rich background of prior work, the Information Quality Cluster (IQC), established within the Federation of Earth Science Information Partners (ESIP) has been active with membership from multiple organizations. Its objectives and activities, aimed at ensuring and improving information quality for Earth science data and products, are discussed briefly.
NASA Technical Reports Server (NTRS)
Ramapriyan, H. K. (Rama); Peng, Ge; Moroni, David; Shie, Chung-Lin
2016-01-01
Quality of products is always of concern to users regardless of the type of products. The focus of this paper is on the quality of Earth science data products. There are four different aspects of quality scientific, product, stewardship and service. All these aspects taken together constitute Information Quality. With increasing requirement on ensuring and improving information quality, there has been considerable work related to information quality during the last several years. Given this rich background of prior work, the Information Quality Cluster (IQC), established within the Federation of Earth Science Information Partners (ESIP) has been active with membership from multiple organizations. Its objectives and activities, aimed at ensuring and improving information quality for Earth science data and products, are discussed briefly.
Estimating the impacts of federal efforts to improve energy efficiency: The case of buildings
LaMontagne, J; Jones, R; Nicholls, A; Shankle, S
1994-09-01
The US Department of Energy`s Office of Energy Efficiency and Renewable Energy (EE) has for more than a decade focused its efforts on research to develop new technologies for improving the efficiency of energy use and increasing the role of renewable energy; success has usually been measured in term of energy saved or displaced. Estimates of future energy savings remain an important factor in program planning and prioritization. A variety of internal and external factors are now radically changing the planning process, and in turn the composition and thrust of the EE program. The Energy Policy Act of 1992, the Framework Convention on Climate Change (and the Administration`s Climate Change Action Plan), and concerns for the future of the economy (especially employment and international competitiveness) are increasing emphasis on technology deployment and near-term results. The Reinventing Government Initiative, the Government Performance and Results Act, and the Executive Order on Environmental Justice are all forcing Federal programs to demonstrate that they are producing desired results in a cost-effective manner. The application of Total Quality management principles has increased the scope and importance of producing quantified measures of benefit. EE has established a process for estimating the benefits of DOE`s energy efficiency and renewable energy programs called ``Quality Metrics`` (QM). The ``metrics`` are: energy, employment, equity, environment, risk, economics. This paper describes the approach taken by EE`s Office of Building Technologies to prepare estimates of program benefits in terms of these metrics, presents the estimates, discusses their implications, and explores possible improvements to the QM process as it is currently configured.
Estimating the impacts of federal efforts to improve energy efficiency: The case of building
Nicolls, A.K.; Shankle, S.A.; LaMontagne, J.; Jones, R.E.
1994-11-01
The US Department of Energy`s Office of Energy Efficiency and Renewable Energy [EE] has for more than a decade focused its efforts on research to develop new technologies for improving the efficiency of energy use and increasing the role of renewable energy; success has usually been measured in terms of energy saved or displaced. Estimates of future energy savings remain an important factor in program planning and prioritization. A variety of internal and external factors are now radically changing the planning process, and in turn the composition and thrust of the EE program. The Energy Policy Act of 1992, the Framework Convention on Climate Change (and the Administration`s Climate Change Action Plan), and concerns for the future of the economy (especially employment and international competitiveness) are increasing emphasis on technology deployment and near-term results. The Reinventing Government Initiative, the Government Performance and Results Act, and the Executive Order on Environmental Justice are all forcing Federal programs to demonstrate that they are producing desired results in a cost-effective manner. The application of Total Quality Management principles has increased the scope and importance of producing quantified measures of benefit. EE has established a process for estimating the benefits of DOE`s energy efficiency and renewable energy programs called `Quality Metrics` (QM). The ``metrics`` are: Energy; Environment; Employment; Risk; Equity; Economics. This paper describes the approach taken by EE`s Office of Building Technologies to prepare estimates of program benefits in terms of these metrics, presents the estimates, discusses their implications, and explores possible improvements to the QM process as it is currently configured.
Sample, Bradley E; Schlekat, Chris; Spurgeon, David J; Menzie, Charlie; Rauscher, Jon; Adams, Bill
2014-07-01
An integral component in the development of media-specific values for the ecological risk assessment of chemicals is the derivation of safe levels of exposure for wildlife. Although the derivation and subsequent application of these values can be used for screening purposes, there is a need to identify the threshold for effects when making remedial decisions during site-specific assessments. Methods for evaluation of wildlife exposure are included in the US Environmental Protection Agency (USEPA) ecological soil screening levels (Eco-SSLs), registration, evaluation, authorization, and restriction of chemicals (REACH), and other risk-based soil assessment approaches. The goal of these approaches is to ensure that soil-associated contaminants do not pose a risk to wildlife that directly ingest soil, or to species that may be exposed to contaminants that persist in the food chain. These approaches incorporate broad assumptions in the exposure and effects assessments and in the risk characterization process. Consequently, thresholds for concluding risk are frequently very low with conclusions of risk possible when soil metal concentrations fall in the range of natural background. A workshop held in September, 2012 evaluated existing methods and explored recent science about factors to consider when establishing appropriate remedial goals for concentrations of metals in soils. A Foodweb Exposure Workgroup was organized to evaluate methods for quantifying exposure of wildlife to soil-associated metals through soil and food consumption and to provide recommendations for the development of ecological soil cleanup values (Eco-SCVs) that are both practical and scientifically defensible. The specific goals of this article are to review the current practices for quantifying exposure of wildlife to soil-associated contaminants via bioaccumulation and trophic transfer, to identify potential opportunities for refining and improving these exposure estimates, and finally, to make
NASA Technical Reports Server (NTRS)
Zhang, Qingyuan; Cheng, Yen-Ben; Lyapustin, Alexei I.; Wang, Yujie; Zhang, Xiaoyang; Suyker, Andrew; Verma, Shashi; Shuai, Yanmin; Middleton, Elizabeth M.
2015-01-01
Satellite remote sensing estimates of Gross Primary Production (GPP) have routinely been made using spectral Vegetation Indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVIgreen), and the green band Chlorophyll Index (CIgreen) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVIgreen, or CIgreen). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X-Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPARchl) and the VIs, and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPARchl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R2), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions. The scaled CIgreen did not improve results, compared to the original CIgreen. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the
Westgate, Philip M
2016-01-01
When generalized estimating equations (GEE) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator.
Westgate, Philip M.
2016-01-01
When generalized estimating equations (GEE) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator. PMID:27818539
CP40: Improved Estimation of the Thermal Noise in the SAMOSA retracker.
NASA Astrophysics Data System (ADS)
Martin, Francisco; Repucci, Antonio; Egido, Alejandro; Cotton, David; Beneviste, Jerome
2016-04-01
Estimation of the thermal noise is a key parameter in the retracking of the SAR altimeter waveforms, since it affects directly the estimation of the Significant Wave Height (SWH). Originally the noise level was obtained as the average value of the early part of the waveform prior to the leading edge, l typically gates 11-21 for a waveform with 128 gates. However, this fixed approach does not consider the impact that the SWH can have on the leading edge and on the amplitude of the averaged SAR waveform. In fact, the range position of the first gate of the leading edge, depends on the SWH, and can vary considerably. Thus using the "fixed-gate" (11-21) approach can lead on erroneous noise floor estimation. In the framework of the CP40 project, an empirical model was proposed for the computation of the thermal noise, attending to the leading edge position variability, as part of an modified implementation of the SAMOSA re-tracker. However, analysis of a test data set generated with this modified re-tracker, revealed that further optimisation of this model was need . In this work the authors presents an improved estimation of the Thermal Noise, where an approach based on the uncorrelated characteristics of the thermal noise have been used as supplementary tool. Data from Cryosat-2 CNES-CPP L1b (v14) have been used as input to evaluate this optimised version of the thermal noise estimation. The analysis has been focused on the area where in situ data (wave buoy data) are available (30°-65°N and 20°-0° W). Main results, as well the main characteristics of the approach followed, will be presented. .
Improved estimates of upper-ocean warming and multi-decadal sea-level rise.
Domingues, Catia M; Church, John A; White, Neil J; Gleckler, Peter J; Wijffels, Susan E; Barker, Paul M; Dunn, Jeff R
2008-06-19
Changes in the climate system's energy budget are predominantly revealed in ocean temperatures and the associated thermal expansion contribution to sea-level rise. Climate models, however, do not reproduce the large decadal variability in globally averaged ocean heat content inferred from the sparse observational database, even when volcanic and other variable climate forcings are included. The sum of the observed contributions has also not adequately explained the overall multi-decadal rise. Here we report improved estimates of near-global ocean heat content and thermal expansion for the upper 300 m and 700 m of the ocean for 1950-2003, using statistical techniques that allow for sparse data coverage and applying recent corrections to reduce systematic biases in the most common ocean temperature observations. Our ocean warming and thermal expansion trends for 1961-2003 are about 50 per cent larger than earlier estimates but about 40 per cent smaller for 1993-2003, which is consistent with the recognition that previously estimated rates for the 1990s had a positive bias as a result of instrumental errors. On average, the decadal variability of the climate models with volcanic forcing now agrees approximately with the observations, but the modelled multi-decadal trends are smaller than observed. We add our observational estimate of upper-ocean thermal expansion to other contributions to sea-level rise and find that the sum of contributions from 1961 to 2003 is about 1.5 +/- 0.4 mm yr(-1), in good agreement with our updated estimate of near-global mean sea-level rise (using techniques established in earlier studies) of 1.6 +/- 0.2 mm yr(-1).
Improved algorithm for the transmittance estimation of spectra obtained with SOIR/Venus Express.
Trompet, Loic; Mahieux, Arnaud; Ristic, Bojan; Robert, Séverine; Wilquet, Valérie; Thomas, Ian R; Vandaele, Ann Carine; Bertaux, Jean-Loup
2016-11-10
The Solar Occultation in the InfraRed (SOIR) instrument onboard the ESA Venus Express spacecraft, an infrared spectrometer sensitive from 2.2 to 4.3 μm, probed the atmosphere of Venus from June 2006 until December 2014. During this time, it performed more than 750 solar occultations of the Venus mesosphere and lower thermosphere. A new procedure has been developed for the estimation of the transmittance in order to decrease the number of rejected spectra, to check that the treated spectra are well calibrated, and to improve the quality of the calibrated spectra by reducing the noise and accurately normalizing it to the solar spectrum.
Improvement of the quality of effective dose estimation by interlaboratory comparisons
NASA Astrophysics Data System (ADS)
Katarzyna, Ciszewska; Malgorzata, Dymecka; Tomasz, Pliszczynski; Jakub, Osko
2010-01-01
Radiation Protection Measurements Laboratory (RPLM) of the Institute of Atomic Energy POLATOM determines radionuclides in human urine to estimate the effective dose. Being an accredited laboratory, RPLM participated in interlaboratory comparisons in order to assure the quality of services concerning monitoring of internal contamination. The purpose of the study was to examine the effect of interlaboratory comparisons on the accuracy of the provided measurements. The results regarding tritium (3H) and strontium (90Sr) determination, obtained within the radiotoxicological intercomparison exercises, organized by PROCORAD, in 2005-2010, were analyzed and the methods used by the laboratory were verified and improved.
Coburn, T.C.; Freeman, P.A.; Attanasi, E.D.
2012-01-01
The primary objectives of this research were to (1) investigate empirical methods for establishing regional trends in unconventional gas resources as exhibited by historical production data and (2) determine whether or not incorporating additional knowledge of a regional trend in a suite of previously established local nonparametric resource prediction algorithms influences assessment results. Three different trend detection methods were applied to publicly available production data (well EUR aggregated to 80-acre cells) from the Devonian Antrim Shale gas play in the Michigan Basin. This effort led to the identification of a southeast-northwest trend in cell EUR values across the play that, in a very general sense, conforms to the primary fracture and structural orientations of the province. However, including this trend in the resource prediction algorithms did not lead to improved results. Further analysis indicated the existence of clustering among cell EUR values that likely dampens the contribution of the regional trend. The reason for the clustering, a somewhat unexpected result, is not completely understood, although the geological literature provides some possible explanations. With appropriate data, a better understanding of this clustering phenomenon may lead to important information about the factors and their interactions that control Antrim Shale gas production, which may, in turn, help establish a more general protocol for better estimating resources in this and other shale gas plays. ?? 2011 International Association for Mathematical Geology (outside the USA).
NASA Astrophysics Data System (ADS)
Rafieeinasab, Arezoo; Norouzi, Amir; Seo, Dong-Jun; Nelson, Brian
2015-12-01
For monitoring and prediction of water-related hazards in urban areas such as flash flooding, high-resolution hydrologic and hydraulic modeling is necessary. Because of large sensitivity and scale dependence of rainfall-runoff models to errors in quantitative precipitation estimates (QPE), it is very important that the accuracy of QPE be improved in high-resolution hydrologic modeling to the greatest extent possible. With the availability of multiple radar-based precipitation products in many areas, one may now consider fusing them to produce more accurate high-resolution QPE for a wide spectrum of applications. In this work, we formulate and comparatively evaluate four relatively simple procedures for such fusion based on Fisher estimation and its conditional bias-penalized variant: Direct Estimation (DE), Bias Correction (BC), Reduced-Dimension Bias Correction (RBC) and Simple Estimation (SE). They are applied to fuse the Multisensor Precipitation Estimator (MPE) and radar-only Next Generation QPE (Q2) products at the 15-min 1-km resolution (Experiment 1), and the MPE and Collaborative Adaptive Sensing of the Atmosphere (CASA) QPE products at the 15-min 500-m resolution (Experiment 2). The resulting fused estimates are evaluated using the 15-min rain gauge observations from the City of Grand Prairie in the Dallas-Fort Worth Metroplex (DFW) in north Texas. The main criterion used for evaluation is that the fused QPE improves over the ingredient QPEs at their native spatial resolutions, and that, at the higher resolution, the fused QPE improves not only over the ingredient higher-resolution QPE but also over the ingredient lower-resolution QPE trivially disaggregated using the ingredient high-resolution QPE. All four procedures assume that the ingredient QPEs are unbiased, which is not likely to hold true in reality even if real-time bias correction is in operation. To test robustness under more realistic conditions, the fusion procedures were evaluated with and
2013-01-01
Background Over 20% of hospital bed use is inappropriate, implying a waste of resources and the increase of patient iatrogenic risk. Methods This is a cluster, pragmatic, randomised controlled trial, carried out in a large University Hospital of Northern Italy, aiming to evaluate the effect of a strategy to reduce unnecessary hospital days. The primary outcome was the percentage of patient-days compatible with discharge. Among secondary objectives, to describe the strategy’s effect in the long-term, as well as on hospital readmissions, considered to be a marker of the quality of hospital care. The 12 medical wards with the longest length of stay participated. Effectiveness was measured at the individual level on 3498 eligible patients during monthly index days. Patients admitted or discharged on index days, or with stay >90 days, were excluded. All ward staff was blinded to the index days, while staff in the control arm and data analysts were blinded to the trial’s objectives and interventions. The strategy comprised the distribution to physicians of the list of their patients whose hospital stay was compatible with discharge according to a validated Delay Tool, and of physician length of stay profiles, followed by audits managed autonomously by the physicians of the ward. Results During the 12 months of data collection, over 50% of patient-days were judged to be compatible with discharge. Delays were mainly due to problems with activities under medical staff control. Multivariate analysis considering clustering showed that the strategy reduced patient-days compatible with discharge by 16% in the intervention vs control group, (OR=0.841; 95% CI, 0.735 to 0.963; P=0.012). Follow-up at 1 year did not yield a statistically significant difference between the percentages of patient-days judged to be compatible with discharge between the two arms (OR=0.818; 95% CI, 0.476 to 1.405; P=0.47). There was no significant difference in 30-day readmission and mortality rates
Impact of regression methods on improved effects of soil structure on soil water retention estimates
NASA Astrophysics Data System (ADS)
Nguyen, Phuong Minh; De Pue, Jan; Le, Khoa Van; Cornelis, Wim
2015-06-01
Increasing the accuracy of pedotransfer functions (PTFs), an indirect method for predicting non-readily available soil features such as soil water retention characteristics (SWRC), is of crucial importance for large scale agro-hydrological modeling. Adding significant predictors (i.e., soil structure), and implementing more flexible regression algorithms are among the main strategies of PTFs improvement. The aim of this study was to investigate whether the improved effect of categorical soil structure information on estimating soil-water content at various matric potentials, which has been reported in literature, could be enduringly captured by regression techniques other than the usually applied linear regression. Two data mining techniques, i.e., Support Vector Machines (SVM), and k-Nearest Neighbors (kNN), which have been recently introduced as promising tools for PTF development, were utilized to test if the incorporation of soil structure will improve PTF's accuracy under a context of rather limited training data. The results show that incorporating descriptive soil structure information, i.e., massive, structured and structureless, as grouping criterion can improve the accuracy of PTFs derived by SVM approach in the range of matric potential of -6 to -33 kPa (average RMSE decreased up to 0.005 m3 m-3 after grouping, depending on matric potentials). The improvement was primarily attributed to the outperformance of SVM-PTFs calibrated on structureless soils. No improvement was obtained with kNN technique, at least not in our study in which the data set became limited in size after grouping. Since there is an impact of regression techniques on the improved effect of incorporating qualitative soil structure information, selecting a proper technique will help to maximize the combined influence of flexible regression algorithms and soil structure information on PTF accuracy.
Worm-improved estimators in continuous-time quantum Monte Carlo
NASA Astrophysics Data System (ADS)
Gunacker, P.; Wallerberger, M.; Ribic, T.; Hausoel, A.; Sangiovanni, G.; Held, K.
2016-09-01
We derive the improved estimators for general interactions and employ these for the continuous-time quantum Monte Carlo method. Using a worm algorithm we show how measuring higher-ordered correlators leads to an improved high-frequency behavior in irreducible quantities such as the one-particle self-energy or the irreducible two-particle vertex for non-density-density interactions. A good knowledge of the asymptotics of the two-particle vertex is essential for calculating nonlocal electronic correlations using diagrammatic extensions to the dynamical mean field theory as well as for calculating susceptibilities. We test our algorithm against analytic results for the multiorbital atomic limit and the Falicov-Kimball model.
Wang, Jun; Zhou, Bihua; Zhou, Shudao
2016-01-01
This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior. PMID:26880874
Wang, Jun; Zhou, Bihua; Zhou, Shudao
2016-01-01
This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior.
Improvement and quantitative performance estimation of the back support muscle suit.
Muramatsu, Y; Umehara, H; Kobayashi, H
2013-01-01
We have been developing the wearable muscle suit for direct and physical motion supports. The use of the McKibben artificial muscle has opened the way to the introduction of "muscle suits" compact, lightweight, reliable, wearable "assist-bots" enabling manual worker to lift and carry weights. Since back pain is the most serious problem for manual worker, improvement of the back support muscle suit under the feasibility study and quantitative estimation are shown in this paper. The structure of the upper body frame, the method to attach to the body, and the axes addition were explained as for the improvement. In the experiments, we investigated quantitative performance results and efficiency of the back support muscle suit in terms of vertical lifting of heavy weights by employing integral electromyography (IEMG). The results indicated that the values of IEMG were reduced by about 40% by using the muscle suit.
Kinetic Estimation of GFR Improves Prediction of Dialysis and Recovery after Kidney Transplantation
Pianta, Timothy J.; Endre, Zoltan H.; Pickering, John W.; Buckley, Nicholas A.; Peake, Philip W.
2015-01-01
Background The early prediction of delayed graft function (DGF) would facilitate patient management after kidney transplantation. Methods In a single-centre retrospective analysis, we investigated kinetic estimated GFR under non-steady-state conditions, KeGFR, in prediction of DGF. KeGFRsCr was calculated at 4h, 8h and 12h in 56 recipients of deceased donor kidneys from initial serum creatinine (sCr) concentrations, estimated creatinine production rate, volume of distribution, and the difference between consecutive sCr values. The utility of KeGFRsCr for DGF prediction was compared with, sCr, plasma cystatin C (pCysC), and KeGFRpCysC similarly derived from pCysC concentrations. Results At 4h, the KeGFRsCr area under the receiver operator characteristic curve (AUC) for DGF prediction was 0.69 (95% CI: 0.56–0.83), while sCr was not useful (AUC 0.56, (CI: 0.41–0.72). Integrated discrimination improvement analysis showed that the KeGFRsCr improved a validated clinical prediction model at 4h, 8h, and 12h, increasing the AUC from 0.68 (0.52–0.83) to 0.88 (0.78–0.99) at 12h (p = 0.01). KeGFRpCysC also improved DGF prediction. In contrast, sCr provided no improvement at any time point. Conclusions Calculation of KeGFR from sCr facilitates early prediction of DGF within 4 hours of renal transplantation. PMID:25938452
Improved Atmospheric Soundings and Error Estimates from Analysis of AIRS/AMSU Data
NASA Technical Reports Server (NTRS)
Susskind, Joel
2007-01-01
The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Three very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control; and 3) development of an accurate AIRS only cloud clearing and retrieval system. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions, without the need for microwave observations in the cloud clearing step as has been done previously. In this methodology, longwave C02 channel observations in the spectral region 700 cm-' to 750 cm-' are used exclusively for cloud clearing purposes, while shortwave C02 channels in the spectral region 2195 cm-' to 2395 cm-' are used for temperature sounding purposes. The new methodology for improved error estimates and their use in quality control is described briefly and results are shown indicative of their accuracy. Results are also shown of forecast impact experiments assimilating AIRS Version 5.0 retrieval products in the Goddard GEOS 5 Data Assimilation System using different quality control thresholds.
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.
State Estimation and Forecasting of the Ski-Slope Model Using an Improved Shadowing Filter
NASA Astrophysics Data System (ADS)
Mat Daud, Auni Aslah
In this paper, we present the application of the gradient descent of indeterminism (GDI) shadowing filter to a chaotic system, that is the ski-slope model. The paper focuses on the quality of the estimated states and their usability for forecasting. One main problem is that the existing GDI shadowing filter fails to provide stability to the convergence of the root mean square error and the last point error of the ski-slope model. Furthermore, there are unexpected cases in which the better state estimates give worse forecasts than the worse state estimates. We investigate these unexpected cases in particular and show how the presence of the humps contributes to them. However, the results show that the GDI shadowing filter can successfully be applied to the ski-slope model with only slight modification, that is, by introducing the adaptive step-size to ensure the convergence of indeterminism. We investigate its advantages over fixed step-size and how it can improve the performance of our shadowing filter.
Improved W-band Doppler Radar Spectrum Width Estimates during the VOCALS 2008 Cruise
NASA Astrophysics Data System (ADS)
Williams, C. R.; Fairall, C. W.; Moran, K.
2011-12-01
During the VOCALS 2008 cruise, a NOAA W-band (94 GHz) radar was mounted on a stabilized platform and pointed vertically to observe the marine boundary layer cloud structure. Occasionally during drizzle events, while the reflectivity was consistent with time and height, the estimated spectrum width had a few randomly distributed large values. During the post-cruise processing, it was determined that the large spectrum widths occurred when the recorded Doppler velocity spectra contained multiple peaks. The multiple peaks were symmetric about the dominant peak and were at least 30 dB smaller than the dominant peak. The few randomly distributed large spectrum widths were due to the real-time spectra processing routine estimating the spectrum moments across multiple peaks. Since the secondary peaks are so small relative to the dominant peak, there was very little change in the estimated reflectivity as the spectrum width varied from observation to observation. This presentation will describe how a multi-peak picking routine was used to identify the dominant peak in the Doppler velocity reflectivity spectra and how the spectrum moments were recalculated using just the dominant peak. Examples of the multiple peaks will be shown to clarify that the multiple peaks were not due to multi-peaked drizzle droplet size distributions and not due to Mie scattering effects. Improvements in the spectral moments during drizzle will be shown and will show consistent spectrum widths with time and height during drizzle.
Improved regression models for ventilation estimation based on chest and abdomen movements
Liu, Shaopeng; Gao, Robert; He, Qingbo; Staudenmayer, John; Freedson, Patty
2012-01-01
Non-invasive estimation of minute ventilation is important for quantifying the intensity of physical activity of individuals. In this paper, several improved regression models are presented, based on the measurement of chest and abdomen movements from sensor belts worn by subjects (n = 50) engaged in 14 types of physical activities. Five linear models involving a combination of 11 features were developed, and the effects of different model training approaches and window sizes for computing the features were investigated. The performance of the models was evaluated using experimental data collected during the physical activity protocol. The predicted minute ventilation was compared to the criterion ventilation measured using a bidirectional digital volume transducer housed in a respiratory gas exchange system. The results indicate that the inclusion of breathing frequency and the use of percentile points instead of interdecile ranges over a 60-second window size reduced error by about 43%, when applied to the classical two degrees-of-freedom model. The mean percentage error of the minute ventilation estimated for all the activities was below 7.5%, verifying reasonably good performance of the models and the applicability of the wearable sensing system for minute ventilation estimation during physical activity. PMID:22173273
ERIC Educational Resources Information Center
Kolstad, Andrew
Use of clustering methods with data from the 1987 High School Transcript (HST) component of the 1986 National Assessment of Educational Progress (NAEP) is discussed in an attempt to persuade data analysts and researchers that they should be interested in problems traditionally turned over to samplers. Nearly all surveys used by the National Center…
ERIC Educational Resources Information Center
Bauer, Daniel J.
2009-01-01
When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for…
Estimation of contrast agent bolus arrival delays for improved reproducibility of liver DCE MRI
NASA Astrophysics Data System (ADS)
Chouhan, Manil D.; Bainbridge, Alan; Atkinson, David; Punwani, Shonit; Mookerjee, Rajeshwar P.; Lythgoe, Mark F.; Taylor, Stuart A.
2016-10-01
Delays between contrast agent (CA) arrival at the site of vascular input function (VIF) sampling and the tissue of interest affect dynamic contrast enhanced (DCE) MRI pharmacokinetic modelling. We investigate effects of altering VIF CA bolus arrival delays on liver DCE MRI perfusion parameters, propose an alternative approach to estimating delays and evaluate reproducibility. Thirteen healthy volunteers (28.7 ± 1.9 years, seven males) underwent liver DCE MRI using dual-input single compartment modelling, with reproducibility (n = 9) measured at 7 days. Effects of VIF CA bolus arrival delays were assessed for arterial and portal venous input functions. Delays were pre-estimated using linear regression, with restricted free modelling around the pre-estimated delay. Perfusion parameters and 7 days reproducibility were compared using this method, freely modelled delays and no delays using one-way ANOVA. Reproducibility was assessed using Bland-Altman analysis of agreement. Maximum percent change relative to parameters obtained using zero delays, were -31% for portal venous (PV) perfusion, +43% for total liver blood flow (TLBF), +3247% for hepatic arterial (HA) fraction, +150% for mean transit time and -10% for distribution volume. Differences were demonstrated between the 3 methods for PV perfusion (p = 0.0085) and HA fraction (p < 0.0001), but not other parameters. Improved mean differences and Bland-Altman 95% Limits-of-Agreement for reproducibility of PV perfusion (9.3 ml/min/100 g, ±506.1 ml/min/100 g) and TLBF (43.8 ml/min/100 g, ±586.7 ml/min/100 g) were demonstrated using pre-estimated delays with constrained free modelling. CA bolus arrival delays cause profound differences in liver DCE MRI quantification. Pre-estimation of delays with constrained free modelling improved 7 days reproducibility of perfusion parameters in volunteers.
NASA Astrophysics Data System (ADS)
Ge, Xinmin; Fan, Yiren; Deng, Shaogui; Han, Yujiao; Liu, Jiaxiong
2016-09-01
We present an improved fractal model for pore structure evaluation and permeability estimation based on the high pressure mercury porosimetry data. An accumulative fractal equation is introduced to characterize the piecewise nature of the capillary pressure and the mercury saturation. The iterative truncated singular value decomposition algorithm is developed to solve the accumulative fractal equation and obtain the fractal dimension distributions. Furthermore, the fractal dimension distributions and relevant parameters are used to characterize the pore structure and permeability. The results demonstrate that the proposed model provides better characterization of the mercury injection capillary pressure than conventional monofractal theory. In addition, there is a direct relationship between the pore structure types and the fractal dimension spectrums. What is more, the permeability is strongly correlated with the geometric and the arithmetic mean values of fractal dimensions, and the permeability estimated using these new fractal dimension parameters achieve excellent result. The improved model and solution give a fresh perspective of the conventional monofractal theory, which may be applied in many geological and geophysical fields.
Using dark current data to estimate AVIRIS noise covariance and improve spectral analyses
NASA Technical Reports Server (NTRS)
Boardman, Joseph W.
1995-01-01
Starting in 1994, all AVIRIS data distributions include a new product useful for quantification and modeling of the noise in the reported radiance data. The 'postcal' file contains approximately 100 lines of dark current data collected at the end of each data acquisition run. In essence this is a regular spectral-image cube, with 614 samples, 100 lines and 224 channels, collected with a closed shutter. Since there is no incident radiance signal, the recorded DN measure only the DC signal level and the noise in the system. Similar dark current measurements, made at the end of each line are used, with a 100 line moving average, to remove the DC signal offset. Therefore, the pixel-by-pixel fluctuations about the mean of this dark current image provide an excellent model for the additive noise that is present in AVIRIS reported radiance data. The 61,400 dark current spectra can be used to calculate the noise levels in each channel and the noise covariance matrix. Both of these noise parameters should be used to improve spectral processing techniques. Some processing techniques, such as spectral curve fitting, will benefit from a robust estimate of the channel-dependent noise levels. Other techniques, such as automated unmixing and classification, will be improved by the stable and scene-independence noise covariance estimate. Future imaging spectrometry systems should have a similar ability to record dark current data, permitting this noise characterization and modeling.
Coupling NLDAS Model Output with MODIS Products for Improved Spatial Evapotranspiration Estimates
NASA Astrophysics Data System (ADS)
Kim, J.; Hogue, T.
2008-12-01
Given the growing concern over regional water supplies in much of the arid west, the quantification of water use by urban and agricultural landscapes is critically important. Water lost through evapotranspiration (ET) typically can not be recaptured or recycled, increasing the need for accurate accounting of ET in regional water management and planning. In this study, we investigate a method to better capture the spatial characteristics of ET by coupling operational North American Land Data Assimilation System (NLDAS) Noah Land Surface Model (LSM) outputs and a previously developed MODIS-based Potential Evapotranspiration (PET) product. The resultant product is higher resolution (1km) than the NLDAS model ET outputs (~12.5 km) and provides improved estimates within highly heterogeneous terrain and landscapes. We undertake this study in the Southern California region which provides an excellent case study for examining the developed product's ability to estimate vegetation dynamics over rapidly growing, and highly-irrigated, urban ecosystems. General trends in both products are similar; however the coupled MODIS-NLDAS ET product shows higher spatial variability, better capturing land surface heterogeneity than the NLDAS-based ET. Improved ET representation is especially obvious during the spring season, when precipitation is muted and evaporative flux is dominant. We also quantify seasonal landscape water demand over urban landscapes in several major counties (i.e. Los Angeles, San Diego and Riverside) using the MODIS-NLDAS ET model.
Improved estimation of anomalous diffusion exponents in single-particle tracking experiments
NASA Astrophysics Data System (ADS)
Kepten, Eldad; Bronshtein, Irena; Garini, Yuval
2013-05-01
The mean square displacement is a central tool in the analysis of single-particle tracking experiments, shedding light on various biophysical phenomena. Frequently, parameters are extracted by performing time averages on single-particle trajectories followed by ensemble averaging. This procedure, however, suffers from two systematic errors when applied to particles that perform anomalous diffusion. The first is significant at short-time lags and is induced by measurement errors. The second arises from the natural heterogeneity in biophysical systems. We show how to estimate and correct these two errors and improve the estimation of the anomalous parameters for the whole particle distribution. As a consequence, we manage to characterize ensembles of heterogeneous particles even for rather short and noisy measurements where regular time-averaged mean square displacement analysis fails. We apply this method to both simulations and in vivo measurements of telomere diffusion in 3T3 mouse embryonic fibroblast cells. The motion of telomeres is found to be subdiffusive with an average exponent constant in time. Individual telomere exponents are normally distributed around the average exponent. The proposed methodology has the potential to improve experimental accuracy while maintaining lower experimental costs and complexity.
Aggarwal, Ankush
2017-03-01
Motivated by the well-known result that stiffness of soft tissue is proportional to the stress, many of the constitutive laws for soft tissues contain an exponential function. In this work, we analyze properties of the exponential function and how it affects the estimation and comparison of elastic parameters for soft tissues. In particular, we find that as a consequence of the exponential function there are lines of high covariance in the elastic parameter space. As a result, one can have widely varying mechanical parameters defining the tissue stiffness but similar effective stress-strain responses. Drawing from elementary algebra, we propose simple changes in the norm and the parameter space, which significantly improve the convergence of parameter estimation and robustness in the presence of noise. More importantly, we demonstrate that these changes improve the conditioning of the problem and provide a more robust solution in the case of heterogeneous material by reducing the chances of getting trapped in a local minima. Based upon the new insight, we also propose a transformed parameter space which will allow for rational parameter comparison and avoid misleading conclusions regarding soft tissue mechanics.
Improving Global Surface Mass Variation Estimates With Multi-Satellite Data Combination
NASA Astrophysics Data System (ADS)
Wu, X.; Blom, R. G.; Dong, D.; Ivins, E. R.; Owen, S. E.; Oyafuso, F. A.
2006-05-01
The GRACE gravity mission is an important milestone toward global high resolution and accurate monitoring of surface mass variations. The spherical harmonic spectra of the variations, however, are not complete without the degree-1 (or equivalently geocenter motion) terms. Also, SLR geocenter motion solutions severely under- sample the center-of-figure of the solid Earth surface due to its small tracking network. To validate and complement gravity data, we compare and combine them with GPS crustal deformation measurements and a TOPEX/JASON data-assimilated ocean bottom pressure (OBP) model to solve for surface mass variations up to degree and order 50, with reduced aid of a priori information. The solutions include geocenter motion estimates with < 0.5 mm annual precision. To further reduce a priori model dependency and improve surface mass variation accuracy, the supplemented GRACE data (plus degree-1 terms) are combined with OBP and GPS data without a priori model. The resulting average oceanic and Antarctic mass variation estimates show nearly a factor of 2 improvements over those derived from the supplemented GRACE solution alone.
Alexander, Kelly T; Dreibelbis, Robert; Freeman, Matthew C; Ojeny, Betty; Rheingans, Richard
2013-09-01
Water, sanitation, and hygiene (WASH) programs in schools have been shown to improve health and reduce absence. In resource-poor settings, barriers such as inadequate budgets, lack of oversight, and competing priorities limit effective and sustained WASH service delivery in schools. We employed a cluster-randomized trial to examine if schools could improve WASH conditions within existing administrative structures. Seventy schools were divided into a control group and three intervention groups. All intervention schools received a budget for purchasing WASH-related items. One group received no further intervention. A second group received additional funding for hiring a WASH attendant and making repairs to WASH infrastructure, and a third group was given guides for student and community monitoring of conditions. Intervention schools made significant improvements in provision of soap and handwashing water, treated drinking water, and clean latrines compared with controls. Teachers reported benefits of monitoring, repairs, and a WASH attendant, but quantitative data of WASH conditions did not determine whether expanded interventions out-performed our budget-only intervention. Providing schools with budgets for WASH operational costs improved access to necessary supplies, but did not ensure consistent service delivery to students. Further work is needed to clarify how schools can provide WASH services daily.
NASA Astrophysics Data System (ADS)
Zhang, W.; Haase, J. S.; Hertzog, A.; Lou, Y.; Vincent, R. A.
2015-12-01
Gravity waves (GWs) play an important role in transferring energy and momentum from the troposphere to the middle atmosphere. However, shorter period GWs are generally not explicitly resolved in general circulation models but need to be parameterized instead. Super pressure balloons, which float on the isopycnal surfaces, provide a direct access to measure GW characteristics as a function of wave intrinsic frequency that are needed for these parameterizations. The 30 s sampling rate of the GPS receivers carried on the balloons deployed in 2010 Concordiasi campaign in the Antarctic region is much higher compared to the previous campaigns and can cover the full range of the GW spectrum. Two among 19 balloons in the Concordiasi campaign are also equipped with the high-accuracy dual-frequency GPS receivers initially developed for GPS radio occultation research in addition to the regular single-frequency receivers, which enables us to expect a better accuracy of balloon positions for the purpose of GW momentum flux estimates. The positions are estimated using the Precise Point Positioning with Ambiguity Resolution (PPPAR) method based on the GPS data. Improvements of the positions are significant, from ~3-10 m to ~0.1-0.2 m in 3-D positions, which makes it possible to resolve the Eulerian pressure independently of height for the estimation of the intrinsic phase speed. The impacts of the position improvements on the final GW parameters (momentum flux and intrinsic phase speed) retrievals are highlighted, with ~0.54 mPa difference of the mean absolute momentum flux in Antarctic region and considerable difference in the distribution of the intrinsic phase speed.
NASA Astrophysics Data System (ADS)
Hazenberg, Pieter; Leijnse, Hidde; Uijlenhoet, Remko
2016-04-01
Weather radars provide information on the characteristics of precipitation at high spatial and temporal resolution. Unfortunately, rainfall measurements by radar are affected by multiple error sources. The current study is focused on the impact of variations of the raindrop size distribution on radar rainfall estimates. Such variations lead to errors in the estimated rainfall intensity (R) and specific attenuation (k) when using fixed relations for the conversion of the observed reflectivity (Z) into R and k. For non-polarimetric radar, this error source has received relatively little attention compared to other error sources. We propose to link the parameters of the Z-R and Z-k relations directly to those of the normalized gamma DSD. The benefit of this procedure is that it reduces the number of unknown parameters. In this work, the DSD parameters are obtained using 1) surface observations from a Parsivel and Thies LPM disdrometer, and 2) a Monte Carlo optimization procedure using surface rain gauge observations. The impact of both approaches for a given precipitation type is assessed for 45 days of summertime precipitation observed in The Netherlands. Accounting for DSD variations using disdrometer observations leads to an improved radar QPE product as compared to applying climatological Z-R and Z-k relations. This especially holds for situations where widespread stratiform precipitation is observed. The best results are obtained when the DSD parameters are optimized. However, the optimized Z-R and Z-k relations show an unrealistic variability that arises from uncorrected error sources. As such, the optimization approach does not result in a realistic DSD shape but instead also accounts for uncorrected error sources resulting in the best radar rainfall adjustment. Therefore, to further improve the quality of preciptitation estimates by weather radar, usage should either be made of polarimetric radar or by extending the network of disdrometers.
Strategies for Improving Power in Cluster Randomized Studies of Professional Development
ERIC Educational Resources Information Center
Kelcey, Ben; Spybrook, Jessaca; Zhang, Jiaqi; Phelps, Geoffrey; Jones, Nathan
2015-01-01
With research indicating substantial differences among teachers in terms of their effectiveness (Nye, Konstantopoulous, & Hedges, 2004), a major focus of recent research in education has been on improving teacher quality through professional development (Desimone, 2009; Institute of Educations Sciences [IES], 2012; Measures of Effective…
Improvements to TOVS retrievals over sea ice and applications to estimating Arctic energy fluxes
NASA Technical Reports Server (NTRS)
Francis, Jennifer A.
1994-01-01
Modeling studies suggest that polar regions play a major role in modulating the Earth's climate and that they may be more sensitive than lower latitudes to climate change. Until recently, however, data from meteorological stations poleward of 70 degs have been sparse, and consequently, our understanding of air-sea-ice interaction processes is relatively poor. Satellite-borne sensors now offer a promising opportunity to observe polar regions and ultimately to improve parameterizations of energy transfer processes in climate models. This study focuses on the application of the TIROS-N operational vertical sounder (TOVS) to sea-ice-covered regions in the nonmelt season. TOVS radiances are processed with the improved initialization inversion ('3I') algorithm, providng estimates of layer-average temperature and moisture, cloud conditions, and surface characteristics at a horizontal resolution of approximately 100 km x 100 km. Although TOVS has flown continuously on polar-orbiting satellites since 1978, its potential has not been realized in high latitudes because the quality of retrievals is often significantly lower over sea ice and snow than over the surfaces. The recent availability of three Arctic data sets has provided an opportunity to validate TOVS retrievals: the first from the Coordinated Eastern Arctic Experiment (CEAREX) in winter 1988/1989, the second from the LeadEx field program in spring 1992, and the third from Russian drifting ice stations. Comparisons with these data reveal deficiencies in TOVS retrievals over sea ice during the cold season; e.g., ice surface temperature is often 5 to 15 K too warm, microwave emissivity is approximately 15% too low at large view angles, clear/cloudy scenes are sometimes misidentified, and low-level inversions are often not captured. In this study, methods to reduce these errors are investigated. Improvements to the ice surface temperature retrieval have reduced rms errors from approximately 7 K to 3 K; correction of
Impact of an improved neutrino energy estimate on outflows in neutron star merger simulations
NASA Astrophysics Data System (ADS)
Foucart, Francois; O'Connor, Evan; Roberts, Luke; Kidder, Lawrence E.; Pfeiffer, Harald P.; Scheel, Mark A.
2016-12-01
Binary neutron star mergers are promising sources of gravitational waves for ground-based detectors such as Advanced LIGO. Neutron-rich material ejected by these mergers may also be the main source of r-process elements in the Universe, while radioactive decays in the ejecta can power bright electromagnetic postmerger signals. Neutrino-matter interactions play a critical role in the evolution of the composition of the ejected material, which significantly impacts the outcome of nucleosynthesis and the properties of the associated electromagnetic signal. In this work, we present a simulation of a binary neutron star merger using an improved method for estimating the average neutrino energies in our energy-integrated neutrino transport scheme. These energy estimates are obtained by evolving the neutrino number density in addition to the neutrino energy and flux densities. We show that significant changes are observed in the composition of the polar ejecta when comparing our new results with earlier simulations in which the neutrino spectrum was assumed to be the same everywhere in optically thin regions. In particular, we find that material ejected in the polar regions is less neutron rich than previously estimated. Our new estimates of the composition of the polar ejecta make it more likely that the color and time scale of the electromagnetic signal depend on the orientation of the binary with respect to an observer's line of sight. These results also indicate that important observable properties of neutron star mergers are sensitive to the neutrino energy spectrum, and may need to be studied through simulations including a more accurate, energy-dependent neutrino transport scheme.
Hunter, Margaret E.; Oyler-McCance, Sara J.; Dorazio, Robert M.; Fike, Jennifer A.; Smith, Brian J.; Hunter, Charles T.; Reed, Robert N.; Hart, Kristen M.
2015-01-01
Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors
NASA Astrophysics Data System (ADS)
St. Pé, Alexandra; Wesloh, Daniel; Antoszewski, Graham; Daham, Farrah; Goudarzi, Navid; Rabenhorst, Scott; Delgado, Ruben
2016-06-01
There is enormous potential to harness the kinetic energy of offshore wind and produce power. However significant uncertainties are introduced in the offshore wind resource assessment process, due in part to limited observational networks and a poor understanding of the marine atmosphere's complexity. Given the cubic relationship between a turbine's power output and wind speed, a relatively small error in the wind speed estimate translates to a significant error in expected power production. The University of Maryland Baltimore County (UMBC) collected in-situ measurements offshore, within Maryland's Wind Energy Area (WEA) from July-August 2013. This research demonstrates the ability of Doppler wind lidar technology to reduce uncertainty in estimating an offshore wind resource, compared to traditional resource assessment techniques, by providing a more accurate representation of the wind profile and associated hub-height wind speed variability. The second objective of this research is to elucidate the impact of offshore micrometeorology controls (stability, wind shear, turbulence) on a turbine's ability to produce power. Compared to lidar measurements, power law extrapolation estimates and operational National Weather Service models underestimated hub-height wind speeds in the WEA. In addition, lidar observations suggest the frequent development of a low-level wind maximum (LLWM), with high turbinelayer wind shear and low turbulence intensity within a turbine's rotor layer (40m-160m). Results elucidate the advantages of using Doppler wind lidar technology to improve offshore wind resource estimates and its ability to monitor under-sampled offshore meteorological controls impact on a potential turbine's ability to produce power.
Hunter, Margaret E.; Oyler-McCance, Sara J.; Dorazio, Robert M.; Fike, Jennifer A.; Smith, Brian J.; Hunter, Charles T.; Reed, Robert N.; Hart, Kristen M.
2015-01-01
Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors
NASA Astrophysics Data System (ADS)
Minjarez-Sosa, Carlos Manuel
Thunderstorms that occur in areas of complex terrain are a major severe weather hazard in the intermountain western U.S. Short-term quantitative estimation (QPE) of precipitation in complex terrain is a pressing need to better forecast flash flooding. Currently available techniques for QPE, that utilize a combination of rain gauge and weather radar information, may underestimate precipitation in areas where gauges do not exist or there is radar beam blockage. These are typically very mountainous and remote areas, that are quite vulnerable to flash flooding because of the steep topography. Lightning has been one of the novel ways suggested by the scientific community as an alternative to estimate precipitation over regions that experience convective precipitation, especially those continental areas with complex topography where the precipitation sensor measurements are scarce. This dissertation investigates the relationship between cloud-to-ground lightning and precipitation associated with convection with the purpose of estimating precipitation- mainly over areas of complex terrain which have precipitation sensor coverage problems (e.g. Southern Arizona). The results of this research are presented in two papers. The first, entitled Toward Development of Improved QPE in Complex Terrain Using Cloud-to-Ground Lighting Data: A case Study for the 2005 Monsoon in Southern Arizona, was published in the Journal of Hydrometeorology in December 2012. This initial study explores the relationship between cloud-to-ground lightning occurrences and multi-sensor gridded precipitation over southern Arizona. QPE is performed using a least squares approach for several time resolutions (seasonal---June, July and August---24 hourly and hourly) and for a 8 km grid size. The paper also presents problems that arise when the time resolution is increased, such as the spatial misplacing of discrete lightning events with gridded precipitation and the need to define a "diurnal day" that is
NASA Astrophysics Data System (ADS)
Henke, D.; Schubert, A.; Small, D.; Meier, E.; Lüthi, M. P.; Vieli, A.
2014-12-01
A new method for glacier surface velocity (GSV) estimates is proposed here which combines ground- and space-based measurements with hidden state space modeling (HSSM). Examples of such a fusion of physical models with remote sensing (RS) observations were described in (Henke & Meier, Hidden State Space Models for Improved Remote Sensing Applications, ITISE 2014, p. 1242-1255) and are currently adapted for GSV estimation. GSV can be estimated using in situ measurements, RS methods or numerical simulations based on ice-flow models. In situ measurements ensure high accuracy but limited coverage and time consuming field work, while RS methods offer regular observations with high spatial coverage generally not possible with in situ methods. In particular, spaceborne Synthetic Aperture Radar (SAR) can obtain useful images independent of daytime and cloud cover. A ground portable radar interferometer (GPRI) is useful for investigating a particular area in more detail than is possible from space, but provides local coverage only. Several processing methods for deriving GSV from radar sensors have been established, including interferometry and offset tracking (Schubert et al, Glacier surface velocity estimation using repeat TerraSAR-X images. ISPRS Journal of P&RS, p. 49-62, 2013). On the other hand, it is also possible to derive glacier parameters from numerical ice-flow modeling alone. Given a well-parameterized model, GSV can in theory be derived and propagated continuously in time. However, uncertainties in the glacier flow dynamics and model errors increase with excessive propagation. All of these methods have been studied independently, but attempts to combine them have only rarely been made. The HSSM we propose recursively estimates the GSV based on 1) a process model making use of temporal and spatial interdependencies between adjacent states, and 2) observations (RS and optional in situ). The in situ and GPRI images currently being processed were acquired in the
NASA Astrophysics Data System (ADS)
Balasis, G.; Egbert, G. D.
2005-12-01
Electromagnetic (EM) induction studies using satellite and ground-based magnetic data may ultimately provide critical new constraints on the electrical conductivity of Earth's mantle. Unlike ground-based observatories, which leave large areas of the Earth (especially the ocean basins) unsampled, satellites have the potential for nearly complete global coverage. However, because the number of operating satellites is limited, spatially complex (especially non-zonal) external current sources are sampled relatively poorly by satellites at any fixed time. The comparatively much larger number of ground-based observatories provides more complete synoptic sampling of external source structure. By combining data from both satellites and observatories models of external sources can be improved, leading to more reliable global mapping of Earth conductivity. For example, estimates of EM induction transfer functions estimated from night-side CHAMP data have been previously shown to have biases which depend systematically on local time (LT). This pattern of biases suggests that a purely zonal model does not adequately describe magnetospheric sources. As a first step toward improved modeling of spatial complexity in sources, we have applied empirical orthogonal function (EOF) methods to exploratory analysis of night-side observatory data. After subtraction of the predictions of the CM4 comprehensive model, which includes a zonally symmetric storm-time correction based on Dst, we find significant non-axisymmetric, but large scale coherent variability in the mid-latitude night-side observatory residuals. Over the restricted range of local times (18:00-6:00) and latitudes (50°S to 50°N) considered, the dominant spatial mode of variability is reasonably approximated by a q21 quadrupole spherical harmonic. Temporal variability of this leading EOF mode is well correlated with Dst. Strategies for moving beyond this initial exploratory EOF analysis to combine observatory data with
Lu, Dan; Zhang, Guannan; Webster, Clayton G.; ...
2016-12-30
In this paper, we develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challengemore » in estimating CDFs is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. In addition, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and practical algorithm that can be applied to a wide range of subsurface problems for high-dimensional uncertainty quantification, such as a fine-grid oil reservoir model considered in this effort. The numerical results reveal that with the use of the calibrated smoothing function, the improved MLMC technique significantly reduces the computational complexity compared to the standard MC approach. Finally, we discuss several factors that affect the performance of the MLMC method and provide guidance for effective and efficient usage in practice.« less
Lu, Dan; Zhang, Guannan; Webster, Clayton G.; Barbier, Charlotte N.
2016-12-30
In this paper, we develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challenge in estimating CDFs is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. In addition, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and practical algorithm that can be applied to a wide range of subsurface problems for high-dimensional uncertainty quantification, such as a fine-grid oil reservoir model considered in this effort. The numerical results reveal that with the use of the calibrated smoothing function, the improved MLMC technique significantly reduces the computational complexity compared to the standard MC approach. Finally, we discuss several factors that affect the performance of the MLMC method and provide guidance for effective and efficient usage in practice.
Wright, D; Gfroerer, J; Epstein, J
1997-01-01
Levels of hardcore drug use have been especially difficult to estimate because of the relative rarity of the behavior, the difficulty of locating hardcore drug users, and the tendency to underreport stigmatized behavior. This chapter presents a new application of ratio estimation, combining sample data from the National Household Survey on Drug Abuse (NHSDA) together with population counts of the number of persons arrested in the past year from the Uniform Crime Report (UCR) and the number of persons in drug treatment programs in the past year from the National Drug and Alcoholism Treatment Unit Survey (NDATUS). The population counts serve as a benchmark accounting for undercoverage and underreporting of hard drug users.
2014-01-01
Background The Portuguese National Health Directorate has issued clinical practice guidelines on prescription of anti-inflammatory drugs, acid suppressive therapy, and antiplatelets. However, their effectiveness in changing actual practice is unknown. Methods The study will compare the effectiveness of educational outreach visits regarding the improvement of compliance with clinical guidelines in primary care against usual dissemination strategies. A cost-benefit analysis will also be conducted. We will carry out a parallel, open, superiority, randomized trial directed to primary care physicians. Physicians will be recruited and allocated at a cluster-level (primary care unit) by minimization. Data will be analyzed at the physician level. Primary care units will be eligible if they use electronic prescribing and have at least four physicians willing to participate. Physicians in intervention units will be offered individual educational outreach visits (one for each guideline) at their workplace during a six-month period. Physicians in the control group will be offered a single unrelated group training session. Primary outcomes will be the proportion of cyclooxygenase-2 inhibitors prescribed in the anti-inflammatory class, and the proportion of omeprazole in the proton pump inhibitors class at 18 months post-intervention. Prescription data will be collected from the regional pharmacy claims database. We estimated a sample size of 110 physicians in each group, corresponding to 19 clusters with a mean size of 6 physicians. Outcome collection and data analysis will be blinded to allocation, but due to the nature of the intervention, physicians and detailers cannot be blinded. Discussion This trial will attempt to address unresolved issues in the literature, namely, long term persistence of effect, the importance of sequential visits in an outreach program, and cost issues. If successful, this trial may be the cornerstone for deploying large scale educational outreach
Abedini, Mohammad; Moradi, Mohammad H; Hosseinian, S M
2016-03-01
This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functio