Sample records for analysis combining ensemble

  1. Ensemble lymph node detection from CT volumes combining local intensity structure analysis approach and appearance learning approach (United States)

    Nakamura, Yoshihiko; Nimura, Yukitaka; Oda, Masahiro; Kitasaka, Takayuki; Furukawa, Kazuhiro; Goto, Hidemi; Fujiwara, Michitaka; Misawa, Kazunari; Mori, Kensaku


    This paper presents an ensemble lymph node detection method combining two automated lymph node detection methods from CT volumes. Detecting enlarged abdominal lymph nodes from CT volumes is an important task for the pre-operative diagnosis and planning done for cancer surgery. Although several research works have been conducted toward achieving automated abdominal lymph node detection methods, such methods still do not have enough accuracy for detecting lymph nodes of 5 mm or larger. This paper proposes an ensemble lymph node detection method that integrates two different lymph node detection schemes: (1) the local intensity structure analysis approach and (2) the appearance learning approach. This ensemble approach is introduced with the aim of achieving high sensitivity and specificity. Each component detection method is independently designed to detect candidate regions of enlarged abdominal lymph nodes whose diameters are over 5 mm. We applied the proposed ensemble method to 22 cases using abdominal CT volumes. Experimental results showed that we can detect about 90.4% (47/52) of the abdominal lymph nodes with about 15.2 false-positives/case for lymph nodes of 5mm or more in diameter.

  2. Combining 2-m temperature nowcasting and short range ensemble forecasting

    Directory of Open Access Journals (Sweden)

    A. Kann


    Full Text Available During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG since 2006 (Haiden et al., 2011, provides short range deterministic forecasts at high temporal (15 min–60 min and spatial (1 km resolution. An INCA Ensemble (INCA-EPS of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting which is running operationally at ZAMG (Wang et al., 2011. The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR which yields a statistical mbox{correction} of the first and second moment (mean bias and dispersion for Gaussian distributed continuous

  3. Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions (United States)

    Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.


    This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in

  4. A modified algorithm of the combined ensemble empirical mode decomposition and independent component analysis for the removal of cardiac artifacts from neuromuscular electrical signals

    International Nuclear Information System (INIS)

    Lee, Kwang Jin; Lee, Boreom; Choi, Eue Keun; Oh, Seil; Lee, Seung Min


    Neuronal and muscular electrical signals contain useful information about the neuromuscular system, with which researchers have been investigating the relationship of various neurological disorders and the neuromuscular system. However, neuromuscular signals can be critically contaminated by cardiac electrical activity (CEA) such as the electrocardiogram (ECG) which confounds data analysis. The purpose of our study is to provide a method for removing cardiac electrical artifacts from the neuromuscular signals recorded. We propose a new method for cardiac artifact removal which modifies the algorithm combining ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA). We compare our approach with a cubic smoothing spline method and the previous combined EEMD and ICA for various signal-to-noise ratio measures in simulated noisy physiological signals using a surface electromyogram (sEMG). Finally, we apply the proposed method to two real-life sets of data such as sEMG with ECG artifacts and ambulatory dog cardiac autonomic nervous signals measured from the ganglia near the heart, which are also contaminated with CEA. Our method can not only extract and remove artifacts, but can also preserve the spectral content of the neuromuscular signals. (paper)

  5. Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method (United States)

    Žvokelj, Matej; Zupan, Samo; Prebil, Ivan


    The article presents a novel non-linear multivariate and multiscale statistical process monitoring and signal denoising method which combines the strengths of the Kernel Principal Component Analysis (KPCA) non-linear multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD) to handle multiscale system dynamics. The proposed method which enables us to cope with complex even severe non-linear systems with a wide dynamic range was named the EEMD-based multiscale KPCA (EEMD-MSKPCA). The method is quite general in nature and could be used in different areas for various tasks even without any really deep understanding of the nature of the system under consideration. Its efficiency was first demonstrated by an illustrative example, after which the applicability for the task of bearing fault detection, diagnosis and signal denosing was tested on simulated as well as actual vibration and acoustic emission (AE) signals measured on purpose-built large-size low-speed bearing test stand. The positive results obtained indicate that the proposed EEMD-MSKPCA method provides a promising tool for tackling non-linear multiscale data which present a convolved picture of many events occupying different regions in the time-frequency plane.

  6. On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

    KAUST Repository

    Luo, Xiaodong


    The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].

  7. Gradient Flow Analysis on MILC HISQ Ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Brown, Nathan [Washington U., St. Louis; Bazavov, Alexei [Brookhaven; Bernard, Claude [Washington U., St. Louis; DeTar, Carleton [Utah U.; Foley, Justin [Utah U.; Gottlieb, Steven [Indiana U.; Heller, Urs M. [APS, New York; Hetrick, J. E. [U. Pacific, Stockton; Komijani, Javad [Washington U., St. Louis; Laiho, Jack [Syracuse U.; Levkova, Ludmila [Utah U.; Oktay, M. B. [Utah U.; Sugar, Robert [UC, Santa Barbara; Toussaint, Doug [Arizona U.; Van de Water, Ruth S. [Fermilab; Zhou, Ran [Fermilab


    We report on a preliminary scale determination with gradient-flow techniques on the $N_f = 2 + 1 + 1$ HISQ ensembles generated by the MILC collaboration. The ensembles include four lattice spacings, ranging from 0.15 to 0.06 fm, and both physical and unphysical values of the quark masses. The scales $\\sqrt{t_0}/a$ and $w_0/a$ are computed using Symanzik flow and the cloverleaf definition of $\\langle E \\rangle$ on each ensemble. Then both scales and the meson masses $aM_\\pi$ and $aM_K$ are adjusted for mistunings in the charm mass. Using a combination of continuum chiral perturbation theory and a Taylor series ansatz in the lattice spacing, the results are simultaneously extrapolated to the continuum and interpolated to physical quark masses. Our preliminary results are $\\sqrt{t_0} = 0.1422(7)$fm and $w_0 = 0.1732(10)$fm. We also find the continuum mass-dependence of $w_0$.

  8. Performance Analysis of Local Ensemble Kalman Filter (United States)

    Tong, Xin T.


    Ensemble Kalman filter (EnKF) is an important data assimilation method for high-dimensional geophysical systems. Efficient implementation of EnKF in practice often involves the localization technique, which updates each component using only information within a local radius. This paper rigorously analyzes the local EnKF (LEnKF) for linear systems and shows that the filter error can be dominated by the ensemble covariance, as long as (1) the sample size exceeds the logarithmic of state dimension and a constant that depends only on the local radius; (2) the forecast covariance matrix admits a stable localized structure. In particular, this indicates that with small system and observation noises, the filter error will be accurate in long time even if the initialization is not. The analysis also reveals an intrinsic inconsistency caused by the localization technique, and a stable localized structure is necessary to control this inconsistency. While this structure is usually taken for granted for the operation of LEnKF, it can also be rigorously proved for linear systems with sparse local observations and weak local interactions. These theoretical results are also validated by numerical implementation of LEnKF on a simple stochastic turbulence in two dynamical regimes.

  9. Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts

    National Research Council Canada - National Science Library

    Berrocal, Veronica J; Raftery, Adrian E; Gneiting, Tilmann


    .... Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites...

  10. Ensemble Methods (United States)

    Re, Matteo; Valentini, Giorgio


    proposed to explain the characteristics and the successful application of ensembles to different application domains. For instance, Allwein, Schapire, and Singer interpreted the improved generalization capabilities of ensembles of learning machines in the framework of large margin classifiers [4,177], Kleinberg in the context of stochastic discrimination theory [112], and Breiman and Friedman in the light of the bias-variance analysis borrowed from classical statistics [21,70]. Empirical studies showed that both in classification and regression problems, ensembles improve on single learning machines, and moreover large experimental studies compared the effectiveness of different ensemble methods on benchmark data sets [10,11,49,188]. The interest in this research area is motivated also by the availability of very fast computers and networks of workstations at a relatively low cost that allow the implementation and the experimentation of complex ensemble methods using off-the-shelf computer platforms. However, as explained in Section 26.2 there are deeper reasons to use ensembles of learning machines, motivated by the intrinsic characteristics of the ensemble methods. The main aim of this chapter is to introduce ensemble methods and to provide an overview and a bibliography of the main areas of research, without pretending to be exhaustive or to explain the detailed characteristics of each ensemble method. The paper is organized as follows. In the next section, the main theoretical and practical reasons for combining multiple learners are introduced. Section 26.3 depicts the main taxonomies on ensemble methods proposed in the literature. In Section 26.4 and 26.5, we present an overview of the main supervised ensemble methods reported in the literature, adopting a simple taxonomy, originally proposed in Ref. [201]. Applications of ensemble methods are only marginally considered, but a specific section on some relevant applications of ensemble methods in astronomy and

  11. An educational model for ensemble streamflow simulation and uncertainty analysis

    Directory of Open Access Journals (Sweden)

    A. AghaKouchak


    Full Text Available This paper presents the hands-on modeling toolbox, HBV-Ensemble, designed as a complement to theoretical hydrology lectures, to teach hydrological processes and their uncertainties. The HBV-Ensemble can be used for in-class lab practices and homework assignments, and assessment of students' understanding of hydrological processes. Using this modeling toolbox, students can gain more insights into how hydrological processes (e.g., precipitation, snowmelt and snow accumulation, soil moisture, evapotranspiration and runoff generation are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI and an ensemble simulation scheme that can be used for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity. HBV-Ensemble was administered in a class for both in-class instruction and a final project, and students submitted their feedback about the toolbox. The results indicate that this educational software had a positive impact on students understanding and knowledge of uncertainty in hydrological modeling.

  12. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    Energy Technology Data Exchange (ETDEWEB)

    Vrugt, Jasper A [Los Alamos National Laboratory; Wohling, Thomas [NON LANL


    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  13. Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions

    CERN Document Server

    Seni, Giovanni


    This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although e

  14. Combining large model ensembles with extreme value statistics to improve attribution statements of rare events

    Directory of Open Access Journals (Sweden)

    Sebastian Sippel


    In conclusion, our study shows that EVT and empirical estimates based on numerical simulations can indeed be used to productively inform each other, for instance to derive appropriate EVT parameters for short observational time series. Further, the combination of ensemble simulations with EVT allows us to significantly reduce the number of simulations needed for statements about the tails.

  15. An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters

    KAUST Repository

    Ait-El-Fquih, Boujemaa


    This work addresses the state-parameter filtering problem for dynamical systems with relatively large-dimensional state and low-dimensional parameters\\' vector. A Bayesian filtering algorithm combining the strengths of the particle filter (PF) and the ensemble Kalman filter (EnKF) is proposed. At each assimilation cycle of the proposed EnKF-PF, the PF is first used to sample the parameters\\' ensemble followed by the EnKF to compute the state ensemble conditional on the resulting parameters\\' ensemble. The proposed scheme is expected to be more efficient than the traditional state augmentation techniques, which suffer from the curse of dimensionality and inconsistency that is particularly pronounced when the state is a strongly nonlinear function of the parameters. In the new scheme, the EnKF and PF interact via their ensembles\\' members, in contrast with the recently introduced two-stage EnKF-PF (TS-EnKF-PF), which exchanges point estimates between EnKF and PF while requiring almost double the computational load. Numerical experiments are conducted with the Lorenz-96 model to assess the behavior of the proposed filter and to evaluate its performances against the joint PF, joint EnKF, and TS-EnKF-PF. Numerical results suggest that the EnKF-PF performs best in all tested scenarios. It was further found to be more robust, successfully estimating both state and parameters in different sensitivity experiments.

  16. Fuzzy ensemble clustering based on random projections for DNA microarray data analysis. (United States)

    Avogadri, Roberto; Valentini, Giorgio


    Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The main goal of this work consists in the exploration of new strategies and in the development of new clustering methods to improve the accuracy and robustness of clustering results, taking into account the uncertainty underlying the assignment of examples to clusters in the context of gene expression data analysis. We propose a fuzzy ensemble clustering approach both to improve the accuracy of clustering results and to take into account the inherent fuzziness of biological and bio-medical gene expression data. We applied random projections that obey the Johnson-Lindenstrauss lemma to obtain several instances of lower dimensional gene expression data from the original high-dimensional ones, approximately preserving the information and the metric structure of the original data. Then we adopt a double fuzzy approach to obtain a consensus ensemble clustering, by first applying a fuzzy k-means algorithm to the different instances of the projected low-dimensional data and then by using a fuzzy t-norm to combine the multiple clusterings. Several variants of the fuzzy ensemble clustering algorithms are proposed, according to different techniques to combine the base clusterings and to obtain the final consensus clustering. We applied our proposed fuzzy ensemble methods to the gene expression analysis of leukemia, lymphoma, adenocarcinoma and melanoma patients, and we compared the results with other state of the art ensemble methods. Results show that in some cases, taking into account the natural fuzziness of the data, we can improve the discovery of classes of patients defined at bio-molecular level. The reduction of the dimension of the data, achieved through random

  17. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad


    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  18. Public Involvement, Multi-Million Member Ensembles and Systematic Uncertainty Analysis (United States)

    Stainforth, D. A.; Allen, M.; Kettleborough, J.; Collins, M.; Heaps, A.; Stott, P.; Wehner, M.


    The project is preparing to carry out the first systematic uncertainty analysis of climate forecasts using large ensembles of GCM climate simulations. This will be done by involving schools, businesses and members of the public, and utilizing the novel technology of distributed computing. Each participant will be asked to run one member of the ensemble on their PC. The model used will initially be the UK Met Office's Unified Model (UM). It will be run under Windows and software will be provided to enable those involved to view their model output as it develops. The project will use this method to carry out large perturbed physics GCM ensembles and thereby analyse the uncertainty in the forecasts from such models. Each participant/ensemble member will therefore have a version of the UM in which certain aspects of the model physics have been perturbed from their default values. Of course the non-linear nature of the system means that it will be necessary to look not just at perturbations to individual parameters in specific schemes, such as the cloud parameterization, but also to the many combinations of perturbations. This rapidly leads to the need for very large, perhaps multi-million member ensembles, which could only be undertaken using the distributed computing methodology. The status of the project will be presented and the Windows client will be demonstrated. In addition, initial results will be presented from beta test runs using a demo release for Linux PCs and Alpha workstations. Although small by comparison to the whole project, these pilot results constitute a 20-50 member perturbed physics climate ensemble with results indicating how climate sensitivity can be substantially affected by individual parameter values in the cloud scheme.

  19. Combining super-ensembles and statistical emulation to improve a regional climate and vegetation model (United States)

    Hawkins, L. R.; Rupp, D. E.; Li, S.; Sarah, S.; McNeall, D. J.; Mote, P.; Betts, R. A.; Wallom, D.


    Changing regional patterns of surface temperature, precipitation, and humidity may cause ecosystem-scale changes in vegetation, altering the distribution of trees, shrubs, and grasses. A changing vegetation distribution, in turn, alters the albedo, latent heat flux, and carbon exchanged with the atmosphere with resulting feedbacks onto the regional climate. However, a wide range of earth-system processes that affect the carbon, energy, and hydrologic cycles occur at sub grid scales in climate models and must be parameterized. The appropriate parameter values in such parameterizations are often poorly constrained, leading to uncertainty in predictions of how the ecosystem will respond to changes in forcing. To better understand the sensitivity of regional climate to parameter selection and to improve regional climate and vegetation simulations, we used a large perturbed physics ensemble and a suite of statistical emulators. We dynamically downscaled a super-ensemble (multiple parameter sets and multiple initial conditions) of global climate simulations using a 25-km resolution regional climate model HadRM3p with the land-surface scheme MOSES2 and dynamic vegetation module TRIFFID. We simultaneously perturbed land surface parameters relating to the exchange of carbon, water, and energy between the land surface and atmosphere in a large super-ensemble of regional climate simulations over the western US. Statistical emulation was used as a computationally cost-effective tool to explore uncertainties in interactions. Regions of parameter space that did not satisfy observational constraints were eliminated and an ensemble of parameter sets that reduce regional biases and span a range of plausible interactions among earth system processes were selected. This study demonstrated that by combining super-ensemble simulations with statistical emulation, simulations of regional climate could be improved while simultaneously accounting for a range of plausible land

  20. Combining the ensemble and Franck-Condon approaches for calculating spectral shapes of molecules in solution (United States)

    Zuehlsdorff, T. J.; Isborn, C. M.


    The correct treatment of vibronic effects is vital for the modeling of absorption spectra of many solvated dyes. Vibronic spectra for small dyes in solution can be easily computed within the Franck-Condon approximation using an implicit solvent model. However, implicit solvent models neglect specific solute-solvent interactions on the electronic excited state. On the other hand, a straightforward way to account for solute-solvent interactions and temperature-dependent broadening is by computing vertical excitation energies obtained from an ensemble of solute-solvent conformations. Ensemble approaches usually do not account for vibronic transitions and thus often produce spectral shapes in poor agreement with experiment. We address these shortcomings by combining zero-temperature vibronic fine structure with vertical excitations computed for a room-temperature ensemble of solute-solvent configurations. In this combined approach, all temperature-dependent broadening is treated classically through the sampling of configurations and quantum mechanical vibronic contributions are included as a zero-temperature correction to each vertical transition. In our calculation of the vertical excitations, significant regions of the solvent environment are treated fully quantum mechanically to account for solute-solvent polarization and charge-transfer. For the Franck-Condon calculations, a small amount of frozen explicit solvent is considered in order to capture solvent effects on the vibronic shape function. We test the proposed method by comparing calculated and experimental absorption spectra of Nile red and the green fluorescent protein chromophore in polar and non-polar solvents. For systems with strong solute-solvent interactions, the combined approach yields significant improvements over the ensemble approach. For systems with weak to moderate solute-solvent interactions, both the high-energy vibronic tail and the width of the spectra are in excellent agreement with

  1. An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels

    Directory of Open Access Journals (Sweden)

    Gang Zhang


    Full Text Available Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT and support vector machine (SVM are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2%  ±  2.8% measured by zero-one loss for the first evaluation session and 79.6%  ±  3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low.

  2. Impact of hybrid GSI analysis using ETR ensembles

    Indian Academy of Sciences (India)

    NCMRWF Global Forecast. System) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) of resolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013 and forecast ...

  3. EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data

    Energy Technology Data Exchange (ETDEWEB)

    Shu, Qingya; Guo, Hanqi; Che, Limei; Yuan, Xiaoru; Liu, Junfeng; Liang, Jie


    We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based on ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.

  4. Ovis: A framework for visual analysis of ocean forecast ensembles

    KAUST Repository

    Hollt, Thomas


    We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea. © 1995-2012 IEEE.

  5. Computer-aided detection (CAD) of breast masses in mammography: combined detection and ensemble classification

    International Nuclear Information System (INIS)

    Choi, Jae Young; Kim, Dae Hoe; Ro, Yong Man; Plataniotis, Konstantinos N


    We propose a novel computer-aided detection (CAD) framework of breast masses in mammography. To increase detection sensitivity for various types of mammographic masses, we propose the combined use of different detection algorithms. In particular, we develop a region-of-interest combination mechanism that integrates detection information gained from unsupervised and supervised detection algorithms. Also, to significantly reduce the number of false-positive (FP) detections, the new ensemble classification algorithm is developed. Extensive experiments have been conducted on a benchmark mammogram database. Results show that our combined detection approach can considerably improve the detection sensitivity with a small loss of FP rate, compared to representative detection algorithms previously developed for mammographic CAD systems. The proposed ensemble classification solution also has a dramatic impact on the reduction of FP detections; as much as 70% (from 15 to 4.5 per image) at only cost of 4.6% sensitivity loss (from 90.0% to 85.4%). Moreover, our proposed CAD method performs as well or better (70.7% and 80.0% per 1.5 and 3.5 FPs per image respectively) than the results of mammography CAD algorithms previously reported in the literature. (paper)

  6. Spike Train SIMilarity Space (SSIMS): a frame-work for single neuron and ensemble data analysis (United States)

    Vargas-Irwin, Carlos E.; Brandman, David M.; Zimmermann, Jonas B.; Donoghue, John P.; Black, Michael J.


    Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large scale ensemble activity, beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models. PMID:25380335

  7. The Impact of Incorporating Chemistry to Numerical Weather Prediction Models: An Ensemble-Based Sensitivity Analysis (United States)

    Barnard, P. A.; Arellano, A. F.


    Data assimilation has emerged as an integral part of numerical weather prediction (NWP). More recently, atmospheric chemistry processes have been incorporated into NWP models to provide forecasts and guidance on air quality. There is, however, a unique opportunity within this coupled system to investigate the additional benefit of constraining model dynamics and physics due to chemistry. Several studies have reported the strong interaction between chemistry and meteorology through radiation, transport, emission, and cloud processes. To examine its importance to NWP, we conduct an ensemble-based sensitivity analysis of meteorological fields to the chemical and aerosol fields within the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Data Assimilation Research Testbed (DART) framework. In particular, we examine the sensitivity of the forecasts of surface temperature and related dynamical fields to the initial conditions of dust and aerosol concentrations in the model over the continental United States within the summer 2008 time period. We use an ensemble of meteorological and chemical/aerosol predictions within WRF-Chem/DART to calculate the sensitivities. This approach is similar to recent ensemble-based sensitivity studies in NWP. The use of an ensemble prediction is appealing because the analysis does not require the adjoint of the model, which to a certain extent becomes a limitation due to the rapidly evolving models and the increasing number of different observations. Here, we introduce this approach as applied to atmospheric chemistry. We also show our initial results of the calculated sensitivities from joint assimilation experiments using a combination of conventional meteorological observations from the National Centers for Environmental Prediction, retrievals of aerosol optical depth from NASA's Moderate Resolution Imaging Spectroradiometer, and retrievals of carbon monoxide from NASA's Measurements of Pollution in the

  8. Ensemble approach combining multiple methods improves human transcription start site prediction

    LENUS (Irish Health Repository)

    Dineen, David G


    Abstract Background The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets. Results We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier (\\'Profisi Ensemble\\') using predictions from 7 programs, along with 2 other data sources. Support vector machines using \\'full\\' and \\'reduced\\' data sets are combined in an either\\/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool. Conclusions Supervised learning methods are a useful way to combine predictions from diverse sources.

  9. Combining Rosetta with molecular dynamics (MD): A benchmark of the MD-based ensemble protein design. (United States)

    Ludwiczak, Jan; Jarmula, Adam; Dunin-Horkawicz, Stanislaw


    Computational protein design is a set of procedures for computing amino acid sequences that will fold into a specified structure. Rosetta Design, a commonly used software for protein design, allows for the effective identification of sequences compatible with a given backbone structure, while molecular dynamics (MD) simulations can thoroughly sample near-native conformations. We benchmarked a procedure in which Rosetta design is started on MD-derived structural ensembles and showed that such a combined approach generates 20-30% more diverse sequences than currently available methods with only a slight increase in computation time. Importantly, the increase in diversity is achieved without a loss in the quality of the designed sequences assessed by their resemblance to natural sequences. We demonstrate that the MD-based procedure is also applicable to de novo design tasks started from backbone structures without any sequence information. In addition, we implemented a protocol that can be used to assess the stability of designed models and to select the best candidates for experimental validation. In sum our results demonstrate that the MD ensemble-based flexible backbone design can be a viable method for protein design, especially for tasks that require a large pool of diverse sequences. Copyright © 2018 Elsevier Inc. All rights reserved.

  10. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting (United States)

    Rubin, J. I.; Reid, J. S.; Hansen, J. A.; Anderson, J. L.; Collins, N.; Hoar, T. J.; Hogan, T.; Lynch, P.; McLay, J.; Reynolds, C. A.; Sessions, W. R.; Westphal, D. L.; Zhang, J.


    An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System (ENAAPS) at 1° × 1°, combined with an Ensemble Adjustment Kalman Filter from NCAR's Data Assimilation Research Testbed (DART). The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS) meteorological ensemble to drive offline NAAPS simulations coupled with the DART Ensemble Kalman Filter architecture to assimilate bias-corrected MODIS Aerosol Optical Thickness (AOT) retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long range transport. Conversely, the meteorological ensemble produces sufficient spread at the synoptic scale to enable observational impact through the ensemble data

  11. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS and its application of the Data Assimilation Research Testbed (DART in support of aerosol forecasting

    Directory of Open Access Journals (Sweden)

    J. I. Rubin


    Full Text Available An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System (ENAAPS at 1 × 1°, combined with an ensemble adjustment Kalman filter from NCAR's Data Assimilation Research Testbed (DART. The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS meteorological ensemble to drive offline NAAPS simulations coupled with the DART ensemble Kalman filter architecture to assimilate bias-corrected MODIS aerosol optical thickness (AOT retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions, such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long-range transport. Conversely, the meteorological ensemble generates sufficient spread at the synoptic scale to enable observational impact

  12. Atomic clock ensemble in space (ACES) data analysis (United States)

    Meynadier, F.; Delva, P.; le Poncin-Lafitte, C.; Guerlin, C.; Wolf, P.


    The Atomic Clocks Ensemble in Space (ACES/PHARAO mission, ESA & CNES) will be installed on board the International Space Station (ISS) next year. A crucial part of this experiment is its two-way microwave link (MWL), which will compare the timescale generated on board with those provided by several ground stations disseminated on the Earth. A dedicated data analysis center is being implemented at SYRTE—Observatoire de Paris, where our team currently develops theoretical modelling, numerical simulations and the data analysis software itself. In this paper, we present some key aspects of the MWL measurement method and the associated algorithms for simulations and data analysis. We show the results of tests using simulated data with fully realistic effects such as fundamental measurement noise, Doppler, atmospheric delays, or cycle ambiguities. We demonstrate satisfactory performance of the software with respect to the specifications of the ACES mission. The main scientific product of our analysis is the clock desynchronisation between ground and space clocks, i.e. the difference of proper times between the space clocks and ground clocks at participating institutes. While in flight, this measurement will allow for tests of general relativity and Lorentz invariance at unprecedented levels, e.g. measurement of the gravitational redshift at the 3×10-6 level. As a specific example, we use real ISS orbit data with estimated errors at the 10 m level to study the effect of such errors on the clock desynchronisation obtained from MWL data. We demonstrate that the resulting effects are totally negligible.

  13. Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers. (United States)

    Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M


    The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

  14. Combining Statistical and Ensemble Streamflow Predictions to Cope with Consensus Forecast (United States)

    Mirfenderesgi, G.; Najafi, M.; Moradkhani, H.


    Monthly and seasonal water supply outlooks are used for water resource planning and management including the industrial and agriculture water allocation as well as reservoir operations. Currently consensus forecasts are jointly issued by the operational agencies in the Western US based on statistical regression equations and ensemble streamflow predictions. However, an objective method is needed to combine the forecasts from these methods. In this study monthly and seasonal streamflow predictions are generated from various hydrologic and statistical simulations including: Variable Infiltration Capacity (VIC), Sacramento Soil Moisture Accounting Model (SAC-SMA), Precipitation Runoff Modeling System (PRMS), Conceptual Hydrologic MODel (HYMOD), and Principal and Independent Component Regression (PCR and ICR), etc. The results are optimally combined by several objective multi-modeling methods. The increase in forecast accuracy is assessed in comparison with the available best and worst prediction. The precision of each multi-model method is also estimated. The study is performed over the Lake Granby, located in the headwaters of the Colorado River Basin. Overall the results show improvements in both monthly and seasonal forecasts as compared with single model simulations.

  15. Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data (United States)

    U.S. Environmental Protection Agency — This dataset documents the source of the data analyzed in the manuscript " Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII...

  16. Climate Prediction Center (CPC)Ensemble Canonical Correlation Analysis 90-Day Seasonal Forecast of Precipitation (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) precipitation forecast is a 90-day (seasonal) outlook of US surface precipitation anomalies. The ECCA uses...

  17. Climate Prediction Center(CPC)Ensemble Canonical Correlation Analysis Forecast of Temperature (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) temperature forecast is a 90-day (seasonal) outlook of US surface temperature anomalies. The ECCA uses Canonical...

  18. Application of Cyclone Relative Approach and Ensemble Sensitivity Analysis to Better Understand Extratropical Cyclone Errors in Operational Models and Ensembles (United States)

    Song, Xinxia

    A cyclone relative approach and an ensemble sensitivity analysis (ESA) were applied to explore some of the possible reasons for extratropical cyclone center mean sea level pressure errors. For the cyclone relative approach, data were extracted within a box region and saved every 6 hours. GEFS (Global Ensemble Forecast System) control member and ensemble members forecast data were utilized in this research. Around the cyclone, errors in fields such as mean sea level pressure and precipitation rapidly increase from day 4 to day 5, and the errors of all fields examined are consistent with the overpredicted and underpredicted cyclones. For example, for an overforecast cyclone, it has more intense PV (potential vorticity) at 320K, a stronger temperature gradient on 925hPa, and greater simulated precipitation than observed, while the underpredicted cyclones have the opposite results. The day 3 precipitation errors and 925 hPa temperature gradient errors are relatively large before the cyclone errors develop, thus suggesting that moisture and latent heat and dry dynamics could contribute to cyclogenesis intensity errors. ESA accompanied with cyclone relative approach implies that moisture may contribute to the cyclogenesis error at an initial stage of cyclone development. There are also hints of upstream errors growing and moving in from ESA cases. AA possible explanation for underpredicted cyclones might be that less moisture on the warm side of cyclones leads to a weaker upper tropospheric latent heat release, and hence a less amplified PV field, and a weaker cyclone. In addition, a weaker temperature gradient at 925 hPa could also cause a weaker cyclone.

  19. An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter

    Directory of Open Access Journals (Sweden)

    G. Wu


    Full Text Available The ensemble Kalman filter (EnKF is a widely used ensemble-based assimilation method, which estimates the forecast error covariance matrix using a Monte Carlo approach that involves an ensemble of short-term forecasts. While the accuracy of the forecast error covariance matrix is crucial for achieving accurate forecasts, the estimate given by the EnKF needs to be improved using inflation techniques. Otherwise, the sampling covariance matrix of perturbed forecast states will underestimate the true forecast error covariance matrix because of the limited ensemble size and large model errors, which may eventually result in the divergence of the filter. In this study, the forecast error covariance inflation factor is estimated using a generalized cross-validation technique. The improved EnKF assimilation scheme is tested on the atmosphere-like Lorenz-96 model with spatially correlated observations, and is shown to reduce the analysis error and increase its sensitivity to the observations.

  20. Assessing the blinking state of fluorescent quantum dots in free solution by combining fluorescence correlation spectroscopy with ensemble spectroscopic methods. (United States)

    Dong, Chaoqing; Liu, Heng; Ren, Jicun


    The current method for investigating the blinking behavior is to immobilize quantum dots (QDs) in the matrix and then apply a fluorescent technique to monitor the fluorescent trajectories of individual QDs. So far, no method can be used to directly assess the blinking state of ensemble QDs in free solution. In this study, a new method was described to characterize the blinking state of the QDs in free solution by combining single molecule fluorescence correlation spectroscopy (FCS) with ensemble spectroscopic methods. Its principle is based on the observation that the apparent concentration of bright QDs obtained by FCS is less than its actual concentration measured by ensemble spectroscopic method due to the QDs blinking. We proposed a blinking index (Kblink) for characterizing the blinking state of QDs, and Kblink is defined as the ratio of the actual concentration (Cb,actual) measured by the ensemble spectroscopic method to the apparent concentration (Cb,app) of QDs obtained by FCS. The effects of certain factors such as laser intensity, growth process, and ligands on blinking of QDs were investigated. The Kblink data of QDs obtained were successfully used to characterize the blinking state of QDs and explain certain experimental results.

  1. A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico

    KAUST Repository

    Hoteit, Ibrahim


    This paper describes the development of an advanced ensemble Kalman filter (EnKF)-based ocean data assimilation system for prediction of the evolution of the loop current in the Gulf of Mexico (GoM). The system integrates the Data Assimilation Research Testbed (DART) assimilation package with the Massachusetts Institute of Technology ocean general circulation model (MITgcm). The MITgcm/DART system supports the assimilation of a wide range of ocean observations and uses an ensemble approach to solve the nonlinear assimilation problems. The GoM prediction system was implemented with an eddy-resolving 1/10th degree configuration of the MITgcm. Assimilation experiments were performed over a 6-month period between May and October during a strong loop current event in 1999. The model was sequentially constrained with weekly satellite sea surface temperature and altimetry data. Experiments results suggest that the ensemble-based assimilation system shows a high predictive skill in the GoM, with estimated ensemble spread mainly concentrated around the front of the loop current. Further analysis of the system estimates demonstrates that the ensemble assimilation accurately reproduces the observed features without imposing any negative impact on the dynamical balance of the system. Results from sensitivity experiments with respect to the ensemble filter parameters are also presented and discussed. © 2013 Elsevier B.V.

  2. With or without a conductor: Comparative analysis of leadership models in the musical ensemble

    Directory of Open Access Journals (Sweden)

    Kovačević Mia


    Full Text Available In search of innovative models of work organization and therefore the artistic process of one musical ensemble, in the last ten years musical ensembles have developed examples of non-traditional artistic-performing decisions and organizational practice. The paper is conceived as a research and analysis of the dominant models of leadership (i.e. organizing, conducting business applicable on the music ensembles and experiences of the musicians. The aim is to recognize and define leadership styles that encourage the increase of motivation and productivity of musicians within the musical ensemble. The paper will specifically investigate the relationship and differences between the two dominant models of leadership, leadership of conductor and collaborative leadership. At the same time, the paper describes and analyses an experiment that was conducted by the Ensemble Metamorphosis, which applied into their work two dominant models of leadership. In an effort to increase the motivation and productivity of musicians, Ensemble Metamorphosis also searched for a new management model of work organization and a new model of leadership. The aim of this paper was therefore to investigate the effects of leadership models that improve the artistic quality, motivation of the musicians, psychological climate and overall increase productivity of musical organization.

  3. Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

    Directory of Open Access Journals (Sweden)

    I. Kioutsioukis


    Full Text Available Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism. Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3, nitrogen dioxide (NO2 and particulate matter (PM10. Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII. The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint

  4. Ensemble Learning or Deep Learning? Application to Default Risk Analysis

    Directory of Open Access Journals (Sweden)

    Shigeyuki Hamori


    Full Text Available Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy and classification ability of three ensemble-learning methods—specifically, bagging, random forest, and boosting—with those of various neural-network methods, each of which has a different activation function. The results obtained indicate that the classification ability of boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of neural-network models depends on the choice of activation function, the number of middle layers, and the inclusion of dropout.

  5. Ensemble catchment hydrological modelling for climate change impact analysis (United States)

    Vansteenkiste, Thomas; Ntegeka, Victor; Willems, Patrick


    It is vital to investigate how the hydrological model structure affects the climate change impact given that future changes not in the range for which the models were calibrated or validated are likely. Thus an ensemble modelling approach which involves a diversity of models with different structures such as spatial resolutions and process descriptions is crucial. The ensemble modelling approach was applied to a set of models: from the lumped conceptual models NAM, PDM and VHM, an intermediate detailed and distributed model WetSpa, to the highly detailed and fully distributed model MIKE-SHE. Explicit focus was given to the high and low flow extremes. All models were calibrated for sub flows and quick flows derived from rainfall and potential evapotranspiration (ETo) time series. In general, all models were able to produce reliable estimates of the flow regimes under the current climate for extreme peak and low flows. An intercomparison of the low and high flow changes under changed climatic conditions was made using climate scenarios tailored for extremes. Tailoring was important for two reasons. First, since the use of many scenarios was not feasible it was necessary to construct few scenarios that would reasonably represent the range of extreme impacts. Second, scenarios would be more informative as changes in high and low flows would be easily traced to changes of ETo and rainfall; the tailored scenarios are constructed using seasonal changes that are defined using different levels of magnitude (high, mean and low) for rainfall and ETo. After simulation of these climate scenarios in the five hydrological models, close agreement was found among the models. The different models predicted similar range of peak flow changes. For the low flows, however, the differences in the projected impact range by different hydrological models was larger, particularly for the drier scenarios. This suggests that the hydrological model structure is critical in low flow predictions

  6. Extreme Value Analysis of hydro meteorological extremes in the ClimEx Large-Ensemble (United States)

    Wood, R. R.; Martel, J. L.; Willkofer, F.; von Trentini, F.; Schmid, F. J.; Leduc, M.; Frigon, A.; Ludwig, R.


    Many studies show an increase in the magnitude and frequency of hydrological extreme events in the course of climate change. However the contribution of natural variability to the magnitude and frequency of hydrological extreme events is not yet settled. A reliable estimate of extreme events is from great interest for water management and public safety. In the course of the ClimEx Project ( a new single-model large-ensemble was created by dynamically downscaling the CanESM2 large-ensemble with the Canadian Regional Climate Model version 5 (CRCM5) for an European Domain and a Northeastern North-American domain. By utilizing the ClimEx 50-Member Large-Ensemble (CRCM5 driven by CanESM2 Large-Ensemble) a thorough analysis of natural variability in extreme events is possible. Are the current extreme value statistical methods able to account for natural variability? How large is the natural variability for e.g. a 1/100 year return period derived from a 50-Member Large-Ensemble for Europe and Northeastern North-America? These questions should be answered by applying various generalized extreme value distributions (GEV) to the ClimEx Large-Ensemble. Hereby various return levels (5-, 10-, 20-, 30-, 60- and 100-years) based on various lengths of time series (20-, 30-, 50-, 100- and 1500-years) should be analyzed for the maximum one day precipitation (RX1d), the maximum three hourly precipitation (RX3h) and the streamflow for selected catchments in Europe. The long time series of the ClimEx Ensemble (7500 years) allows us to give a first reliable estimate of the magnitude and frequency of certain extreme events.

  7. Application of Ensemble Detection and Analysis to Modeling Uncertainty in Non Stationary Process (United States)

    Racette, Paul


    Characterization of non stationary and nonlinear processes is a challenge in many engineering and scientific disciplines. Climate change modeling and projection, retrieving information from Doppler measurements of hydrometeors, and modeling calibration architectures and algorithms in microwave radiometers are example applications that can benefit from improvements in the modeling and analysis of non stationary processes. Analyses of measured signals have traditionally been limited to a single measurement series. Ensemble Detection is a technique whereby mixing calibrated noise produces an ensemble measurement set. The collection of ensemble data sets enables new methods for analyzing random signals and offers powerful new approaches to studying and analyzing non stationary processes. Derived information contained in the dynamic stochastic moments of a process will enable many novel applications.

  8. The Ensembl REST API: Ensembl Data for Any Language. (United States)

    Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R S; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul


    We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. The Ensembl REST API can be accessed at and source code is freely available under an Apache 2.0 license from © The Author 2014. Published by Oxford University Press.

  9. Impact of hybrid GSI analysis using ETR ensembles

    Indian Academy of Sciences (India)

    Rainfall forecast is verified over Indian region against combined observations of IMD and NCMRWF. ... The verification of forecasts with radiosonde observations also show improvementin wind forecasts with the hybrid assimilation. ... National Centre for Medium Range Weather Forecasting, A-50, Sector-62, Noida, India.

  10. Impact of hybrid GSI analysis using ETR ensembles

    Indian Academy of Sciences (India)

    Parrish D F and Derber J C 1992 The National Meteoro- logical Center's spectral statistical interpolation analysis system; Mon. Wea. Rev. 120 1747–1763. Patil D, Hunt B R, Kalnay E, Yorke J A and Ott E 2001. Local low dimensionality at atmospheric dynamics; Phys. Rev. Lett. 86 5878–5881. Penny S G 2014 The hybrid ...

  11. Multi-model ensemble combinations of the water budget in the East/Japan Sea (United States)

    HAN, S.; Hirose, N.; Usui, N.; Miyazawa, Y.


    The water balance of East/Japan Sea is determined mainly by inflow and outflow through the Korea/Tsushima, Tsugaru and Soya/La Perouse Straits. However, the volume transports measured at three straits remain quantitatively unbalanced. This study examined the seasonal variation of the volume transport using the multiple linear regression and ridge regression of multi-model ensemble (MME) methods to estimate physically consistent circulation in East/Japan Sea by using four different data assimilation models. The MME outperformed all of the single models by reducing uncertainties, especially the multicollinearity problem with the ridge regression. However, the regression constants turned out to be inconsistent with each other if the MME was applied separately for each strait. The MME for a connected system was thus performed to find common constants for these straits. The estimation of this MME was found to be similar to the MME result of sea level difference (SLD). The estimated mean transport (2.42 Sv) was smaller than the measurement data at the Korea/Tsushima Strait, but the calibrated transport of the Tsugaru Strait (1.63 Sv) was larger than the observed data. The MME results of transport and SLD also suggested that the standard deviation (STD) of the Korea/Tsushima Strait is larger than the STD of the observation, whereas the estimated results were almost identical to that observed for the Tsugaru and Soya/La Perouse Straits. The similarity between MME results enhances the reliability of the present MME estimation.

  12. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning (United States)

    Yu, Jianbo


    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  13. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting in the Tehachapi Region Winter Season

    Energy Technology Data Exchange (ETDEWEB)

    Zack, John [AWS Truepower, LLC, Albany, NY (United States); Natenberg, Eddie [AWS Truepower, LLC, Albany, NY (United States); Young, Steve [AWS Truepower, LLC, Albany, NY (United States); Van Knowe, Glenn [AWS Truepower, LLC, Albany, NY (United States); Waight, Ken [AWS Truepower, LLC, Albany, NY (United States); Manobainco, John [AWS Truepower, LLC, Albany, NY (United States); Kamath, Chandrika [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)


    This study extends the wind power forecast sensitivity work done by Zack et al. (2010a, b) in two prior research efforts. Zack et al. (2010a, b) investigated the relative predictive value and optimal combination of different variables/locations from correlated sensitivity patterns. Their work involved developing the Multiple Observation Optimization Algorithm (MOOA) and applying the algorithm to the results obtained from the Ensemble Sensitivity Analysis (ESA) method (Ancell and Hakim 2007; Torn and Hakim 2008).

  14. Comparison of different incremental analysis update schemes in a realistic assimilation system with Ensemble Kalman Filter (United States)

    Yan, Y.; Barth, A.; Beckers, J. M.; Brankart, J. M.; Brasseur, P.; Candille, G.


    In this paper, three incremental analysis update schemes (IAU 0, IAU 50 and IAU 100) are compared in the same assimilation experiments with a realistic eddy permitting primitive equation model of the North Atlantic Ocean using the Ensemble Kalman Filter. The difference between the three IAU schemes lies on the position of the increment update window. The relevance of each IAU scheme is evaluated through analyses on both thermohaline and dynamical variables. The validation of the assimilation results is performed according to both deterministic and probabilistic metrics against different sources of observations. For deterministic validation, the ensemble mean and the ensemble spread are compared to the observations. For probabilistic validation, the continuous ranked probability score (CRPS) is used to evaluate the ensemble forecast system according to reliability and resolution. The reliability is further decomposed into bias and dispersion by the reduced centred random variable (RCRV) score. The obtained results show that 1) the IAU 50 scheme has the same performance as the IAU 100 scheme 2) the IAU 50/100 schemes outperform the IAU 0 scheme in error covariance propagation for thermohaline variables in relatively stable region, while the IAU 0 scheme outperforms the IAU 50/100 schemes in dynamical variables estimation in dynamically active region 3) in case with sufficient number of observations and good error specification, the impact of IAU schemes is negligible. The differences between the IAU 0 scheme and the IAU 50/100 schemes are mainly due to different model integration time and different instability (density inversion, large vertical velocity, etc.) induced by the increment update. The longer model integration time with the IAU 50/100 schemes, especially the free model integration, on one hand, allows for better re-establishment of the equilibrium model state, on the other hand, smooths the strong gradients in dynamically active region.

  15. Optics clustered to output unique solutions: a multi-laser facility for combined single molecule and ensemble microscopy. (United States)

    Clarke, David T; Botchway, Stanley W; Coles, Benjamin C; Needham, Sarah R; Roberts, Selene K; Rolfe, Daniel J; Tynan, Christopher J; Ward, Andrew D; Webb, Stephen E D; Yadav, Rahul; Zanetti-Domingues, Laura; Martin-Fernandez, Marisa L


    Optics clustered to output unique solutions (OCTOPUS) is a microscopy platform that combines single molecule and ensemble imaging methodologies. A novel aspect of OCTOPUS is its laser excitation system, which consists of a central core of interlocked continuous wave and pulsed laser sources, launched into optical fibres and linked via laser combiners. Fibres are plugged into wall-mounted patch panels that reach microscopy end-stations in adjacent rooms. This allows multiple tailor-made combinations of laser colours and time characteristics to be shared by different end-stations minimising the need for laser duplications. This setup brings significant benefits in terms of cost effectiveness, ease of operation, and user safety. The modular nature of OCTOPUS also facilitates the addition of new techniques as required, allowing the use of existing lasers in new microscopes while retaining the ability to run the established parts of the facility. To date, techniques interlinked are multi-photon/multicolour confocal fluorescence lifetime imaging for several modalities of fluorescence resonance energy transfer (FRET) and time-resolved anisotropy, total internal reflection fluorescence, single molecule imaging of single pair FRET, single molecule fluorescence polarisation, particle tracking, and optical tweezers. Here, we use a well-studied system, the epidermal growth factor receptor network, to illustrate how OCTOPUS can aid in the investigation of complex biological phenomena. © 2011 American Institute of Physics

  16. Dimensionality Reduction Ensembles


    Farrelly, Colleen M.


    Ensemble learning has had many successes in supervised learning, but it has been rare in unsupervised learning and dimensionality reduction. This study explores dimensionality reduction ensembles, using principal component analysis and manifold learning techniques to capture linear, nonlinear, local, and global features in the original dataset. Dimensionality reduction ensembles are tested first on simulation data and then on two real medical datasets using random forest classifiers; results ...

  17. Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools (United States)

    Henley, E. M.; Pope, E. C. D.


    This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

  18. Ensemble Empirical Mode Decomposition: Image Data Analysis with White-noise Reflection

    Directory of Open Access Journals (Sweden)

    M. Kopecký


    Full Text Available During the last decade, Zhaohua Wu and Norden E. Huang announced a new improvement of the original Empirical Mode Decomposition method (EMD. Ensemble Empirical Mode Decomposition and its abbreviation EEMD represents a major improvement with great versatility and robustness in noisy data filtering. EEMD consists of sifting and making an ensemble of a white noise-added signal, and treats the mean value as the final true result. This is due to the use of a finite, not infinitesimal, amplitude of white noise which forces the ensemble to exhaust all possible solutions in the sifting process. These steps collate signals of different scale in a proper intrinsic mode function (IMF dictated by the dyadic filter bank. As EEMD is a time–space analysis method, the added white noise is averaged out with a sufficient number of trials. Here, the only persistent part that survives the averaging process is the signal component (original data, which is then treated as the true and more physically meaningful answer. The main purpose of adding white noise was to provide a uniform reference frame in the time–frequency space. The added noise collates the portion of the signal of comparable scale in a single IMF. Image data taken as time series is a non-stationary and nonlinear process to which the new proposed EEMD method can be fitted out. This paper reviews the new approach of using EEMD and demonstrates its use on the example of image data analysis, making use of some advantages of the statistical characteristics of white noise. This approach helps to deal with omnipresent noise.

  19. Ensemble Data Mining Methods (United States)

    National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...

  20. Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

    Directory of Open Access Journals (Sweden)

    Badri Padhukasahasram

    Full Text Available Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.

  1. Statistical Analysis of the First Passage Path Ensemble of Jump Processes (United States)

    von Kleist, Max; Schütte, Christof; Zhang, Wei


    The transition mechanism of jump processes between two different subsets in state space reveals important dynamical information of the processes and therefore has attracted considerable attention in the past years. In this paper, we study the first passage path ensemble of both discrete-time and continuous-time jump processes on a finite state space. The main approach is to divide each first passage path into nonreactive and reactive segments and to study them separately. The analysis can be applied to jump processes which are non-ergodic, as well as continuous-time jump processes where the waiting time distributions are non-exponential. In the particular case that the jump processes are both Markovian and ergodic, our analysis elucidates the relations between the study of the first passage paths and the study of the transition paths in transition path theory. We provide algorithms to numerically compute statistics of the first passage path ensemble. The computational complexity of these algorithms scales with the complexity of solving a linear system, for which efficient methods are available. Several examples demonstrate the wide applicability of the derived results across research areas.

  2. Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles. (United States)

    Williams, N J; Nasuto, S J; Saddy, J D


    The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Using decision trees and their ensembles for analysis of NIR spectroscopic data

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.

    Advanced machine learning methods, like convolutional neural networks and decision trees, became extremely popular in the last decade. This, first of all, is directly related to the current boom in Big data analysis, where traditional statistical methods are not efficient. According to the — the most popular online resource for Big data problems and solutions — methods based on decision trees and their ensembles are most widely used for solving the problems. It can be noted that the decision trees and convolutional neural networks are not very popular in Chemometrics. One of the reasons...... for that is the landscape of the data matrix: the modern machine learning methods need number of measurements much larger than the number of variables to avoid overfitting, which is opposite to the layout of the data we usually deal with. Another drawback is a lack of interactive instruments for exploring...

  4. Ensemble clustering in deterministic ensemble Kalman filters

    Directory of Open Access Journals (Sweden)

    Javier Amezcua


    Full Text Available Ensemble clustering (EC can arise in data assimilation with ensemble square root filters (EnSRFs using non-linear models: an M-member ensemble splits into a single outlier and a cluster of M–1 members. The stochastic Ensemble Kalman Filter does not present this problem. Modifications to the EnSRFs by a periodic resampling of the ensemble through random rotations have been proposed to address it. We introduce a metric to quantify the presence of EC and present evidence to dispel the notion that EC leads to filter failure. Starting from a univariate model, we show that EC is not a permanent but transient phenomenon; it occurs intermittently in non-linear models. We perform a series of data assimilation experiments using a standard EnSRF and a modified EnSRF by a resampling though random rotations. The modified EnSRF thus alleviates issues associated with EC at the cost of traceability of individual ensemble trajectories and cannot use some of algorithms that enhance performance of standard EnSRF. In the non-linear regimes of low-dimensional models, the analysis root mean square error of the standard EnSRF slowly grows with ensemble size if the size is larger than the dimension of the model state. However, we do not observe this problem in a more complex model that uses an ensemble size much smaller than the dimension of the model state, along with inflation and localisation. Overall, we find that transient EC does not handicap the performance of the standard EnSRF.

  5. Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Xun Chen


    Full Text Available Electroencephalogram (EEG recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD with multiset canonical correlation analysis (MCCA. We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications.

  6. Extracting the Neural Representation of Tone Onsets for Separate Voices of Ensemble Music Using Multivariate EEG Analysis

    DEFF Research Database (Denmark)

    Sturm, Irene; Treder, Matthias S.; Miklody, Daniel


    responses to tone onsets, such as N1/P2 ERP components. Music clips (resembling minimalistic electro-pop) were presented to 11 subjects, either in an ensemble version (drums, bass, keyboard) or in the corresponding three solo versions. For each instrument we train a spatio-temporal regression filter......When listening to ensemble music even non-musicians can follow single instruments effortlessly. Electrophysiological indices for neural sensory encoding of separate streams have been described using oddball paradigms which utilize brain reactions to sound events that deviate from a repeating...... standard pattern. Obviously, these paradigms put constraints on the compositional complexity of the musical stimulus. Here, we apply a regression-based method of multivariate EEG analysis in order to reveal the neural encoding of separate voices of naturalistic ensemble music that is based on cortical...

  7. NYYD Ensemble

    Index Scriptorium Estoniae


    NYYD Ensemble'i duost Traksmann - Lukk E.-S. Tüüri teosega "Symbiosis", mis on salvestatud ka hiljuti ilmunud NYYD Ensemble'i CDle. 2. märtsil Rakvere Teatri väikeses saalis ja 3. märtsil Rotermanni Soolalaos, kavas Tüür, Kaumann, Berio, Reich, Yun, Hauta-aho, Buckinx

  8. Dynamics and Predictability of Hurricane Humberto (2007) Revealed from Ensemble Analysis and Forecasting (United States)

    Sippel, Jason A.; Zhang, Fuqing


    This study uses short-range ensemble forecasts initialized with an Ensemble-Kalman filter to study the dynamics and predictability of Hurricane Humberto, which made landfall along the Texas coast in 2007. Statistical correlation is used to determine why some ensemble members strengthen the incipient low into a hurricane and others do not. It is found that deep moisture and high convective available potential energy (CAPE) are two of the most important factors for the genesis of Humberto. Variations in CAPE result in as much difference (ensemble spread) in the final hurricane intensity as do variations in deep moisture. CAPE differences here are related to the interaction between the cyclone and a nearby front, which tends to stabilize the lower troposphere in the vicinity of the circulation center. This subsequently weakens convection and slows genesis. Eventually the wind-induced surface heat exchange mechanism and differences in landfall time result in even larger ensemble spread. 1

  9. Statistical analysis of time-resolved emission from ensembles of semiconductor quantum dots: interpretations of exponantial decay models

    NARCIS (Netherlands)

    van Driel, A.F.; Nikolaev, I.; Vergeer, P.; Lodahl, P.; Vanmaekelbergh, D.; Vos, Willem L.


    We present a statistical analysis of time-resolved spontaneous emission decay curves from ensembles of emitters, such as semiconductor quantum dots, with the aim of interpreting ubiquitous non-single-exponential decay. Contrary to what is widely assumed, the density of excited emitters and the

  10. Ensemble habitat mapping of invasive plant species (United States)

    Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.


    Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.


    Directory of Open Access Journals (Sweden)

    M. Balamurugan


    Full Text Available In Ensemble classifiers, the Combination of multiple prediction models of classifiers is important for making progress in a variety of difficult prediction problems. Ensemble of classifiers proved potential in getting higher accuracy compared to single classifier. Even though by the usage ensemble classifiers, still there is in-need to improve its performance. There are many possible ways available to increase the performance of ensemble classifiers. One of the ways is sampling, which plays a major role for improving the quality of ensemble classifier. Since, it helps in reducing the bias in input data set of ensemble. Sampling is the process of extracting the subset of samples from the original dataset. In this research work, analysis is done on sampling techniques for ensemble classifiers. In ensemble classifier, specifically one of the probability based sampling techniques is being always used. Samples are gathered in a process which gives all the individuals in the population of equal chances, such that, sampling bias is removed. In this paper, analyse the performance of ensemble classifiers by using various sampling techniques and list out their drawbacks.

  12. Ensemble Data Mining Methods (United States)

    Oza, Nikunj C.


    Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  13. Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis (United States)

    Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin


    A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in fault diagnosis for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.

  14. Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis

    International Nuclear Information System (INIS)

    Wu, Yunfeng; Yang, Shanshan; Zheng, Fang; Cai, Suxian; Lu, Meng; Wu, Meihong


    High-resolution knee joint vibroarthrographic (VAG) signals can help physicians accurately evaluate the pathological condition of a degenerative knee joint, in order to prevent unnecessary exploratory surgery. Artifact cancellation is vital to preserve the quality of VAG signals prior to further computer-aided analysis. This paper describes a novel method that effectively utilizes ensemble empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA) algorithms for the removal of baseline wander and white noise in VAG signal processing. The EEMD method first successively decomposes the raw VAG signal into a set of intrinsic mode functions (IMFs) with fast and low oscillations, until the monotonic baseline wander remains in the last residue. Then, the DFA algorithm is applied to compute the fractal scaling index parameter for each IMF, in order to identify the anti-correlation and the long-range correlation components. Next, the DFA algorithm can be used to identify the anti-correlated and the long-range correlated IMFs, which assists in reconstructing the artifact-reduced VAG signals. Our experimental results showed that the combination of EEMD and DFA algorithms was able to provide averaged signal-to-noise ratio (SNR) values of 20.52 dB (standard deviation: 1.14 dB) and 20.87 dB (standard deviation: 1.89 dB) for 45 normal signals in healthy subjects and 20 pathological signals in symptomatic patients, respectively. The combination of EEMD and DFA algorithms can ameliorate the quality of VAG signals with great SNR improvements over the raw signal, and the results were also superior to those achieved by wavelet matching pursuit decomposition and time-delay neural filter. (paper)

  15. Investigating properties of the cardiovascular system using innovative analysis algorithms based on ensemble empirical mode decomposition. (United States)

    Yeh, Jia-Rong; Lin, Tzu-Yu; Chen, Yun; Sun, Wei-Zen; Abbod, Maysam F; Shieh, Jiann-Shing


    Cardiovascular system is known to be nonlinear and nonstationary. Traditional linear assessments algorithms of arterial stiffness and systemic resistance of cardiac system accompany the problem of nonstationary or inconvenience in practical applications. In this pilot study, two new assessment methods were developed: the first is ensemble empirical mode decomposition based reflection index (EEMD-RI) while the second is based on the phase shift between ECG and BP on cardiac oscillation. Both methods utilise the EEMD algorithm which is suitable for nonlinear and nonstationary systems. These methods were used to investigate the properties of arterial stiffness and systemic resistance for a pig's cardiovascular system via ECG and blood pressure (BP). This experiment simulated a sequence of continuous changes of blood pressure arising from steady condition to high blood pressure by clamping the artery and an inverse by relaxing the artery. As a hypothesis, the arterial stiffness and systemic resistance should vary with the blood pressure due to clamping and relaxing the artery. The results show statistically significant correlations between BP, EEMD-based RI, and the phase shift between ECG and BP on cardiac oscillation. The two assessments results demonstrate the merits of the EEMD for signal analysis.

  16. Dysphonic Voice Pattern Analysis of Patients in Parkinson’s Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods

    Directory of Open Access Journals (Sweden)

    Yunfeng Wu


    Full Text Available Analysis of quantified voice patterns is useful in the detection and assessment of dysphonia and related phonation disorders. In this paper, we first study the linear correlations between 22 voice parameters of fundamental frequency variability, amplitude variations, and nonlinear measures. The highly correlated vocal parameters are combined by using the linear discriminant analysis method. Based on the probability density functions estimated by the Parzen-window technique, we propose an interclass probability risk (ICPR method to select the vocal parameters with small ICPR values as dominant features and compare with the modified Kullback-Leibler divergence (MKLD feature selection approach. The experimental results show that the generalized logistic regression analysis (GLRA, support vector machine (SVM, and Bagging ensemble algorithm input with the ICPR features can provide better classification results than the same classifiers with the MKLD selected features. The SVM is much better at distinguishing normal vocal patterns with a specificity of 0.8542. Among the three classification methods, the Bagging ensemble algorithm with ICPR features can identify 90.77% vocal patterns, with the highest sensitivity of 0.9796 and largest area value of 0.9558 under the receiver operating characteristic curve. The classification results demonstrate the effectiveness of our feature selection and pattern analysis methods for dysphonic voice detection and measurement.

  17. Sea surface temperature predictions using a multi-ocean analysis ensemble scheme (United States)

    Zhang, Ying; Zhu, Jieshun; Li, Zhongxian; Chen, Haishan; Zeng, Gang


    This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982-2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Niño and Atlantic Niño regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions.

  18. Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review. (United States)

    Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R


    Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NB SW =NB BI-GRAM =SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as

  19. A Link-Based Cluster Ensemble Approach For Improved Gene Expression Data Analysis

    Directory of Open Access Journals (Sweden)



    Full Text Available Abstract It is difficult from possibilities to select a most suitable effective way of clustering algorithm and its dataset for a defined set of gene expression data because we have a huge number of ways and huge number of gene expressions. At present many researchers are preferring to use hierarchical clustering in different forms this is no more totally optimal. Cluster ensemble research can solve this type of problem by automatically merging multiple data partitions from a wide range of different clusterings of any dimensions to improve both the quality and robustness of the clustering result. But we have many existing ensemble approaches using an association matrix to condense sample-cluster and co-occurrence statistics and relations within the ensemble are encapsulated only at raw level while the existing among clusters are totally discriminated. Finding these missing associations can greatly expand the capability of those ensemble methodologies for microarray data clustering. We propose general K-means cluster ensemble approach for the clustering of general categorical data into required number of partitions.

  20. Analysis of Commercially Available Firefighting Helmet and Boot Options for the Joint Firefighter Integrated Response Ensemble (JFIRE) (United States)


    from the top, while the Targa 0086showed an increase in both measurements due to its motorcycle helmet -like design. None of the helmets met the RCM...AFRL-RX-TY-TR-2012-0022 ANALYSIS OF COMMERCIALLY AVAILABLE HELMET AND BOOT OPTIONS FOR THE JOINT FIREFIGHTER INTEGRATED RESPONSE ENSEMBLE...should be aware that notwithstanding any other provision of law , no person shall be subject to any penalty for failing to comply with a collection of

  1. An Ensemble Analysis of Antarctic Glacial Isostatic Adjustment and Sea Level (United States)

    Lecavalier, B.; Tarasov, L.


    Inferences of past ice sheet evolution that lack any uncertainty assessment (implicit or explicit), have little value. A developing technique for explicit uncertainty quantification of glacial systems is Bayesian calibration of models against large observational data-sets (Tarasov et al., 2012). The foundation for a Bayesian calibration of a 3D glacial systems model (GSM) for Antarctica has recently been completed (Briggs et al., 2013; 2014; Briggs and Tarasov, 2013). Bayesian calibration thoroughly samples model uncertainties against fits to observational data to generate a probability distribution for the Antarctic Ice Sheet deglaciation with explicit and well-defined confidence intervals. To have validity as a complete inference of past ice sheet evolution, Bayesian calibration requires a model that "brackets reality".Past work has shown the GSM to have likely inadequate range of grounding line migration in certain sectors as well as persistent ice thickness biases in topographically complex regions (Briggs et al., 2014). To advance towards full calibration, these deficiencies are being addressed through a number of model developments. The grounding line scheme has been revised (Pollard and DeConto, 2012), the horizontal resolution is increased to 20 km, and boundary conditions are updated. The basal drag representation now includes the sub-grid treatment of the thermo-mechanical impacts of high basal roughness. Parametric uncertainties in basal drag for regions that are presently marine have been re-evaluated. The impact of past changes in ocean temperature on sub ice shelf melt is explicitly incorporated in the current ocean forcing parametric scheme. Uncertainties in earth rheology are also probed to robustly quantify uncertainties affiliated with glacial isostatic adjustment. The ensemble analysis of the Antarctic glacial system provides dynamical bounds on past and present Antarctica glacial isostatic adjustment and sea level contributions. This research

  2. Analysis of ensemble learning using simple perceptrons based on online learning theory (United States)

    Miyoshi, Seiji; Hara, Kazuyuki; Okada, Masato


    Ensemble learning of K nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. One purpose of statistical learning theory is to theoretically obtain the generalization error. This paper shows that ensemble generalization error can be calculated by using two order parameters, that is, the similarity between a teacher and a student, and the similarity among students. The differential equations that describe the dynamical behaviors of these order parameters are derived in the case of general learning rules. The concrete forms of these differential equations are derived analytically in the cases of three well-known rules: Hebbian learning, perceptron learning, and AdaTron (adaptive perceptron) learning. Ensemble generalization errors of these three rules are calculated by using the results determined by solving their differential equations. As a result, these three rules show different characteristics in their affinity for ensemble learning, that is “maintaining variety among students.” Results show that AdaTron learning is superior to the other two rules with respect to that affinity.

  3. Spin–Orbit Alignment of Exoplanet Systems: Ensemble Analysis Using Asteroseismology

    DEFF Research Database (Denmark)

    Campante, T. L.; Lund, M. N.; Kuszlewicz, James S.


    The angle ψ between a planet’s orbital axis and the spin axis of its parent star is an important diagnostic of planet formation, migration, and tidal evolution. We seek empirical constraints on ψ by measuring the stellar inclination i s via asteroseismology for an ensemble of 25 solar-type hosts ...

  4. An Efficient Ensemble Learning Method for Gene Microarray Classification

    Directory of Open Access Journals (Sweden)

    Alireza Osareh


    Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  5. Ensemble methods for handwritten digit recognition

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Liisberg, Christian; Salamon, P.


    Neural network ensembles are applied to handwritten digit recognition. The individual networks of the ensemble are combinations of sparse look-up tables (LUTs) with random receptive fields. It is shown that the consensus of a group of networks outperforms the best individual of the ensemble...

  6. Using precipitation data ensemble for uncertainty analysis in SWAT streamflow simulation (United States)

    Strauch, Michael; Bernhofer, Christian; Koide, Sérgio; Volk, Martin; Lorz, Carsten; Makeschin, Franz


    SummaryPrecipitation patterns in the tropics are characterized by extremely high spatial and temporal variability that are difficult to adequately represent with rain gauge networks. Since precipitation is commonly the most important input data in hydrological models, model performance and uncertainty will be negatively impacted in areas with sparse rain gauge networks. To investigate the influence of precipitation uncertainty on both model parameters and predictive uncertainty in a data sparse region, the integrated river basin model SWAT was calibrated against measured streamflow of the Pipiripau River in Central Brazil. Calibration was conducted using an ensemble of different precipitation data sources, including: (1) point data from the only available rain gauge within the watershed, (2) a smoothed version of the gauge data derived using a moving average, (3) spatially distributed data using Thiessen polygons (which includes rain gauges from outside the watershed), and (4) Tropical Rainfall Measuring Mission radar data. For each precipitation input model, the best performing parameter set and their associated uncertainty ranges were determined using the Sequential Uncertainty Fitting Procedure. Although satisfactory streamflow simulations were generated with each precipitation input model, the results of our study indicate that parameter uncertainty varied significantly depending upon the method used for precipitation data-set generation. Additionally, improved deterministic streamflow predictions and more reliable probabilistic forecasts were generated using different ensemble-based methods, such as the arithmetic ensemble mean, and more advanced Bayesian Model Averaging schemes. This study shows that ensemble modeling with multiple precipitation inputs can considerably increase the level of confidence in simulation results, particularly in data-poor regions.

  7. Ensemble-sensitivity Analysis Based Observation Targeting for Mesoscale Convection Forecasts and Factors Influencing Observation-Impact Prediction (United States)

    Hill, A.; Weiss, C.; Ancell, B. C.


    The basic premise of observation targeting is that additional observations, when gathered and assimilated with a numerical weather prediction (NWP) model, will produce a more accurate forecast related to a specific phenomenon. Ensemble-sensitivity analysis (ESA; Ancell and Hakim 2007; Torn and Hakim 2008) is a tool capable of accurately estimating the proper location of targeted observations in areas that have initial model uncertainty and large error growth, as well as predicting the reduction of forecast variance due to the assimilated observation. ESA relates an ensemble of NWP model forecasts, specifically an ensemble of scalar forecast metrics, linearly to earlier model states. A thorough investigation is presented to determine how different factors of the forecast process are impacting our ability to successfully target new observations for mesoscale convection forecasts. Our primary goals for this work are to determine: (1) If targeted observations hold more positive impact over non-targeted (i.e. randomly chosen) observations; (2) If there are lead-time constraints to targeting for convection; (3) How inflation, localization, and the assimilation filter influence impact prediction and realized results; (4) If there exist differences between targeted observations at the surface versus aloft; and (5) how physics errors and nonlinearity may augment observation impacts.Ten cases of dryline-initiated convection between 2011 to 2013 are simulated within a simplified OSSE framework and presented here. Ensemble simulations are produced from a cycling system that utilizes the Weather Research and Forecasting (WRF) model v3.8.1 within the Data Assimilation Research Testbed (DART). A "truth" (nature) simulation is produced by supplying a 3-km WRF run with GFS analyses and integrating the model forward 90 hours, from the beginning of ensemble initialization through the end of the forecast. Target locations for surface and radiosonde observations are computed 6, 12, and

  8. Ocean Ensemble Forecasting in the Navy Earth System Prediction Capability (United States)

    Rowley, C. D.; Hogan, P. J.; Frolov, S.; Wei, M.; Thoppil, P. G.; Smedstad, O. M.; Barton, N. P.; Bishop, C. H.


    An extended range ensemble forecast system is being developed in the US Navy Earth System Prediction Capability (ESPC). A global ocean ensemble generation capability to support the coupled ESPC ensemble forecast has been developed, and initial assessments are underway. The ocean ensemble generation is based on a perturbed-observation analysis developed for the Navy Coupled Ocean Data Assimilation system (NCODA). The resulting analysis perturbations are used to represent uncertainty in the initial conditions of a global ocean forecast ensemble using the Hybrid Coordinate Ocean Model (HYCOM). For cycling with HYCOM, the NCODA system performs a 3D variational analysis of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. Sea surface height is assimilated through synthetic temperature and salinity profiles generated using the Modular Ocean Data Assimilation System (MODAS) historical regression database with surface height and surface temperature as inputs. Perturbations to the surface and profile observations use random samples from a normal distribution scaled by the observation error standard deviation, which combines estimates of instrument and representation error. Perturbations to the synthetic profiles are generated by supplying the perturbed surface inputs to the MODAS system, resulting in correlated profile changes with vertical correlations associated with historical uncertainty about thermocline depth and gradients. Initial results from a cycling global analysis show the analysis perturbations have scales and amplitudes consistent with short term forecast error covariances, and improve measures of ensemble forecast skill regionally and globally. Assessments of the global ocean ensemble forecast skill using the perturbed observation analysis will be presented

  9. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Haakon


    The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

  10. Stochastic resonance of ensemble neurons for transient spike trains: Wavelet analysis

    International Nuclear Information System (INIS)

    Hasegawa, Hideo


    By using the wavelet transformation (WT), I have analyzed the response of an ensemble of N (=1, 10, 100, and 500) Hodgkin-Huxley neurons to transient M-pulse spike trains (M=1 to 3) with independent Gaussian noises. The cross correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the denoising method within the WT, by which the noise contribution is extracted from the output signals. Although the response of a single (N=1) neuron to subthreshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross correlation and SNR is shown to be much improved by increasing the value of N: a population of neurons plays an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for suprathreshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for subthreshold inputs

  11. Statistical analysis of time-resolved emission from ensembles of semiconductor quantum dots: Interpretation of exponential decay models

    DEFF Research Database (Denmark)

    Van Driel, A.F.; Nikolaev, I.S.; Vergeer, P.


    We present a statistical analysis of time-resolved spontaneous emission decay curves from ensembles of emitters, such as semiconductor quantum dots, with the aim of interpreting ubiquitous non-single-exponential decay. Contrary to what is widely assumed, the density of excited emitters and the in......We present a statistical analysis of time-resolved spontaneous emission decay curves from ensembles of emitters, such as semiconductor quantum dots, with the aim of interpreting ubiquitous non-single-exponential decay. Contrary to what is widely assumed, the density of excited emitters...... decay component is multiplied by its radiative decay rate. A central result of our paper is the derivation of the emission decay curve when both radiative and nonradiative decays are independently distributed. In this case, the well-known emission quantum efficiency can no longer be expressed...... by a single number, but is also distributed. We derive a practical description of non-single-exponential emission decay curves in terms of a single distribution of decay rates; the resulting distribution is identified as the distribution of total decay rates weighted with the radiative rates. We apply our...

  12. A Flexible Approach for the Statistical Visualization of Ensemble Data

    Energy Technology Data Exchange (ETDEWEB)

    Potter, K. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute; Wilson, A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bremer, P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, V. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute; Johnson, C. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute


    Scientists are increasingly moving towards ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. We present a collection of overview and statistical displays linked through a high level of interactivity to provide a framework for gaining key scientific insight into the distribution of the simulation results as well as the uncertainty associated with the data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked statistical displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate this approach using driving problems from climate modeling and meteorology and discuss generalizations to other fields.

  13. [Removal Algorithm of Power Line Interference in Electrocardiogram Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition]. (United States)

    Zhao, Wei; Xiao, Shixiao; Zhang, Baocan; Huang, Xiaojing; You, Rongyi


    Electrocardiogram (ECG) signals are susceptible to be disturbed by 50 Hz power line interference (PLI) in the process of acquisition and conversion. This paper, therefore, proposes a novel PLI removal algorithm based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD). Firstly, according to the morphological differences in ECG waveform characteristics, the noisy ECG signal was decomposed into the mutated component, the smooth component and the residual component by MCA. Secondly, intrinsic mode functions (IMF) of PLI was filtered. The noise suppression rate (NSR) and the signal distortion ratio (SDR) were used to evaluate the effect of de-noising algorithm. Finally, the ECG signals were re-constructed. Based on the experimental comparison, it was concluded that the proposed algorithm had better filtering functions than the improved Levkov algorithm, because it could not only effectively filter the PLI, but also have smaller SDR value.

  14. Heat strain imposed by personal protective ensembles: quantitative analysis using a thermoregulation model (United States)

    Xu, Xiaojiang; Gonzalez, Julio A.; Santee, William R.; Blanchard, Laurie A.; Hoyt, Reed W.


    The objective of this paper is to study the effects of personal protective equipment (PPE) and specific PPE layers, defined as thermal/evaporative resistances and the mass, on heat strain during physical activity. A stepwise thermal manikin testing and modeling approach was used to analyze a PPE ensemble with four layers: uniform, ballistic protection, chemical protective clothing, and mask and gloves. The PPE was tested on a thermal manikin, starting with the uniform, then adding an additional layer in each step. Wearing PPE increases the metabolic rates (dot{M}) , thus dot{M} were adjusted according to the mass of each of four configurations. A human thermoregulatory model was used to predict endurance time for each configuration at fixed dot{M} and at its mass adjusted dot{M} . Reductions in endurance time due to resistances, and due to mass, were separately determined using predicted results. Fractional contributions of PPE's thermal/evaporative resistances by layer show that the ballistic protection and the chemical protective clothing layers contribute about 20 %, respectively. Wearing the ballistic protection over the uniform reduced endurance time from 146 to 75 min, with 31 min of the decrement due to the additional resistances of the ballistic protection, and 40 min due to increased dot{M} associated with the additional mass. Effects of mass on heat strain are of a similar magnitude relative to effects of increased resistances. Reducing resistances and mass can both significantly alleviate heat strain.

  15. Recovering a Representative Conformational Ensemble from Underdetermined Macromolecular Structural Data (United States)

    Berlin, Konstantin; Castañeda, Carlos A.; Schneidman-Duhovny, Dina; Sali, Andrej; Nava-Tudela, Alfredo; Fushman, David


    Structural analysis of proteins and nucleic acids is complicated by their inherent flexibility, conferred, for example, by linkers between their contiguous domains. Therefore, the macromolecule needs to be represented by an ensemble of conformations instead of a single conformation. Determining this ensemble is challenging because the experimental data are a convoluted average of contributions from multiple conformations. As the number of the ensemble degrees of freedom generally greatly exceeds the number of independent observables, directly deconvolving experimental data into a representative ensemble is an ill-posed problem. Recent developments in sparse approximations and compressive sensing have demonstrated that useful information can be recovered from underdetermined (ill-posed) systems of linear equations by using sparsity regularization. Inspired by these advances, we designed Sparse Ensemble Selection (SES) method for recovering multiple conformations from a limited number of observations. SES is more general and accurate than previously published minimum-ensemble methods, and we use it to obtain representative conformational ensembles of Lys48-linked di-ubiquitin, characterized by the residual dipolar coupling data measured at several pH conditions. These representative ensembles are validated against NMR chemical shift perturbation data and compared to maximum-entropy results. The SES method reproduced and quantified the previously observed pH dependence of the major conformation of Lys48-linked di-ubiquitin, and revealed lesser-populated conformations that are pre-organized for binding known di-ubiquitin receptors, thus providing insights into possible mechanisms of receptor recognition by polyubiquitin. SES is applicable to any experimental observables that can be expressed as a weighted linear combination of data for individual states. PMID:24093873

  16. A study of fuzzy logic ensemble system performance on face recognition problem (United States)

    Polyakova, A.; Lipinskiy, L.


    Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

  17. Comparative Analysis of Upper Ocean Heat Content Variability from Ensemble Operational Ocean Analyses (United States)

    Xue, Yan; Balmaseda, Magdalena A.; Boyer, Tim; Ferry, Nicolas; Good, Simon; Ishikawa, Ichiro; Rienecker, Michele; Rosati, Tony; Yin, Yonghong; Kumar, Arun


    Upper ocean heat content (HC) is one of the key indicators of climate variability on many time-scales extending from seasonal to interannual to long-term climate trends. For example, HC in the tropical Pacific provides information on thermocline anomalies that is critical for the longlead forecast skill of ENSO. Since HC variability is also associated with SST variability, a better understanding and monitoring of HC variability can help us understand and forecast SST variability associated with ENSO and other modes such as Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Tropical Atlantic Variability (TAV) and Atlantic Multidecadal Oscillation (AMO). An accurate ocean initialization of HC anomalies in coupled climate models could also contribute to skill in decadal climate prediction. Errors, and/or uncertainties, in the estimation of HC variability can be affected by many factors including uncertainties in surface forcings, ocean model biases, and deficiencies in data assimilation schemes. Changes in observing systems can also leave an imprint on the estimated variability. The availability of multiple operational ocean analyses (ORA) that are routinely produced by operational and research centers around the world provides an opportunity to assess uncertainties in HC analyses, to help identify gaps in observing systems as they impact the quality of ORAs and therefore climate model forecasts. A comparison of ORAs also gives an opportunity to identify deficiencies in data assimilation schemes, and can be used as a basis for development of real-time multi-model ensemble HC monitoring products. The OceanObs09 Conference called for an intercomparison of ORAs and use of ORAs for global ocean monitoring. As a follow up, we intercompared HC variations from ten ORAs -- two objective analyses based on in-situ data only and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability and longterm trend of HC have

  18. Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis (United States)

    Safavi, Hamid R.; Sajjadi, Sayed Mahdi; Raghibi, Vahid


    Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971-2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015-2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.

  19. The diffuse ensemble filter

    Directory of Open Access Journals (Sweden)

    X. Yang


    Full Text Available A new class of ensemble filters, called the Diffuse Ensemble Filter (DEnF, is proposed in this paper. The DEnF assumes that the forecast errors orthogonal to the first guess ensemble are uncorrelated with the latter ensemble and have infinite variance. The assumption of infinite variance corresponds to the limit of "complete lack of knowledge" and differs dramatically from the implicit assumption made in most other ensemble filters, which is that the forecast errors orthogonal to the first guess ensemble have vanishing errors. The DEnF is independent of the detailed covariances assumed in the space orthogonal to the ensemble space, and reduces to conventional ensemble square root filters when the number of ensembles exceeds the model dimension. The DEnF is well defined only in data rich regimes and involves the inversion of relatively large matrices, although this barrier might be circumvented by variational methods. Two algorithms for solving the DEnF, namely the Diffuse Ensemble Kalman Filter (DEnKF and the Diffuse Ensemble Transform Kalman Filter (DETKF, are proposed and found to give comparable results. These filters generally converge to the traditional EnKF and ETKF, respectively, when the ensemble size exceeds the model dimension. Numerical experiments demonstrate that the DEnF eliminates filter collapse, which occurs in ensemble Kalman filters for small ensemble sizes. Also, the use of the DEnF to initialize a conventional square root filter dramatically accelerates the spin-up time for convergence. However, in a perfect model scenario, the DEnF produces larger errors than ensemble square root filters that have covariance localization and inflation. For imperfect forecast models, the DEnF produces smaller errors than the ensemble square root filter with inflation. These experiments suggest that the DEnF has some advantages relative to the ensemble square root filters in the regime of small ensemble size, imperfect model, and copious

  20. An improved statistical analysis for predicting the critical temperature and critical density with Gibbs ensemble Monte Carlo simulation. (United States)

    Messerly, Richard A; Rowley, Richard L; Knotts, Thomas A; Wilding, W Vincent


    A rigorous statistical analysis is presented for Gibbs ensemble Monte Carlo simulations. This analysis reduces the uncertainty in the critical point estimate when compared with traditional methods found in the literature. Two different improvements are recommended due to the following results. First, the traditional propagation of error approach for estimating the standard deviations used in regression improperly weighs the terms in the objective function due to the inherent interdependence of the vapor and liquid densities. For this reason, an error model is developed to predict the standard deviations. Second, and most importantly, a rigorous algorithm for nonlinear regression is compared to the traditional approach of linearizing the equations and propagating the error in the slope and the intercept. The traditional regression approach can yield nonphysical confidence intervals for the critical constants. By contrast, the rigorous algorithm restricts the confidence regions to values that are physically sensible. To demonstrate the effect of these conclusions, a case study is performed to enhance the reliability of molecular simulations to resolve the n-alkane family trend for the critical temperature and critical density.

  1. A comparative research of different ensemble surrogate models based on set pair analysis for the DNAPL-contaminated aquifer remediation strategy optimization (United States)

    Hou, Zeyu; Lu, Wenxi; Xue, Haibo; Lin, Jin


    Surrogate-based simulation-optimization technique is an effective approach for optimizing the surfactant enhanced aquifer remediation (SEAR) strategy for clearing DNAPLs. The performance of the surrogate model, which is used to replace the simulation model for the aim of reducing computation burden, is the key of corresponding researches. However, previous researches are generally based on a stand-alone surrogate model, and rarely make efforts to improve the approximation accuracy of the surrogate model to the simulation model sufficiently by combining various methods. In this regard, we present set pair analysis (SPA) as a new method to build ensemble surrogate (ES) model, and conducted a comparative research to select a better ES modeling pattern for the SEAR strategy optimization problems. Surrogate models were developed using radial basis function artificial neural network (RBFANN), support vector regression (SVR), and Kriging. One ES model is assembling RBFANN model, SVR model, and Kriging model using set pair weights according their performance, and the other is assembling several Kriging (the best surrogate modeling method of three) models built with different training sample datasets. Finally, an optimization model, in which the ES model was embedded, was established to obtain the optimal remediation strategy. The results showed the residuals of the outputs between the best ES model and simulation model for 100 testing samples were lower than 1.5%. Using an ES model instead of the simulation model was critical for considerably reducing the computation time of simulation-optimization process and maintaining high computation accuracy simultaneously.

  2. Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

    International Nuclear Information System (INIS)

    Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo


    The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

  3. A Multimodel Ensemble Analysis of Global Changes in Plant Water Use Efficiency and Primary Productivity in the 21st Century (United States)

    Bernardes, S.


    Outputs from coupled carbon-climate models show considerable variability in atmospheric and land fields over the 21st century, including changes in temperature and in the spatiotemporal distribution and quantity of precipitation over the planet. Reductions in water availability due to decreased precipitation and increased water demand by the atmosphere may reduce carbon uptake by critical ecosystems. Conversely, increases in atmospheric carbon dioxide have the potential to offset reductions in productivity. This work focuses on predicted responses of plants to environmental changes and on how plants will adjust their water use efficiency (WUE, plant production per water loss by evapotranspiration) in the 21st century. Predicted changes in WUE were investigated using an ensemble of Earth System Models from the Coupled Model Intercomparison Project 5 (CMIP5), flux tower data and products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Scenarios for climate futures used two representative concentration pathways, including carbon concentration peak in 2040 (RCP4.5) and rising emissions throughout the 21st century (RCP8.5). Model results included the periods 2006-2009 (predicted) and 1850-2005 (reference). IPCC SREX regions were used to compare modeled, flux and satellite data and to address the significant intermodel variability observed for the CMIP5 ensemble (larger variability for RCP8.5, higher intermodel agreement in Southeast Asia, lower intermodel agreement in arid areas). An evaluation of model skill at the regional level supported model selection and the spatiotemporal analysis of changes in WUE. Departures of projected conditions in relation to historical values are presented for both concentration pathways at global, regional levels, including latitudinal distributions. High model sensitivity to different concentration pathways and increase in GPP and WUE was observed for most of the planet (increases consistently higher for

  4. Probing protein ensemble rigidity and hydrogen-deuterium exchange. (United States)

    Sljoka, Adnan; Wilson, Derek


    Protein rigidity and flexibility can be analyzed accurately and efficiently using the program floppy inclusion and rigid substructure topography (FIRST). Previous studies using FIRST were designed to analyze the rigidity and flexibility of proteins using a single static (snapshot) structure. It is however well known that proteins can undergo spontaneous sub-molecular unfolding and refolding, or conformational dynamics, even under conditions that strongly favor a well-defined native structure. These (local) unfolding events result in a large number of conformers that differ from each other very slightly. In this context, proteins are better represented as a thermodynamic ensemble of 'native-like' structures, and not just as a single static low-energy structure. Working with this notion, we introduce a novel FIRST-based approach for predicting rigidity/flexibility of the protein ensemble by (i) averaging the hydrogen bonding strengths from the entire ensemble and (ii) by refining the mathematical model of hydrogen bonds. Furthermore, we combine our FIRST-ensemble rigidity predictions with the ensemble solvent accessibility data of the backbone amides and propose a novel computational method which uses both rigidity and solvent accessibility for predicting hydrogen-deuterium exchange (HDX). To validate our predictions, we report a novel site specific HDX experiment which characterizes the native structural ensemble of Acylphosphatase from hyperthermophile Sulfolobus solfataricus (Sso AcP). The sub-structural conformational dynamics that is observed by HDX data, is closely matched with the FIRST-ensemble rigidity predictions, which could not be attained using the traditional single 'snapshot' rigidity analysis. Moreover, the computational predictions of regions that are protected from HDX and those that undergo exchange are in very good agreement with the experimental HDX profile of Sso AcP.

  5. Probing protein ensemble rigidity and hydrogen-deuterium exchange (United States)

    Sljoka, Adnan; Wilson, Derek


    Protein rigidity and flexibility can be analyzed accurately and efficiently using the program floppy inclusion and rigid substructure topography (FIRST). Previous studies using FIRST were designed to analyze the rigidity and flexibility of proteins using a single static (snapshot) structure. It is however well known that proteins can undergo spontaneous sub-molecular unfolding and refolding, or conformational dynamics, even under conditions that strongly favor a well-defined native structure. These (local) unfolding events result in a large number of conformers that differ from each other very slightly. In this context, proteins are better represented as a thermodynamic ensemble of ‘native-like’ structures, and not just as a single static low-energy structure. Working with this notion, we introduce a novel FIRST-based approach for predicting rigidity/flexibility of the protein ensemble by (i) averaging the hydrogen bonding strengths from the entire ensemble and (ii) by refining the mathematical model of hydrogen bonds. Furthermore, we combine our FIRST-ensemble rigidity predictions with the ensemble solvent accessibility data of the backbone amides and propose a novel computational method which uses both rigidity and solvent accessibility for predicting hydrogen-deuterium exchange (HDX). To validate our predictions, we report a novel site specific HDX experiment which characterizes the native structural ensemble of Acylphosphatase from hyperthermophile Sulfolobus solfataricus (Sso AcP). The sub-structural conformational dynamics that is observed by HDX data, is closely matched with the FIRST-ensemble rigidity predictions, which could not be attained using the traditional single ‘snapshot’ rigidity analysis. Moreover, the computational predictions of regions that are protected from HDX and those that undergo exchange are in very good agreement with the experimental HDX profile of Sso AcP.

  6. Combining morphological analysis and Bayesian networks for ...

    African Journals Online (AJOL)

    ... how these two computer aided methods may be combined to better facilitate modelling procedures. A simple example is presented, concerning a recent application in the field of environmental decision support. Keywords: Morphological analysis, Bayesian networks, strategic decision support. ORiON Vol. 23 (2) 2007: pp.

  7. Quantum ensembles of quantum classifiers. (United States)

    Schuld, Maria; Petruccione, Francesco


    Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

  8. A Statistical Description of Neural Ensemble Dynamics

    Directory of Open Access Journals (Sweden)

    John D Long


    Full Text Available The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets, providing new insights into how the brain mediates behavior. One limitation of these techniques is they do not provide information about the underlying anatomical connections among the recorded neurons within an ensemble. Moreover, the set of possible interactions grows exponentially with ensemble size. This limitation is at the heart of the challenge one confronts when interpreting these data. Several groups have attempted the challenging inverse problem of inferring the connectivity among the recorded neurons from ensemble data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track changes in the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility for describing the dynamics of ensemble data as they relate to behavior.

  9. World Music Ensemble: Kulintang (United States)

    Beegle, Amy C.


    As instrumental world music ensembles such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music ensembles just starting to gain popularity in particular parts of the United States. The kulintang ensemble, a drum and gong ensemble…

  10. Energy Analysis in Combined Reforming of Propane

    Directory of Open Access Journals (Sweden)

    K. Moon


    Full Text Available Combined (steam and CO2 reforming is one of the methods to produce syngas for different applications. An energy requirement analysis of steam reforming to dry reforming with intermediate steps of steam reduction and equivalent CO2 addition to the feed fuel for syngas generation has been done to identify condition for optimum process operation. Thermodynamic equilibrium data for combined reforming was generated for temperature range of 400–1000°C at 1 bar pressure and combined oxidant (CO2 + H2O stream to propane (fuel ratio of 3, 6, and 9 by employing the Gibbs free energy minimization algorithm of HSC Chemistry software 5.1. Total energy requirement including preheating and reaction enthalpy calculations were done using the equilibrium product composition. Carbon and methane formation was significantly reduced in combined reforming than pure dry reforming, while the energy requirements were lower than pure steam reforming. Temperatures of minimum energy requirement were found in the data analysis of combined reforming which were optimum for the process.

  11. Synchronized mammalian cell culture: part II--population ensemble modeling and analysis for development of reproducible processes. (United States)

    Jandt, Uwe; Barradas, Oscar Platas; Pörtner, Ralf; Zeng, An-Ping


    The consideration of inherent population inhomogeneities of mammalian cell cultures becomes increasingly important for systems biology study and for developing more stable and efficient processes. However, variations of cellular properties belonging to different sub-populations and their potential effects on cellular physiology and kinetics of culture productivity under bioproduction conditions have not yet been much in the focus of research. Culture heterogeneity is strongly determined by the advance of the cell cycle. The assignment of cell-cycle specific cellular variations to large-scale process conditions can be optimally determined based on the combination of (partially) synchronized cultivation under otherwise physiological conditions and subsequent population-resolved model adaptation. The first step has been achieved using the physical selection method of countercurrent flow centrifugal elutriation, recently established in our group for different mammalian cell lines which is presented in Part I of this paper series. In this second part, we demonstrate the successful adaptation and application of a cell-cycle dependent population balance ensemble model to describe and understand synchronized bioreactor cultivations performed with two model mammalian cell lines, AGE1.HNAAT and CHO-K1. Numerical adaptation of the model to experimental data allows for detection of phase-specific parameters and for determination of significant variations between different phases and different cell lines. It shows that special care must be taken with regard to the sampling frequency in such oscillation cultures to minimize phase shift (jitter) artifacts. Based on predictions of long-term oscillation behavior of a culture depending on its start conditions, optimal elutriation setup trade-offs between high cell yields and high synchronization efficiency are proposed. © 2014 American Institute of Chemical Engineers.

  12. Multinomial logistic regression ensembles. (United States)

    Lee, Kyewon; Ahn, Hongshik; Moon, Hojin; Kodell, Ralph L; Chen, James J


    This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model.

  13. Osmotic virial coefficients for model protein and colloidal solutions: Importance of ensemble constraints in the analysis of light scattering data (United States)

    Siderius, Daniel W.; Krekelberg, William P.; Roberts, Christopher J.; Shen, Vincent K.


    Protein-protein interactions in solution may be quantified by the osmotic second virial coefficient (OSVC), which can be measured by various experimental techniques including light scattering. Analysis of Rayleigh light scattering measurements from such experiments requires identification of a scattering volume and the thermodynamic constraints imposed on that volume, i.e., the statistical mechanical ensemble in which light scattering occurs. Depending on the set of constraints imposed on the scattering volume, one can obtain either an apparent OSVC, A2,app, or the true thermodynamic OSVC, {B_{22}^{osm}}, that is rigorously defined in solution theory [M. A. Blanco, E. Sahin, Y. Li, and C. J. Roberts, J. Chem. Phys. 134, 225103 (2011), 10.1063/1.3596726]. However, it is unclear to what extent A2,app and {B_{22}^{osm}} differ, which may have implications on the physical interpretation of OSVC measurements from light scattering experiments. In this paper, we use the multicomponent hard-sphere model and a well-known equation of state to directly compare A2,app and {B_{22}^{osm}}. Our results from the hard-sphere equation of state indicate that A2,app underestimates {B_{22}^{osm}}, but in a systematic manner that may be explained using fundamental thermodynamic expressions for the two OSVCs. The difference between A2,app and {B_{22}^{osm}} may be quantitatively significant, but may also be obscured in experimental application by statistical uncertainty or non-steric interactions. Consequently, the two OSVCs that arise in the analysis of light scattering measurements do formally differ, but in a manner that may not be detectable in actual application.

  14. Ensembles lexicaux

    DEFF Research Database (Denmark)

    Laursen, Bo


    In this article the author proposes a solution to the classical problem in European lexical semantics of delimiting lexical fields, a problem that most field-oriented semanticists involved in practical lexico-semantic analysis have found themselves confronted with. What are the criteria for saying...... on observations of the role that lexical fields play in discourse comprehension. This focus on the discoursal and interpretative functions of lexical fields constitute a new approach to the lexical field, a phenomenon which traditionally has been studied in a systems-oriented perspective....

  15. Control Flow Analysis for SF Combinator Calculus

    Directory of Open Access Journals (Sweden)

    Martin Lester


    Full Text Available Programs that transform other programs often require access to the internal structure of the program to be transformed. This is at odds with the usual extensional view of functional programming, as embodied by the lambda calculus and SK combinator calculus. The recently-developed SF combinator calculus offers an alternative, intensional model of computation that may serve as a foundation for developing principled languages in which to express intensional computation, including program transformation. Until now there have been no static analyses for reasoning about or verifying programs written in SF-calculus. We take the first step towards remedying this by developing a formulation of the popular control flow analysis 0CFA for SK-calculus and extending it to support SF-calculus. We prove its correctness and demonstrate that the analysis is invariant under the usual translation from SK-calculus into SF-calculus.

  16. Analysis and modeling of ensemble recordings from respiratory pre-motor neurons indicate changes in functional network architecture after acute hypoxia

    Directory of Open Access Journals (Sweden)

    Roberto F Galán


    Full Text Available We have combined neurophysiologic recording, statistical analysis, and computational modeling to investigate the dynamics of the respiratory network in the brainstem. Using a multielectrode array, we recorded ensembles of respiratory neurons in perfused in situ rat preparations that produce spontaneous breathing patterns, focusing on inspiratory pre-motor neurons. We compared firing rates and neuronal synchronization among these neurons before and after a brief hypoxic stimulus. We observed a significant decrease in the number of spikes after stimulation, in part due to a transient slowing of the respiratory pattern. However, the median interspike interval did not change, suggesting that the firing threshold of the neurons was not affected but rather the synaptic input was. A bootstrap analysis of synchrony between spike trains revealed that, both before and after brief hypoxia, up to 45 % (but typically less than 5 % of coincident spikes across neuronal pairs was not explained by chance. Most likely, this synchrony resulted from common synaptic input to the pre-motor population, an example of stochastic synchronization. After brief hypoxia most pairs were less synchronized, although some were more, suggesting that the respiratory network was “rewired” transiently after the stimulus. To investigate this hypothesis, we created a simple computational model with feed-forward divergent connections along the inspiratory pathway. Assuming that 1 the number of divergent projections was not the same for all presynaptic cells, but rather spanned a wide range and 2 that the stimulus increased inhibition at the top of the network; this model reproduced the reduction in firing rate and bootstrap-corrected synchrony subsequent to hypoxic stimulation observed in our experimental data.

  17. A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts (United States)

    Liu, P.


    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

  18. Ensemble method for dengue prediction. (United States)

    Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan


    In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

  19. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

    Directory of Open Access Journals (Sweden)

    Yoonsik Shim


    Full Text Available We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP. The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

  20. A statistical analysis of three ensembles of crop model responses totemperature and CO2concentration

    DEFF Research Database (Denmark)

    Makowski, D; Asseng, S; Ewert, F.


    Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data...

  1. Layered Ensemble Architecture for Time Series Forecasting. (United States)

    Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin


    Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

  2. Instantaneous phase difference analysis between thoracic and abdominal movement signals based on complementary ensemble empirical mode decomposition. (United States)

    Chen, Ya-Chen; Hsiao, Tzu-Chien


    Thoracoabdominal asynchrony is often adopted to discriminate respiratory diseases in clinics. Conventionally, Lissajous figure analysis is the most frequently used estimation of the phase difference in thoracoabdominal asynchrony. However, the temporal resolution of the produced results is low and the estimation error increases when the signals are not sinusoidal. Other previous studies have reported time-domain procedures with the use of band-pass filters for phase-angle estimation. Nevertheless, the band-pass filters need calibration for phase delay elimination. To improve the estimation, we propose a novel method (named as instantaneous phase difference) that is based on complementary ensemble empirical mode decomposition for estimating the instantaneous phase relation between measured thoracic wall movement and abdominal wall movement. To validate the proposed method, experiments on simulated time series and human-subject respiratory data with two breathing types (i.e., thoracic breathing and abdominal breathing) were conducted. Latest version of Lissajous figure analysis and automatic phase estimation procedure were compared. The simulation results show that the standard deviations of the proposed method were lower than those of two other conventional methods. The proposed method performed more accurately than the two conventional methods. For the human-subject respiratory data, the results of the proposed method are in line with those in the literature, and the correlation analysis result reveals that they were positively correlated with the results generated by the two conventional methods. Furthermore, the standard deviation of the proposed method was also the smallest. To summarize, this study proposes a novel method for estimating instantaneous phase differences. According to the findings from both the simulation and human-subject data, our approach was demonstrated to be effective. The method offers the following advantages: (1) improves the temporal


    Directory of Open Access Journals (Sweden)

    E. I. Tarlovskaya


    Full Text Available Aim. To pursue pharmacoeconomic analysis of two drug combinations of ACE inhibitor (enalapril and diuretic.Material and methods. Patients with arterial hypertension degree 2 and diabetes mellitus type 2 without ischemic heart disease (n=56 were included into the study. Blood pressure (BP dynamics and cost/effectiveness ratio were evaluated.Results. In group A (fixed combination of original enalapril/hydrochlorothiazide 61% of patients achieved target BP level with initial dose, and the rest 39% of patients – with double dose. In group B (non-fixed combination of generic enalapril/indapamide 60% of patients achieved the target BP with initial dose of drugs, 33% - with double dose of ACE inhibitor, and 7% - with additional amlodipine administration. In patients of group A systolic BP (SBP reduction was 45.82±1.23 mm Hg by the 12th week vs. 40.0±0.81 mm Hg in patients of group B; diastolic BP (DBP reduction was 22.47±1.05 mm Hg and 18.76±0.70 mm Hg, respectively, by the 12th week of treatment. In the first month of treatment costs of target BP achievement was 298.62 rubles per patient in group A, and 299.50 rubles – in group B; by the 12th week of treatment – 629.45 and 631.22 rubles, respectively. Costs of SBP and DBP reduction by 1 mm Hg during 12 weeks of therapy were 13 and 27 rubles per patient, respectively, in group A, and 16 and 34 rubles per patient, respectively, in group B.Conclusion. The original fixed combination (enalapril+hydrochlorothiazide proved to be more clinically effective and more cost effective in the treatment of hypertensive patients in comparison with the non-fixed combination of generic drugs (enalapril+indapamide.


    Directory of Open Access Journals (Sweden)

    E. I. Tarlovskaya


    Full Text Available Aim. To pursue pharmacoeconomic analysis of two drug combinations of ACE inhibitor (enalapril and diuretic.Material and methods. Patients with arterial hypertension degree 2 and diabetes mellitus type 2 without ischemic heart disease (n=56 were included into the study. Blood pressure (BP dynamics and cost/effectiveness ratio were evaluated.Results. In group A (fixed combination of original enalapril/hydrochlorothiazide 61% of patients achieved target BP level with initial dose, and the rest 39% of patients – with double dose. In group B (non-fixed combination of generic enalapril/indapamide 60% of patients achieved the target BP with initial dose of drugs, 33% - with double dose of ACE inhibitor, and 7% - with additional amlodipine administration. In patients of group A systolic BP (SBP reduction was 45.82±1.23 mm Hg by the 12th week vs. 40.0±0.81 mm Hg in patients of group B; diastolic BP (DBP reduction was 22.47±1.05 mm Hg and 18.76±0.70 mm Hg, respectively, by the 12th week of treatment. In the first month of treatment costs of target BP achievement was 298.62 rubles per patient in group A, and 299.50 rubles – in group B; by the 12th week of treatment – 629.45 and 631.22 rubles, respectively. Costs of SBP and DBP reduction by 1 mm Hg during 12 weeks of therapy were 13 and 27 rubles per patient, respectively, in group A, and 16 and 34 rubles per patient, respectively, in group B.Conclusion. The original fixed combination (enalapril+hydrochlorothiazide proved to be more clinically effective and more cost effective in the treatment of hypertensive patients in comparison with the non-fixed combination of generic drugs (enalapril+indapamide.

  5. A genetic ensemble approach for gene-gene interaction identification

    Directory of Open Access Journals (Sweden)

    Ho Joshua WK


    Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of

  6. Critical Listening in the Ensemble Rehearsal: A Community of Learners (United States)

    Bell, Cindy L.


    This article explores a strategy for engaging ensemble members in critical listening analysis of performances and presents opportunities for improving ensemble sound through rigorous dialogue, reflection, and attentive rehearsing. Critical listening asks ensemble members to draw on individual playing experience and knowledge to describe what they…

  7. How to Deal with Low-Resolution Target Structures: Using SAR, Ensemble Docking, Hydropathic Analysis, and 3D-QSAR to Definitively Map the αβ-Tubulin Colchicine Site (United States)

    Da, Chenxiao; Mooberry, Susan L.; Gupton, John T.; Kellogg, Glen E.


    αβ-tubulin colchicine site inhibitors (CSIs) from four scaffolds that we previously tested for antiproliferative activity were modeled to better understand their effect on microtubules. Docking models, constructed by exploiting the SAR of a pyrrole subset and HINT scoring, guided ensemble docking of all 59 compounds. This conformation set and two variants having progressively less structure knowledge were subjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses. The CoMFA+HINT model (docked alignment) showed the best statistics: leave-one-out q2 of 0.616, r2 of 0.949 and r2pred (internal test set) of 0.755. An external (tested in other laboratories) collection of 24 CSIs from eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity trends in 7/8 scaffolds for CoMFA+HINT (8/8 for CoMFA). The combination of SAR, ensemble docking, hydropathic analysis and 3D-QSAR provides an atomic-scale colchicine site model more consistent with a target structure resolution much higher than the ~3.6 Å available for αβ-tubulin. PMID:23961916

  8. Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations (United States)

    Ge, Cui; Wang, Jun; Reid, Jeffrey S.; Posselt, Derek J.; Xian, Peng; Hyer, Edward


    Atmospheric transport of smoke from equatorial Southeast Asian Maritime Continent (Indonesia, Singapore, and Malaysia) to the Philippines was recently verified by the first-ever measurement of aerosol composition in the region of the Sulu Sea from a research vessel named Vasco. However, numerical modeling of such transport can have large uncertainties due to the lack of observations for parameterization schemes and for describing fire emission and meteorology in this region. These uncertainties are analyzed here, for the first time, with an ensemble of 24 Weather Research and Forecasting model with Chemistry (WRF-Chem) simulations. The ensemble reproduces the time series of observed surface nonsea-salt PM2.5 concentrations observed from the Vasco vessel during 17-30 September 2011 and overall agrees with satellite (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS)) and Aerosol Robotic Network (AERONET) data. The difference of meteorology between National Centers for Environmental Prediction (NCEP's) Final (FNL) and European Center for Medium range Weather Forecasting (ECMWF's) ERA renders the biggest spread in the ensemble (up to 20 μg m-3 or 200% in surface PM2.5), with FNL showing systematically superior results. The second biggest uncertainty is from fire emissions; the 2 day maximum Fire Locating and Modelling of Burning Emissions (FLAMBE) emission is superior than the instantaneous one. While Grell-Devenyi (G3) and Betts-Miller-Janjić cumulus schemes only produce a difference of 3 μg m-3 of surface PM2.5 over the Sulu Sea, the ensemble mean agrees best with Climate Prediction Center (CPC) MORPHing (CMORPH)'s spatial distribution of precipitation. Simulation with FNL-G3, 2 day maximum FLAMBE, and 800 m injection height outperforms other ensemble members. Finally, the global transport model (Navy Aerosol Analysis and Prediction System (NAAPS)) outperforms all WRF

  9. Multilevel ensemble Kalman filter

    KAUST Repository

    Chernov, Alexey


    This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.

  10. Combining triggers in HEP data analysis

    International Nuclear Information System (INIS)

    Lendermann, Victor; Herbst, Michael; Krueger, Katja; Schultz-Coulon, Hans-Christian; Stamen, Rainer; Haller, Johannes


    Modern high-energy physics experiments collect data using dedicated complex multi-level trigger systems which perform an online selection of potentially interesting events. In general, this selection suffers from inefficiencies. A further loss of statistics occurs when the rate of accepted events is artificially scaled down in order to meet bandwidth constraints. An offline analysis of the recorded data must correct for the resulting losses in order to determine the original statistics of the analysed data sample. This is particularly challenging when data samples recorded by several triggers are combined. In this paper we present methods for the calculation of the offline corrections and study their statistical performance. Implications on building and operating trigger systems are discussed. (orig.)

  11. Malignancy and Abnormality Detection of Mammograms using Classifier Ensembling

    Directory of Open Access Journals (Sweden)

    Nawazish Naveed


    Full Text Available The breast cancer detection and diagnosis is a critical and complex procedure that demands high degree of accuracy. In computer aided diagnostic systems, the breast cancer detection is a two stage procedure. First, to classify the malignant and benign mammograms, while in second stage, the type of abnormality is detected. In this paper, we have developed a novel architecture to enhance the classification of malignant and benign mammograms using multi-classification of malignant mammograms into six abnormality classes. DWT (Discrete Wavelet Transformation features are extracted from preprocessed images and passed through different classifiers. To improve accuracy, results generated by various classifiers are ensembled. The genetic algorithm is used to find optimal weights rather than assigning weights to the results of classifiers on the basis of heuristics. The mammograms declared as malignant by ensemble classifiers are divided into six classes. The ensemble classifiers are further used for multiclassification using one-against-all technique for classification. The output of all ensemble classifiers is combined by product, median and mean rule. It has been observed that the accuracy of classification of abnormalities is more than 97% in case of mean rule. The Mammographic Image Analysis Society dataset is used for experimentation.

  12. Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique

    Directory of Open Access Journals (Sweden)

    Jan D. Keller


    Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.

  13. A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning. (United States)

    Olesen, Alexander Neergaard; Christensen, Julie A E; Sorensen, Helge B D; Jennum, Poul J


    Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen's kappa of 0.74 indicating substantial agreement between automatic and manual scoring.

  14. Design ensemble machine learning model for breast cancer diagnosis. (United States)

    Hsieh, Sheau-Ling; Hsieh, Sung-Huai; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei


    In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

  15. Ensemble manifold regularization. (United States)

    Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng


    We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.

  16. Dimensionality Reduction Through Classifier Ensembles (United States)

    Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)


    In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

  17. The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

    Directory of Open Access Journals (Sweden)

    Amir Givati


    Full Text Available This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October–April 1982–2010. The six National Multimodel Ensemble (NMME models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME. The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.

  18. Compositional analysis of multi-element magnetic nanoparticles with a combined NMR and TEM approach (United States)

    Gellesch, Markus; Hammerath, Franziska; Süß, Vicky; Haft, Marcel; Hampel, Silke; Wurmehl, Sabine; Büchner, Bernd


    The increasing interest in nanoscale materials goes hand in hand with the challenge to reliably characterize the chemical compositions and structural features of nanosized objects in order to relate those to their physical properties. Despite efforts, the analysis of the chemical composition of individual multi-element nanoparticles remains challenging—from the technical point of view as well as from the point of view of measurement statistics. Here, we demonstrate that zero-field solid-state nuclear magnetic resonance (NMR) complements local, single particle transmission electron microscopy (TEM) studies with information on a large assembly of chemically complex nanoparticles. The combination of both experimental techniques gives information on the local composition and structure and provides an excellent measurement statistic through the corresponding NMR ensemble measurement. This analytical approach is applicable to many kinds of magnetic materials and therefore may prove very versatile in the future research of particulate magnetic nanomaterials.

  19. Ensembl variation resources

    Directory of Open Access Journals (Sweden)

    Marin-Garcia Pablo


    Full Text Available Abstract Background The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics. Description The Ensembl project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the Ensembl variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within Ensembl. Conclusions Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The Ensembl variation resources are integrated into the Ensembl genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All Ensembl data is freely available at and from the public MySQL database server at

  20. Triticeae resources in Ensembl Plants. (United States)

    Bolser, Dan M; Kerhornou, Arnaud; Walts, Brandon; Kersey, Paul


    Recent developments in DNA sequencing have enabled the large and complex genomes of many crop species to be determined for the first time, even those previously intractable due to their polyploid nature. Indeed, over the course of the last 2 years, the genome sequences of several commercially important cereals, notably barley and bread wheat, have become available, as well as those of related wild species. While still incomplete, comparison with other, more completely assembled species suggests that coverage of genic regions is likely to be high. Ensembl Plants ( is an integrative resource organizing, analyzing and visualizing genome-scale information for important crop and model plants. Available data include reference genome sequence, variant loci, gene models and functional annotation. For variant loci, individual and population genotypes, linkage information and, where available, phenotypic information are shown. Comparative analyses are performed on DNA and protein sequence alignments. The resulting genome alignments and gene trees, representing the implied evolutionary history of the gene family, are made available for visualization and analysis. Driven by the case of bread wheat, specific extensions to the analysis pipelines and web interface have recently been developed to support polyploid genomes. Data in Ensembl Plants is accessible through a genome browser incorporating various specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These interfaces are consistent with those offered through the Ensembl interface for the genomes of non-plant species, including those of plant pathogens, pests and pollinators, facilitating the study of the plant in its environment. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

  1. Girsanov reweighting for path ensembles and Markov state models (United States)

    Donati, L.; Hartmann, C.; Keller, B. G.


    The sensitivity of molecular dynamics on changes in the potential energy function plays an important role in understanding the dynamics and function of complex molecules. We present a method to obtain path ensemble averages of a perturbed dynamics from a set of paths generated by a reference dynamics. It is based on the concept of path probability measure and the Girsanov theorem, a result from stochastic analysis to estimate a change of measure of a path ensemble. Since Markov state models (MSMs) of the molecular dynamics can be formulated as a combined phase-space and path ensemble average, the method can be extended to reweight MSMs by combining it with a reweighting of the Boltzmann distribution. We demonstrate how to efficiently implement the Girsanov reweighting in a molecular dynamics simulation program by calculating parts of the reweighting factor "on the fly" during the simulation, and we benchmark the method on test systems ranging from a two-dimensional diffusion process and an artificial many-body system to alanine dipeptide and valine dipeptide in implicit and explicit water. The method can be used to study the sensitivity of molecular dynamics on external perturbations as well as to reweight trajectories generated by enhanced sampling schemes to the original dynamics.

  2. Benchmarking Commercial Conformer Ensemble Generators. (United States)

    Friedrich, Nils-Ole; de Bruyn Kops, Christina; Flachsenberg, Florian; Sommer, Kai; Rarey, Matthias; Kirchmair, Johannes


    We assess and compare the performance of eight commercial conformer ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, and OMEGA) and one leading free algorithm, the distance geometry algorithm implemented in RDKit. The comparative study is based on a new version of the Platinum Diverse Dataset, a high-quality benchmarking dataset of 2859 protein-bound ligand conformations extracted from the PDB. Differences in the performance of commercial algorithms are much smaller than those observed for free algorithms in our previous study (J. Chem. Inf. 2017, 57, 529-539). For commercial algorithms, the median minimum root-mean-square deviations measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers are between 0.46 and 0.61 Å. Commercial conformer ensemble generators are characterized by their high robustness, with at least 99% of all input molecules successfully processed and few or even no substantial geometrical errors detectable in their output conformations. The RDKit distance geometry algorithm (with minimization enabled) appears to be a good free alternative since its performance is comparable to that of the midranked commercial algorithms. Based on a statistical analysis, we elaborate on which algorithms to use and how to parametrize them for best performance in different application scenarios.

  3. The semantic similarity ensemble

    Directory of Open Access Journals (Sweden)

    Andrea Ballatore


    Full Text Available Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach.

  4. Diversity of Aerosol Optical Thickness in analysis and forecasting modes of the models from the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME) (United States)

    Lynch, P.


    With the emergence of global aerosol models intended for operational forecasting use at global numerical weather prediction (NWP) centers, the International Cooperative for Aerosol Prediction (ICAP) was founded in 2010. One of the objectives of ICAP is to develop a global multi-model aerosol forecasting ensemble (ICAP-MME) for operational and basic research use. To increase the accuracy of aerosol forecasts, several of the NWP centers have incorporated assimilation of satellite and/or ground-based observations of aerosol optical thickness (AOT), the most widely available and evaluated aerosol parameter. The ICAP models are independent in their underlying meteorology, as well as aerosol sources, sinks, microphysics and chemistry. The diversity of aerosol representations in the aerosol forecast models results in differences in AOT. In addition, for models that include AOT assimilations, the diversity in assimilation methodology, the observed AOT data to be assimilated, and the pre-assimilation treatments of input data also leads to differences in the AOT analyses. Drawing from members of the ICAP latest generation of quasi-operational aerosol models, five day AOT forecasts and AOT analyses are analyzed from four multi-species models which have AOT assimilations: ECMWF, JMA, NASA GSFC/GMAO, and NRL/FNMOC. For forecast mode only, we also include the dust products from NOAA NGAC, BSC, and UK Met office in our analysis leading to a total of 7 dust models. AOT at 550nm from all models are validated at regionally representative Aerosol Robotic Network (AERONET) sites and a data assimilation grade multi-satellite aerosol analysis. These analyses are also compared with the recently developed AOT reanalysis at NRL. Here we will present the basic verification characteristics of the ICAP-MME, and identify regions of diversity between model analyses and forecasts. Notably, as in many other ensemble environments, the multi model ensemble consensus mean outperforms all of the

  5. Ensemble singular vectors and their use as additive inflation in EnKF

    Directory of Open Access Journals (Sweden)

    Shu-Chih Yang


    Full Text Available Given an ensemble of forecasts, it is possible to determine the leading ensemble singular vector (ESV, that is, the linear combination of the forecasts that, given the choice of the perturbation norm and forecast interval, will maximise the growth of the perturbations. Because the ESV indicates the directions of the fastest growing forecast errors, we explore the potential of applying the leading ESVs in ensemble Kalman filter (EnKF for correcting fast-growing errors. The ESVs are derived based on a quasi-geostrophic multi-level channel model, and data assimilation experiments are carried out under framework of the local ensemble transform Kalman filter. We confirm that even during the early spin-up starting with random initial conditions, the final ESVs of the first analysis with a 12-h window are strongly related to the background errors. Since initial ensemble singular vectors (IESVs grow much faster than Lyapunov Vectors (LVs, and the final ensemble singular vectors (FESVs are close to convergence to leading LVs, perturbations based on leading IESVs grow faster than those based on FESVs, and are therefore preferable as additive inflation. The IESVs are applied in the EnKF framework for constructing flow-dependent additive perturbations to inflate the analysis ensemble. Compared with using random perturbations as additive inflation, a positive impact from using ESVs is found especially in areas with large growing errors. When an EnKF is ‘cold-started’ from random perturbations and poor initial condition, results indicate that using the ESVs as additive inflation has the advantage of correcting large errors so that the spin-up of the EnKF can be accelerated.

  6. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance (United States)

    Ye, Qiang


    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  7. Robust ensemble-based multi-objective optimization

    NARCIS (Netherlands)

    Fonseca, R.M.; Stordahl, A.; Leeuwenburgh, O.; Van den Hof, P.M.J.; Jansen, J.D.


    We consider robust ensemble-based multi-objective optimization using a hierarchical switching algorithm for combined long-term and short term water flooding optimization. We apply a modified formulation of the ensemble gradient which results in improved performance compared to earlier formulations.

  8. Deterministic entanglement of Rydberg ensembles by engineered dissipation

    DEFF Research Database (Denmark)

    Dasari, Durga; Mølmer, Klaus


    We propose a scheme that employs dissipation to deterministically generate entanglement in an ensemble of strongly interacting Rydberg atoms. With a combination of microwave driving between different Rydberg levels and a resonant laser coupling to a short lived atomic state, the ensemble can be d...

  9. Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Meihong Wu


    Full Text Available Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence. In this paper, we computed the sample entropy (SampEn and average stride interval (ASI parameters to quantify the stride series of 50 gender-matched children participants in three age groups. We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively. Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-Whitney U test (p<0.01 in children of 3–14 years old, which indicates the stride irregularity has been significantly ameliorated with the body growth. The original and normalized ASI values are also significantly changing when comparing between any two groups of young (aged 3–5 years, middle (aged 6–8 years, and elder (aged 10–14 years children. Such results suggest that healthy children may better modulate their gait cadence rhythm with the development of their musculoskeletal and neurological systems. In addition, the AdaBoost.M2 and Bagging algorithms were used to effectively distinguish the children’s gait patterns. These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%, recall (≥0.8, and precision (≥0.8077.

  10. Ensemble Pulsar Time Scale (United States)

    Yin, Dong-shan; Gao, Yu-ping; Zhao, Shu-hong


    Millisecond pulsars can generate another type of time scale that is totally independent of the atomic time scale, because the physical mechanisms of the pulsar time scale and the atomic time scale are quite different from each other. Usually the pulsar timing observations are not evenly sampled, and the internals between two data points range from several hours to more than half a month. Further more, these data sets are sparse. All this makes it difficult to generate an ensemble pulsar time scale. Hence, a new algorithm to calculate the ensemble pulsar time scale is proposed. Firstly, a cubic spline interpolation is used to densify the data set, and make the intervals between data points uniform. Then, the Vondrak filter is employed to smooth the data set, and get rid of the high-frequency noises, and finally the weighted average method is adopted to generate the ensemble pulsar time scale. The newly released NANOGRAV (North American Nanohertz Observatory for Gravitational Waves) 9-year data set is used to generate the ensemble pulsar time scale. This data set includes the 9-year observational data of 37 millisecond pulsars observed by the 100-meter Green Bank telescope and the 305-meter Arecibo telescope. It is found that the algorithm used in this paper can reduce effectively the influence caused by the noises in pulsar timing residuals, and improve the long-term stability of the ensemble pulsar time scale. Results indicate that the long-term (> 1 yr) stability of the ensemble pulsar time scale is better than 3.4 × 10-15.

  11. Embedded feature ranking for ensemble MLP classifiers. (United States)

    Windeatt, Terry; Duangsoithong, Rakkrit; Smith, Raymond


    A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.

  12. Disease-associated mutations that alter the RNA structural ensemble.

    Directory of Open Access Journals (Sweden)

    Matthew Halvorsen


    Full Text Available Genome-wide association studies (GWAS often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs from the Human Gene Mutation Database (HGMD that map to the untranslated regions (UTRs of a gene. Rather than using minimum free energy approaches (e.g. mFold, we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, beta-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD, and Hypertension, we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5' UTRs of FTL and RB1 SNP-induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a "RiboSNitch," that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble.

  13. Setup Analysis: Combining SMED with Other Tools

    Directory of Open Access Journals (Sweden)

    Stadnicka Dorota


    Full Text Available The purpose of this paper is to propose the methodology for the setup analysis, which can be implemented mainly in small and medium enterprises which are not convinced to implement the setups development. The methodology was developed after the research which determined the problem. Companies still have difficulties with a long setup time. Many of them do nothing to decrease this time. A long setup is not a sufficient reason for companies to undertake any actions towards the setup time reduction. To encourage companies to implement SMED it is essential to make some analyses of changeovers in order to discover problems. The methodology proposed can really encourage the management to take a decision about the SMED implementation, and that was verified in a production company. The setup analysis methodology is made up of seven steps. Four of them concern a setups analysis in a chosen area of a company, such as a work stand which is a bottleneck with many setups. The goal is to convince the management to begin actions concerning the setups improvement. The last three steps are related to a certain setup and, there, the goal is to reduce a setup time and the risk of problems which can appear during the setup. In this paper, the tools such as SMED, Pareto analysis, statistical analysis, FMEA and other were used.

  14. Ensemble dispersion forecasting - Part 2. Application and evaluation

    DEFF Research Database (Denmark)

    Galmarini, S.; Bianconi, R.; Addis, R.


    of the dispersion of ETEX release 1 and the model ensemble is compared with the monitoring data. The scope of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results and represents a superior analysis of the process evolution. (C) 2004 Elsevier...

  15. Heat waves analysis over France in present and future climate: Application of a new method on the EURO-CORDEX ensemble

    Directory of Open Access Journals (Sweden)

    G. Ouzeau


    Full Text Available Currently, the analysis of heat waves and the representation of such events in a comprehensible and accessible way is a crucial challenge for climate services, in particular for delivering scientific support to policy makers. In order to fulfil this need, a new method for analysing the heat waves in France has been defined. Heat wave detection is based on the high quantiles of daily temperature distributions, and can be applied on any series of temperature. The heat waves are characterised by their duration, maximal temperature and global intensity. Their characteristics are calculated for historical and future climate based on the EURO-CORDEX regional multi-model ensemble, under two different Representative Concentration Pathway scenarios: RCP4.5 and RCP8.5. The historical simulations are evaluated against the SAFRAN reanalysis data. The EURO-CORDEX ensemble simulates heat waves which characteristics are consistent with the events detected from the SAFRAN thermal indicator between 1971 and 2005. Models are able to simulate waves as intense as the 2003 outstanding event. Under future climate conditions, whatever the considered scenario, the heat waves become more frequent and have higher mean duration and intensity. Moreover, heat waves could occur during a larger part of summer. The 2003 event corresponds to a typical event at the end of the century, and its duration and intensity are much lower than the strongest waves that could occur over the last 30 years of the 21st century. However, the intensity of the evolution during the end of the century will strongly depend on climate policies.

  16. Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses (United States)

    Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong


    Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums

  17. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter


    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  18. Predicting the Oxygen-Binding Properties of Platinum Nanoparticle Ensembles by Combining High-Precision Electron Microscopy and Density Functional Theory. (United States)

    Aarons, Jolyon; Jones, Lewys; Varambhia, Aakash; MacArthur, Katherine E; Ozkaya, Dogan; Sarwar, Misbah; Skylaris, Chris-Kriton; Nellist, Peter D


    Many studies of heterogeneous catalysis, both experimental and computational, make use of idealized structures such as extended surfaces or regular polyhedral nanoparticles. This simplification neglects the morphological diversity in real commercial oxygen reduction reaction (ORR) catalysts used in fuel-cell cathodes. Here we introduce an approach that combines 3D nanoparticle structures obtained from high-throughput high-precision electron microscopy with density functional theory. Discrepancies between experimental observations and cuboctahedral/truncated-octahedral particles are revealed and discussed using a range of widely used descriptors, such as electron-density, d-band centers, and generalized coordination numbers. We use this new approach to determine the optimum particle size for which both detrimental surface roughness and particle shape effects are minimized.

  19. A method for ensemble wildland fire simulation (United States)

    Mark A. Finney; Isaac C. Grenfell; Charles W. McHugh; Robert C. Seli; Diane Trethewey; Richard D. Stratton; Stuart Brittain


    An ensemble simulation system that accounts for uncertainty in long-range weather conditions and two-dimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis...

  20. Polynomial Chaos Based Acoustic Uncertainty Predictions from Ocean Forecast Ensembles (United States)

    Dennis, S.


    Most significant ocean acoustic propagation occurs at tens of kilometers, at scales small compared basin and to most fine scale ocean modeling. To address the increased emphasis on uncertainty quantification, for example transmission loss (TL) probability density functions (PDF) within some radius, a polynomial chaos (PC) based method is utilized. In order to capture uncertainty in ocean modeling, Navy Coastal Ocean Model (NCOM) now includes ensembles distributed to reflect the ocean analysis statistics. Since the ensembles are included in the data assimilation for the new forecast ensembles, the acoustic modeling uses the ensemble predictions in a similar fashion for creating sound speed distribution over an acoustically relevant domain. Within an acoustic domain, singular value decomposition over the combined time-space structure of the sound speeds can be used to create Karhunen-Loève expansions of sound speed, subject to multivariate normality testing. These sound speed expansions serve as a basis for Hermite polynomial chaos expansions of derived quantities, in particular TL. The PC expansion coefficients result from so-called non-intrusive methods, involving evaluation of TL at multi-dimensional Gauss-Hermite quadrature collocation points. Traditional TL calculation from standard acoustic propagation modeling could be prohibitively time consuming at all multi-dimensional collocation points. This method employs Smolyak order and gridding methods to allow adaptive sub-sampling of the collocation points to determine only the most significant PC expansion coefficients to within a preset tolerance. Practically, the Smolyak order and grid sizes grow only polynomially in the number of Karhunen-Loève terms, alleviating the curse of dimensionality. The resulting TL PC coefficients allow the determination of TL PDF normality and its mean and standard deviation. In the non-normal case, PC Monte Carlo methods are used to rapidly establish the PDF. This work was


    International Nuclear Information System (INIS)

    Van Saders, Jennifer L.; Gaudi, B. Scott


    Several photometric surveys for short-period transiting giant planets have targeted a number of open clusters, but no convincing detections have been reported. Although each individual survey typically targeted an insufficient number of stars to expect a detection assuming the frequency of short-period giant planets found in surveys of field stars, we ask whether the lack of detections from the ensemble of open cluster surveys is inconsistent with expectations from the field planet population. We select a subset of existing transit surveys with well-defined selection criteria and quantified detection efficiencies, and statistically combine their null results to show that the upper limits on the planet fraction are 5.5% and 1.4% for 1.0 R J and 1.5 R J planets, respectively, in the 3 J and 1.5 R J , respectively. Comparing these results to the frequency of short-period giant planets around field stars in both radial velocity and transit surveys, we conclude that there is no evidence to suggest that open clusters support a fundamentally different planet population than field stars, given the available data.

  2. Tailored Random Graph Ensembles

    International Nuclear Information System (INIS)

    Roberts, E S; Annibale, A; Coolen, A C C


    Tailored graph ensembles are a developing bridge between biological networks and statistical mechanics. The aim is to use this concept to generate a suite of rigorous tools that can be used to quantify and compare the topology of cellular signalling networks, such as protein-protein interaction networks and gene regulation networks. We calculate exact and explicit formulae for the leading orders in the system size of the Shannon entropies of random graph ensembles constrained with degree distribution and degree-degree correlation. We also construct an ergodic detailed balance Markov chain with non-trivial acceptance probabilities which converges to a strictly uniform measure and is based on edge swaps that conserve all degrees. The acceptance probabilities can be generalized to define Markov chains that target any alternative desired measure on the space of directed or undirected graphs, in order to generate graphs with more sophisticated topological features.

  3. Ensemble Data Fitting (United States)

    Perkins, A. L.; Zambo, S. J.; Elmore, P. A.


    In regions with sparse bathymetry, data learning algorithms have shown skill in recognizing dominant features such as seamounts and ridges. The structure of these features provides a means to impute data values to increase the resolution. When two different types of classifiers identify the same acreage - we have two possible interpretations of the sparse data. In this paper we construct an ensemble data fitting method, designed for sparse Bathymetric acreage that arbitrates between two competing nominal data categories. Each categorical data type leads to different data imputation interpretations. From these two interpretations, we construct an ensemble regression to minimize a weighted average of the two categorical interpretations. We demonstrate the method using an idealized Bathymetric data set from which two interpretations are possible.

  4. Development of modern folk and instrumental ensembles in China

    Directory of Open Access Journals (Sweden)

    Jiang Y.


    Full Text Available in the XX century the influence of traditional musical forms is becoming more diverse, in particular, the use of multimedia art forms. The Chinese as well as Western traditions and Ensemble Performance has undergone many changes, mutated forms of a combination of instruments, a creative approach to music, gradually breaking geographical boundaries, began to develop in the direction of further integration. In modern Chinese folk ensemble there is a combination of various Chinese folk instruments.

  5. A cost-minimization analysis of combination therapy in hypertension: fixed-dose vs extemporary combinations

    Directory of Open Access Journals (Sweden)

    Marco Bellone


    Full Text Available BACKGROUND: Cardiovascular disease management and prevention represent the leading cost driver in Italian healthcare expenditure. In order to reach the target blood pressure, a large majority of patients require simultaneous administration of multiple antihypertensive agents.OBJECTIVE: To assess the economic impact of the use of fixed dose combinations of antihypertensive agents, compared to the extemporary combination of the same principles.METHODS: A cost minimization analysis was conducted to determine the pharmaceutical daily cost of five fixed dose combinations (olmesartan 20 mg + amlodipine 5 mg, perindopril 5 mg + amlodipine 5 mg, enalapril 20 mg + lercanidipine 10 mg, felodipine 5 mg + ramipril 5 mg, and delapril 30 mg + manidipine 10 mg compared with extemporary combination of the same principles in the perspective of the Italian NHS. Daily acquisition costs are estimated based on current Italian prices and tariffs.RESULTS: In three cases the use of fixed‑dose combination instead of extemporary combination induces a lower daily cost. Fixed combination treatment with delapril 30 mg + manidipine 10 mg induces greater cost savings for the National Health System (95,47 €/pts/year, as compared to free drugs combination therapy.CONCLUSIONS: Compared with free drug combinations, fixed‑dose combinations of antihypertensive agents are associated with lower daily National Health Service acquisition costs.

  6. Exact analysis of Packet Reversed Packet Combining Scheme and Modified Packet Combining Scheme; and a combined scheme

    International Nuclear Information System (INIS)

    Bhunia, C.T.


    Packet combining scheme is a well defined simple error correction scheme for the detection and correction of errors at the receiver. Although it permits a higher throughput when compared to other basic ARQ protocols, packet combining (PC) scheme fails to correct errors when errors occur in the same bit locations of copies. In a previous work, a scheme known as Packet Reversed Packet Combining (PRPC) Scheme that will correct errors which occur at the same bit location of erroneous copies, was studied however PRPC does not handle a situation where a packet has more than 1 error bit. The Modified Packet Combining (MPC) Scheme that can correct double or higher bit errors was studied elsewhere. Both PRPC and MPC schemes are believed to offer higher throughput in previous studies, however neither adequate investigation nor exact analysis was done to substantiate this claim of higher throughput. In this work, an exact analysis of both PRPC and MPC is carried out and the results reported. A combined protocol (PRPC and MPC) is proposed and the analysis shows that it is capable of offering even higher throughput and better error correction capability at high bit error rate (BER) and larger packet size. (author)

  7. Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions

    Directory of Open Access Journals (Sweden)

    J. Dietrich


    Full Text Available Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by recombining members from different runs of these meteorological forecast systems. A subset of the super-ensemble is selected based on a priori model weights, which are obtained from ensemble calibration. Flood forecasts are simulated by the conceptual rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is represented by a parameter ensemble, which is a priori generated from a comprehensive uncertainty analysis during model calibration. The use of a computationally efficient hydrological model within a flood management system allows us to compute the hydro-meteorological model chain for all members of the sub-ensemble. The model chain is not re-computed before new ensemble forecasts are available, but the probabilistic assessment of the output is updated when new information from deterministic short range forecasts or from assimilation of measured data becomes available. For hydraulic modelling, with the desired result of a probabilistic inundation map with high spatial resolution, a replacement model can help to overcome computational limitations. A prototype of the developed framework has been applied for a case study in the Mulde river basin. However these techniques, in particular the probabilistic assessment and the derivation of decision rules are still in their infancy. Further research is necessary and promising.

  8. Exergy Analysis of Combined Cycle Power Plant: NTPC Dadri, India


    Tiwari, Arvind; Hasan, M; Islam, Mohd.


    The aim of the present paper is to exergy analysis of combined Brayton/Rankine power cycle of NTPC Dadri India. Theoretical exergy analysis is carried out for different components of dadri combined cycle power plant which consists of a gas turbine unit, heat recovery steam generator without extra fuel consumption and steam turbine unit. The results pinpoint that more exergy losses occurred in the gas turbine combustion chamber. Its reached 35% of the total exergy losses while the exergy losse...

  9. Recognizing stationary and locomotion activities using combinational of spectral analysis with statistical descriptors features (United States)

    Zainudin, M. N. Shah; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran


    Prior knowledge in pervasive computing recently garnered a lot of attention due to its high demand in various application domains. Human activity recognition (HAR) considered as the applications that are widely explored by the expertise that provides valuable information to the human. Accelerometer sensor-based approach is utilized as devices to undergo the research in HAR since their small in size and this sensor already build-in in the various type of smartphones. However, the existence of high inter-class similarities among the class tends to degrade the recognition performance. Hence, this work presents the method for activity recognition using our proposed features from combinational of spectral analysis with statistical descriptors that able to tackle the issue of differentiating stationary and locomotion activities. The noise signal is filtered using Fourier Transform before it will be extracted using two different groups of features, spectral frequency analysis, and statistical descriptors. Extracted signal later will be classified using random forest ensemble classifier models. The recognition results show the good accuracy performance for stationary and locomotion activities based on USC HAD datasets.

  10. Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ⁹-tetrahydrocannabinol administration. (United States)

    Fetterhoff, Dustin; Opris, Ioan; Simpson, Sean L; Deadwyler, Sam A; Hampson, Robert E; Kraft, Robert A


    Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models. Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Hakon


    This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.

  12. Analysis and projections of climate change impacts on flood risks in the Dniester river basin based on the ENSEMBLES RCM data (United States)

    Krakovska, S.; Balabukh, V.; Palamarchuk, L.; Djukel, G.; Gnatiuk, N.


    intensive rises of surface air temperature and average temperature of the troposphere (a thickness of 1000-500hPa layer) were found in the investigated region that together with increase of moisture content of the atmosphere led to rise of free convection level and convectively unstable layers of the atmosphere reached almost to 100hPa. The later resulted in an essential increase (almost twice) of Convective Available Potential Energy (CAPE) and, accordingly, speed of updrafts. Ensemble of seven runs of Regional Climate Models (RCM) driven by four Atmosphere and Ocean General Circulation Models (AOGCM) from the ENSEMBLES database was applied in order to obtain projected values of air temperature and precipitation changes for 2021-2050 period within the Dniester basin on a monthly basis. To make calculations more accurate the Dniester basin was subdivided into 3 regions every with 2 subregions according to river geomorphology and topography. Verification of RCM on control 1971-2000 period by E-Obs and stations' data has allowed to obtain optimum ensembles of RCM for every subregion and climate characteristic. Note, that just two regional climate models REMO and RCA both driven by ECHAM5 provided the best results either for all delineated regions or for the entire Dniester basin. Projections for 2021-2050 period were calculated from the same obtained optimum ensembles of RCM as for the control one. More or less uniform air temperature rise is expected in all subregions and months by 0.7-1.7 oC. But projections for precipitation change are more disperse: within a few per cents for annual sums, but almost 20% less for the middle and lower Dniester in August and October (drought risk) and over 15% more for the high flow of the river in September and December (flood risk). Indices of extremes recommended by ECA&D were calculated from daily data of REMO and RCA A1B runs for control and projected periods. The analysis of precipitation extremes (SDII, RX1day, RX5day, etc.) has

  13. Designing boosting ensemble of relational fuzzy systems. (United States)

    Scherer, Rafał


    A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights - elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.

  14. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination. (United States)

    Sørensen, Lauge; Nielsen, Mads


    The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

    Czech Academy of Sciences Publication Activity Database

    Pirttioja, N. K.; Carter, T. R.; Fronzek, S.; Bindi, M.; Hoffmann, H. D.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, Miroslav; Acutis, M.; Asseng, S.; Baranowski, P.; Basso, B.; Bodin, P.; Buis, S.; Cammarano, D.; Deligios, P.; Destain, M. F.; Dumont, B.; Ewert, F.; Ferrise, R.; Francois, L.; Gaiser, T.; Hlavinka, Petr; Jacquemin, I.; Kersebaum, K. C.; Kollas, C.; Krzyszczak, J.; Lorite, I. J.; Minet, J.; Minquez, M. I.; Montesino, M.; Moriondo, M.; Müller, C.; Nendel, C.; Öztürk, I.; Perego, A.; Rodriguez, A.; Ruane, A. C.; Ruget, F.; Sanna, M.; Semenov, M. A.; Slawinski, C.; Stratonovitch, P.; Supit, I.; Waha, K.; Wang, E.; Wu, L.; Zhao, Z.; Rötter, R. P.


    Roč. 65, č. 31 (2015), s. 87-105 ISSN 0936-577X R&D Projects: GA MZe QJ1310123; GA MŠk(CZ) LD13030 Grant - others:German Federal Ministries of Education and Research, and Food and Agriculture(DE) 2812ERA115 Institutional support: RVO:67179843 Keywords : climate * crop model * impact response surface * IRS * sensitivity analysis * wheat * yield Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.690, year: 2015

  16. Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction

    Directory of Open Access Journals (Sweden)

    Kazuo Saito


    forecasts from the LETKF analysis were improved and some of them became comparable to those of the mesoscale 4D-VAR analyses based on the JMA's operational data assimilation system. These results show the importance of LBPs in the MBD method and LETKF. LBPs are critical not only to ameliorate the underestimation of the ensemble spread in the ensemble forecast but also to produce better initial perturbations and to improve the LETKF analysis.

  17. Combining Conversation Analysis and Nexus Analysis to explore hospital practices

    DEFF Research Database (Denmark)

    Paasch, Bettina Sletten

    , ethnographic observations, interviews, photos and documents were obtained. Inspired by the analytical manoeuvre of zooming in and zooming out proposed by Nicolini (Nicolini, 2009; Nicolini, 2013) the present study uses Conversations Analysis (Sacks, Schegloff, & Jefferson, 1974) and Embodied Interaction...... Analysis (Streeck, Goodwin, & LeBaron, 2011) to zoom in on the situated accomplishment of interactions between nurses, patients and mobile work phones, and Nexus Analysis (Scollon & Scollon, 2004) to connect the situated actions with the historical, cultural and political currents circulating the moment...... of interaction. In the conducted interviews nurses report mobile work phones to disturb interactions with patients when they ring, however, analysing the recorded interactions with tools from Conversations Analysis and Embodied Interaction Analysis displays how nurses demonstrate sophisticated awareness...

  18. Modality-Driven Classification and Visualization of Ensemble Variance

    Energy Technology Data Exchange (ETDEWEB)

    Bensema, Kevin; Gosink, Luke; Obermaier, Harald; Joy, Kenneth I.


    Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.

  19. Ensemble perspective for catalytic promiscuity: calorimetric analysis of the active site conformational landscape of a detoxification enzyme. (United States)

    Honaker, Matthew T; Acchione, Mauro; Sumida, John P; Atkins, William M


    Enzymological paradigms have shifted recently to acknowledge the biological importance of catalytic promiscuity. However, catalytic promiscuity is a poorly understood property, and no thermodynamic treatment has described the conformational landscape of promiscuous versus substrate-specific enzymes. Here, two structurally similar glutathione transferase (GST, glutathione S-transferase) isoforms with high specificity or high promiscuity are compared. Differential scanning calorimetry (DSC) indicates a reversible low temperature transition for the promiscuous GSTA1-1 that is not observed with substrate-specific GSTA4-4. This transition is assigned to rearrangement of the C terminus at the active site of GSTA1-1 based on the effects of ligands and mutations. Near-UV and far-UV circular dichroism indicate that this transition is due to repacking of tertiary contacts with the remainder of the subunit, rather than "unfolding" of the C terminus per se. Analysis of the DSC data using a modified Landau theory indicates that the local conformational landscape of the active site of GSTA1-1 is smooth, with barrierless transitions between states. The partition function of the C-terminal states is a broad unimodal distribution at all temperatures within this DSC transition. In contrast, the remainder of the GSTA1-1 subunit and the GSTA4-4 protein exhibit folded and unfolded macrostates with a significant energy barrier separating them. Their partition function includes a sharp unimodal distribution of states only at temperatures that yield either folded or unfolded macrostates. At intermediate temperatures the partition function includes a bimodal distribution. The barrierless rearrangement of the GSTA1-1 active site within a local smooth energy landscape suggests a thermodynamic basis for catalytic promiscuity.

  20. Ensemble Empirical Mode Decomposition with Principal Component Analysis: A Novel Approach for Extracting Respiratory Rate and Heart Rate from Photoplethysmographic Signal. (United States)

    Motin, Mohammod Abdul; Karmakar, Chandan; Palaniswami, Marimuthu


    The photoplethysmographic (PPG) signal measures the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), and respiratory rate (RR) and this will reduce the number of sensors connected to the patient's body for recording these vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR and RR simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 310 (from 35 subjects) and 672 (from 42 subjects) epochs of simultaneously recorded electrocardiogram (ECG), PPG and respiratory signal extracted from MIMIC (Physionet ATM data bank) and Capnobase database respectively. Results of EEMD-PCA based extraction of HR and RR from PPG signal showed that the median RMS error (1st and 3rd quartiles) obtained in MIMIC data set for RR was 0.89 (0, 1.78) breaths/min, for HR was 0.57 (0.30, 0.71) beats/min and in Capnobase data set it was 2.77 (0.50, 5.9) breaths/min and 0.69 (0.54, 1.10) beats/min for RR and HR respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR and RR than other existing methods. Efficient and reliable extraction of HR and RR from the pulse oximeter's PPG signal will help patients for monitoring HR and RR with low cost and less discomfort.

  1. Credit scoring using ensemble of various classifiers on reduced feature set

    Directory of Open Access Journals (Sweden)

    Dahiya Shashi


    Full Text Available Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model.

  2. Analysis of Chromothripsis by Combined FISH and Microarray Analysis. (United States)

    MacKinnon, Ruth N


    Fluorescence in situ hybridization (FISH) to metaphase chromosomes, in conjunction with SNP array, array CGH, or whole genome sequencing, can help determine the organization of abnormal genomes after chromothripsis and other types of complex genome rearrangement. DNA microarrays can identify the changes in copy number, but they do not give information on the organization of the abnormal chromosomes, balanced rearrangements, or abnormalities of the centromeres and other regions comprised of highly repetitive DNA. Many of these details can be determined by the strategic use of metaphase FISH. FISH is a single-cell technique, so it can identify low-frequency chromosome abnormalities, and it can determine which chromosome abnormalities occur in the same or different clonal populations. These are important considerations in cancer. Metaphase chromosomes are intact, so information about abnormalities of the chromosome homologues is preserved. Here we describe strategies for working out the organization of highly rearranged genomes by combining SNP array data with various metaphase FISH methods. This approach can also be used to address some of the uncertainties arising from whole genome or mate-pair sequencing data.

  3. Combining Multi-modal Features for Social Media Analysis (United States)

    Nikolopoulos, Spiros; Giannakidou, Eirini; Kompatsiaris, Ioannis; Patras, Ioannis; Vakali, Athena

    In this chapter we discuss methods for efficiently modeling the diverse information carried by social media. The problem is viewed as a multi-modal analysis process where specialized techniques are used to overcome the obstacles arising from the heterogeneity of data. Focusing at the optimal combination of low-level features (i.e., early fusion), we present a bio-inspired algorithm for feature selection that weights the features based on their appropriateness to represent a resource. Under the same objective of optimal feature combination we also examine the use of pLSA-based aspect models, as the means to define a latent semantic space where heterogeneous types of information can be effectively combined. Tagged images taken from social sites have been used in the characteristic scenarios of image clustering and retrieval, to demonstrate the benefits of multi-modal analysis in social media.

  4. An Ensemble Three-Dimensional Constrained Variational Analysis Method to Derive Large-Scale Forcing Data for Single-Column Models (United States)

    Tang, Shuaiqi

    Atmospheric vertical velocities and advective tendencies are essential as large-scale forcing data to drive single-column models (SCM), cloud-resolving models (CRM) and large-eddy simulations (LES). They cannot be directly measured or easily calculated with great accuracy from field measurements. In the Atmospheric Radiation Measurement (ARM) program, a constrained variational algorithm (1DCVA) has been used to derive large-scale forcing data over a sounding network domain with the aid of flux measurements at the surface and top of the atmosphere (TOA). We extend the 1DCVA algorithm into three dimensions (3DCVA) along with other improvements to calculate gridded large-scale forcing data. We also introduce an ensemble framework using different background data, error covariance matrices and constraint variables to quantify the uncertainties of the large-scale forcing data. The results of sensitivity study show that the derived forcing data and SCM simulated clouds are more sensitive to the background data than to the error covariance matrices and constraint variables, while horizontal moisture advection has relatively large sensitivities to the precipitation, the dominate constraint variable. Using a mid-latitude cyclone case study in March 3rd, 2000 at the ARM Southern Great Plains (SGP) site, we investigate the spatial distribution of diabatic heating sources (Q1) and moisture sinks (Q2), and show that they are consistent with the satellite clouds and intuitive structure of the mid-latitude cyclone. We also evaluate the Q1 and Q2 in analysis/reanalysis, finding that the regional analysis/reanalysis all tend to underestimate the sub-grid scale upward transport of moist static energy in the lower troposphere. With the uncertainties from large-scale forcing data and observation specified, we compare SCM results and observations and find that models have large biases on cloud properties which could not be fully explained by the uncertainty from the large-scale forcing

  5. Association analysis of multiple traits by an approach of combining ...

    Indian Academy of Sciences (India)

    Lili Chen

    However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method. (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given ...

  6. Energy analysis handbook. CAC document 214. [Combining process analysis with input-output analysis

    Energy Technology Data Exchange (ETDEWEB)

    Bullard, C. W.; Penner, P. S.; Pilati, D. A.


    Methods are presented for calculating the energy required, directly and indirectly, to produce all types of goods and services. Procedures for combining process analysis with input-output analysis are described. This enables the analyst to focus data acquisition cost-effectively, and to achieve a specified degree of accuracy in the results. The report presents sample calculations and provides the tables and charts needed to perform most energy cost calculations, including the cost of systems for producing or conserving energy.

  7. R-FCN Object Detection Ensemble based on Object Resolution and Image Quality

    DEFF Research Database (Denmark)

    Rasmussen, Christoffer Bøgelund; Nasrollahi, Kamal; Moeslund, Thomas B.


    detectors. Ensemble strategies explored were firstly data sampling and selection and secondly combination strategies. Data sampling and selection aimed to create different subsets of data with respect to object size and image quality such that expert R-FCN ensemble members could be trained. Two combination...

  8. Variance-based Sensitivity Analysis of Large-scale Hydrological Model to Prepare an Ensemble-based SWOT-like Data Assimilation Experiments (United States)

    Emery, C. M.; Biancamaria, S.; Boone, A. A.; Ricci, S. M.; Garambois, P. A.; Decharme, B.; Rochoux, M. C.


    Land Surface Models (LSM) coupled with River Routing schemes (RRM), are used in Global Climate Models (GCM) to simulate the continental part of the water cycle. They are key component of GCM as they provide boundary conditions to atmospheric and oceanic models. However, at global scale, errors arise mainly from simplified physics, atmospheric forcing, and input parameters. More particularly, those used in RRM, such as river width, depth and friction coefficients, are difficult to calibrate and are mostly derived from geomorphologic relationships, which may not always be realistic. In situ measurements are then used to calibrate these relationships and validate the model, but global in situ data are very sparse. Additionally, due to the lack of existing global river geomorphology database and accurate forcing, models are run at coarse resolution. This is typically the case of the ISBA-TRIP model used in this study.A complementary alternative to in-situ data are satellite observations. In this regard, the Surface Water and Ocean Topography (SWOT) satellite mission, jointly developed by NASA/CNES/CSA/UKSA and scheduled for launch around 2020, should be very valuable to calibrate RRM parameters. It will provide maps of water surface elevation for rivers wider than 100 meters over continental surfaces in between 78°S and 78°N and also direct observation of river geomorphological parameters such as width ans slope.Yet, before assimilating such kind of data, it is needed to analyze RRM temporal sensitivity to time-constant parameters. This study presents such analysis over large river basins for the TRIP RRM. Model output uncertainty, represented by unconditional variance, is decomposed into ordered contribution from each parameter. Doing a time-dependent analysis allows then to identify to which parameters modeled water level and discharge are the most sensitive along a hydrological year. The results show that local parameters directly impact water levels, while

  9. Online breakage detection of multitooth tools using classifier ensembles for imbalanced data (United States)

    Bustillo, Andrés; Rodríguez, Juan J.


    Cutting tool breakage detection is an important task, due to its economic impact on mass production lines in the automobile industry. This task presents a central limitation: real data-sets are extremely imbalanced because breakage occurs in very few cases compared with normal operation of the cutting process. In this paper, we present an analysis of different data-mining techniques applied to the detection of insert breakage in multitooth tools. The analysis applies only one experimental variable: the electrical power consumption of the tool drive. This restriction profiles real industrial conditions more accurately than other physical variables, such as acoustic or vibration signals, which are not so easily measured. Many efforts have been made to design a method that is able to identify breakages with a high degree of reliability within a short period of time. The solution is based on classifier ensembles for imbalanced data-sets. Classifier ensembles are combinations of classifiers, which in many situations are more accurate than individual classifiers. Six different base classifiers are tested: Decision Trees, Rules, Naïve Bayes, Nearest Neighbour, Multilayer Perceptrons and Logistic Regression. Three different balancing strategies are tested with each of the classifier ensembles and compared to their performance with the original data-set: Synthetic Minority Over-Sampling Technique (SMOTE), undersampling and a combination of SMOTE and undersampling. To identify the most suitable data-mining solution, Receiver Operating Characteristics (ROC) graph and Recall-precision graph are generated and discussed. The performance of logistic regression ensembles on the balanced data-set using the combination of SMOTE and undersampling turned out to be the most suitable technique. Finally a comparison using industrial performance measures is presented, which concludes that this technique is also more suited to this industrial problem than the other techniques presented in

  10. Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China (United States)

    Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping


    Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For

  11. The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

    Directory of Open Access Journals (Sweden)

    Gulshan Kumar


    Full Text Available In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI- based techniques play prominent role in development of ensemble for intrusion detection (ID and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1 architecture & approach followed; (2 different methods utilized in different phases of ensemble learning; (3 other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs.

  12. A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

    Directory of Open Access Journals (Sweden)

    G. Thirel


    Full Text Available The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF 10-day Ensemble Prediction System (EPS. Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm

  13. Performance analysis and modeling of energy from waste combined cycles

    International Nuclear Information System (INIS)

    Qiu, K.; Hayden, A.C.S.


    Municipal solid waste (MSW) is produced in a substantial amount with minimal fluctuations throughout the year. The analysis of carbon neutrality of MSW on a life cycle basis shows that MSW is about 67% carbon-neutral, suggesting that only 33% of the CO 2 emissions from incinerating MSW are of fossil origin. The waste constitutes a 'renewable biofuel' energy resource and energy from waste (EfW) can result in a net reduction in CO 2 emissions. In this paper, we explore an approach to extracting energy from MSW efficiently - EfW/gas turbine hybrid combined cycles. This approach innovates by delivering better performance with respect to energy efficiency and CO 2 mitigation. In the combined cycles, the topping cycle consists of a gas turbine, while the bottoming cycle is a steam cycle where the low quality fuel - waste is utilized. This paper assesses the viability of the hybrid combined cycles and analyses their thermodynamic advantages with the help of computer simulations. It was shown that the combined cycles could offer significantly higher energy conversion efficiency and a practical solution to handling MSW. Also, the potential for a net reduction in CO 2 emissions resulting from the hybrid combined cycles was evaluated.

  14. Analysis of diversification: combining phylogenetic and taxonomic data.


    Paradis, Emmanuel


    The estimation of diversification rates using phylogenetic data has attracted a lot of attention in the past decade. In this context, the analysis of incomplete phylogenies (e.g. phylogenies resolved at the family level but unresolved at the species level) has remained difficult. I present here a likelihood-based method to combine partly resolved phylogenies with taxonomic (species-richness) data to estimate speciation and extinction rates. This method is based on fitting a birth-and-death mo...

  15. Exergy analysis for combined regenerative Brayton and inverse Brayton cycles

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Zelong; Chen, Lingen; Sun, Fengrui [College of Naval Architecture and Power, Naval University of Engineering, Wuhan 430033 (China)


    This paper presents the study of exergy analysis of combined regenerative Brayton and inverse Brayton cycles. The analytical formulae of exergy loss and exergy efficiency are derived. The largest exergy loss location is determined. By taking the maximum exergy efficiency as the objective, the choice of bottom cycle pressure ratio is optimized by detailed numerical examples, and the corresponding optimal exergy efficiency is obtained. The influences of various parameters on the exergy efficiency and other performances are analyzed by numerical calculations.

  16. Attenuation Analysis and Acoustic Pressure Levels for Combined Absorptive Mufflers

    Directory of Open Access Journals (Sweden)

    Ovidiu Vasile


    Full Text Available The paper describes the pressure-wave propagation in a muffler for an internal combustion engine in case of two combined mufflers geometry. The approach is generally applicable to analyzing the damping of propagation of harmonic pressure waves. The paper purpose is to show finite elements analysis of both inductive and resistive damping in pressure acoustics. The main output is the attenuation and acoustic pressure levels for the frequency range 50 Hz–3000 Hz.

  17. Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction. (United States)

    Lee, Michael C; Boroczky, Lilla; Sungur-Stasik, Kivilcim; Cann, Aaron D; Borczuk, Alain C; Kawut, Steven M; Powell, Charles A


    Accurate classification methods are critical in computer-aided diagnosis (CADx) and other clinical decision support systems. Previous research has reported on methods for combining genetic algorithm (GA) feature selection with ensemble classifier systems in an effort to increase classification accuracy. In this study, we describe a CADx system for pulmonary nodules using a two-step supervised learning system combining a GA with the random subspace method (RSM), with the aim of exploring algorithm design parameters and demonstrating improved classification performance over either the GA or RSM-based ensembles alone. We used a retrospective database of 125 pulmonary nodules (63 benign; 62 malignant) with CT volumes and clinical history. A total of 216 features were derived from the segmented image data and clinical history. Ensemble classifiers using RSM or GA-based feature selection were constructed and tested via leave-one-out validation with feature selection and classifier training executed within each iteration. We further tested a two-step approach using a GA ensemble to first assess the relevance of the features, and then using this information to control feature selection during a subsequent RSM step. The base classification was performed using linear discriminant analysis (LDA). The RSM classifier alone achieved a maximum leave-one-out Az of 0.866 (95% confidence interval: 0.794-0.919) at a subset size of s=36 features. The GA ensemble yielded an Az of 0.851 (0.775-0.907). The proposed two-step algorithm produced a maximum Az value of 0.889 (0.823-0.936) when the GA ensemble was used to completely remove less relevant features from the second RSM step, with similar results obtained when the GA-LDA results were used to reduce but not eliminate the occurrence of certain features. After accounting for correlations in the data, the leave-one-out Az in the two-step method was significantly higher than in the RSM and the GA-LDA. We have developed a CADx system for

  18. Modeling polydispersive ensembles of diamond nanoparticles

    International Nuclear Information System (INIS)

    Barnard, Amanda S


    While significant progress has been made toward production of monodispersed samples of a variety of nanoparticles, in cases such as diamond nanoparticles (nanodiamonds) a significant degree of polydispersivity persists, so scaling-up of laboratory applications to industrial levels has its challenges. In many cases, however, monodispersivity is not essential for reliable application, provided that the inevitable uncertainties are just as predictable as the functional properties. As computational methods of materials design are becoming more widespread, there is a growing need for robust methods for modeling ensembles of nanoparticles, that capture the structural complexity characteristic of real specimens. In this paper we present a simple statistical approach to modeling of ensembles of nanoparticles, and apply it to nanodiamond, based on sets of individual simulations that have been carefully selected to describe specific structural sources that are responsible for scattering of fundamental properties, and that are typically difficult to eliminate experimentally. For the purposes of demonstration we show how scattering in the Fermi energy and the electronic band gap are related to different structural variations (sources), and how these results can be combined strategically to yield statistically significant predictions of the properties of an entire ensemble of nanodiamonds, rather than merely one individual ‘model’ particle or a non-representative sub-set. (paper)

  19. Diurnal Ensemble Surface Meteorology Statistics (United States)

    U.S. Environmental Protection Agency — Excel file containing diurnal ensemble statistics of 2-m temperature, 2-m mixing ratio and 10-m wind speed. This Excel file contains figures for Figure 2 in the...

  20. IDM-PhyChm-Ens: intelligent decision-making ensemble methodology for classification of human breast cancer using physicochemical properties of amino acids. (United States)

    Ali, Safdar; Majid, Abdul; Khan, Asifullah


    Development of an accurate and reliable intelligent decision-making method for the construction of cancer diagnosis system is one of the fast growing research areas of health sciences. Such decision-making system can provide adequate information for cancer diagnosis and drug discovery. Descriptors derived from physicochemical properties of protein sequences are very useful for classifying cancerous proteins. Recently, several interesting research studies have been reported on breast cancer classification. To this end, we propose the exploitation of the physicochemical properties of amino acids in protein primary sequences such as hydrophobicity (Hd) and hydrophilicity (Hb) for breast cancer classification. Hd and Hb properties of amino acids, in recent literature, are reported to be quite effective in characterizing the constituent amino acids and are used to study protein foldings, interactions, structures, and sequence-order effects. Especially, using these physicochemical properties, we observed that proline, serine, tyrosine, cysteine, arginine, and asparagine amino acids offer high discrimination between cancerous and healthy proteins. In addition, unlike traditional ensemble classification approaches, the proposed 'IDM-PhyChm-Ens' method was developed by combining the decision spaces of a specific classifier trained on different feature spaces. The different feature spaces used were amino acid composition, split amino acid composition, and pseudo amino acid composition. Consequently, we have exploited different feature spaces using Hd and Hb properties of amino acids to develop an accurate method for classification of cancerous protein sequences. We developed ensemble classifiers using diverse learning algorithms such as random forest (RF), support vector machines (SVM), and K-nearest neighbor (KNN) trained on different feature spaces. We observed that ensemble-RF, in case of cancer classification, performed better than ensemble-SVM and ensemble-KNN. Our

  1. PSO-Ensemble Demo Application

    DEFF Research Database (Denmark)


    Within the framework of the PSO-Ensemble project (FU2101) a demo application has been created. The application use ECMWF ensemble forecasts. Two instances of the application are running; one for Nysted Offshore and one for the total production (except Horns Rev) in the Eltra area. The output is a...... is available via two password-protected web-pages hosted at IMM and is used daily by Elsam and E2....

  2. Forest structure analysis combining laser scanning with digital airborne photogrammetry (United States)

    Lissak, Candide; Onda, Yuichi; Kato, Hiroaki


    The interest of Light Detection and Ranging (LiDAR) for vegetation structure analysis has been demonstrated in several research context. Indeed, airborne or ground Lidar surveys can provide detailed three-dimensional data of the forest structure from understorey forest to the canopy. To characterize at different timescale the vegetation components in dense cedar forests we can combine several sources point clouds from Lidar survey and photogrammetry data. For our study, Terrestrial Laser Scanning (TLS-Leica ScanStation C10 processed with Cyclone software) have been lead in three forest areas (≈ 200m2 each zone) mainly composed of japanese cedar (Japonica cryptomeria), in the region of Fukushima (Japan). The study areas are characterized by various vegetation densities. For the 3 areas, Terrestrial laser scanning has been performed from several location points and several heights. Various floors shootings (ground, 4m, 6m and 18m high) were able with the use of a several meters high tower implanted to study the canopy evolution following the Fukushima Daiishi nuclear power plant accident. The combination of all scanners provides a very dense 3D point cloud of ground and canopy structure (average 300 000 000 points). For the Tochigi forest area, a first test of a low-cost Unmanned Aerial Vehicle (UAV) photogrammetry has been lead and calibrated by ground GPS measurements to determine the coordinates of points. TLS combined to UAV photogrammetry make it possible to obtain information on vertical and horizontal structure of the Tochigi forest. This combination of technologies will allow the forest structure mapping, morphometry analysis and the assessment of biomass volume evolution from multi-temporal point clouds. In our research, we used a low-cost UAV 3 Advanced (200 m2 cover, 1300 pictures...). Data processing were performed using PotoScan Pro software to obtain a very dense point clouds to combine to TLS data set. This low-cost UAV photogrammetry data has been

  3. Transition from Poisson to circular unitary ensemble

    Indian Academy of Sciences (India)

    ensemble (SE). These are defined by invariance of the ensemble measure under the orthogonal, unitary and symplectic transformations respectively and are related to the time reversal and rotational symmetries of the system. Gaussian ensembles. (GE) of Hermitian matrices and circular ensembles (CE) of unitary matrices ...

  4. Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity (United States)

    Chen, Huanhuan; Yao, Xin

    Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals.

  5. Concrete ensemble Kalman filters with rigorous catastrophic filter divergence. (United States)

    Kelly, David; Majda, Andrew J; Tong, Xin T


    The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature.

  6. OCSANA: optimal combinations of interventions from network analysis. (United States)

    Vera-Licona, Paola; Bonnet, Eric; Barillot, Emmanuel; Zinovyev, Andrei


    Targeted therapies interfering with specifically one protein activity are promising strategies in the treatment of diseases like cancer. However, accumulated empirical experience has shown that targeting multiple proteins in signaling networks involved in the disease is often necessary. Thus, one important problem in biomedical research is the design and prioritization of optimal combinations of interventions to repress a pathological behavior, while minimizing side-effects. OCSANA (optimal combinations of interventions from network analysis) is a new software designed to identify and prioritize optimal and minimal combinations of interventions to disrupt the paths between source nodes and target nodes. When specified by the user, OCSANA seeks to additionally minimize the side effects that a combination of interventions can cause on specified off-target nodes. With the crucial ability to cope with very large networks, OCSANA includes an exact solution and a novel selective enumeration approach for the combinatorial interventions' problem. The latest version of OCSANA, implemented as a plugin for Cytoscape and distributed under LGPL license, is available together with source code at

  7. Adaptive correction of ensemble forecasts (United States)

    Pelosi, Anna; Battista Chirico, Giovanni; Van den Bergh, Joris; Vannitsem, Stephane


    Forecasts from numerical weather prediction (NWP) models often suffer from both systematic and non-systematic errors. These are present in both deterministic and ensemble forecasts, and originate from various sources such as model error and subgrid variability. Statistical post-processing techniques can partly remove such errors, which is particularly important when NWP outputs concerning surface weather variables are employed for site specific applications. Many different post-processing techniques have been developed. For deterministic forecasts, adaptive methods such as the Kalman filter are often used, which sequentially post-process the forecasts by continuously updating the correction parameters as new ground observations become available. These methods are especially valuable when long training data sets do not exist. For ensemble forecasts, well-known techniques are ensemble model output statistics (EMOS), and so-called "member-by-member" approaches (MBM). Here, we introduce a new adaptive post-processing technique for ensemble predictions. The proposed method is a sequential Kalman filtering technique that fully exploits the information content of the ensemble. One correction equation is retrieved and applied to all members, however the parameters of the regression equations are retrieved by exploiting the second order statistics of the forecast ensemble. We compare our new method with two other techniques: a simple method that makes use of a running bias correction of the ensemble mean, and an MBM post-processing approach that rescales the ensemble mean and spread, based on minimization of the Continuous Ranked Probability Score (CRPS). We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 2-meter temperature and 10-meter wind speed from 18 ground-based automatic weather stations distributed across the region, comparing them with the corresponding COSMO

  8. Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

    Directory of Open Access Journals (Sweden)

    Wong G William


    Full Text Available Abstract Background Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer. Results This study explores the utility of three different feature selection schemas (Student t test, Wilcoxon rank sum test and genetic algorithm to reduce the high dimensionality of a pancreatic cancer proteomic dataset. Using the top features selected from each method, we compared the prediction performances of a single decision tree algorithm C4.5 with six different decision-tree based classifier ensembles (Random forest, Stacked generalization, Bagging, Adaboost, Logitboost and Multiboost. We show that ensemble classifiers always outperform single decision tree classifier in having greater accuracies and smaller prediction errors when applied to a pancreatic cancer proteomics dataset. Conclusion In our cross validation framework, classifier ensembles generally have better classification accuracies compared to that of a single decision tree when applied to a pancreatic cancer proteomic dataset, thus suggesting its utility in future proteomics data analysis. Additionally, the use of feature selection

  9. River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method

    Directory of Open Access Journals (Sweden)

    H. Sanikhani


    Full Text Available Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1 methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE methodology for an application to runoff prediction, (2 methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3 methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system. Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models

  10. A pragmatic approach to the analysis of a combination formulation

    Directory of Open Access Journals (Sweden)

    Noshin Mubtasim


    The proposed combination formulation has shown compatibility with the chosen excipients, verified through FT-IR study. A novel gradient RP-HPLC method was developed and validated according to the ICH guideline which was found to be suitable for the simultaneous estimation of rosuvastatin calcium and amlodipine besylate from the formulation. The retention time of 2.7 and 6.08 min allows the analysis of large amount of samples with less mobile phase which makes the method economic. The dissolution profiles of both the drugs in different dissolution medium were encouraging which makes the combination formulation of rosuvastatin calcium and amlodipine besylate superior and effective in achieving patient compliance.

  11. Technical and financial analysis of combined cycle gas turbine

    Directory of Open Access Journals (Sweden)

    Khan Arshad Muhammad


    Full Text Available This paper presents technical and financial models which were developed in this study to predict the overall performance of combined cycle gas turbine plant in line with the needs of independent power producers in the liberalized market of power sector. Three similar sizes of combined cycle gas turbine power projects up to 200 Megawatt of independent power producers in Pakistan were selected in-order to develop and drive the basic assumptions for the inputs of the models in view of prevailing Government of Pakistan’s two components of electricity purchasing tariff that is energy purchase price and capacity purchase price at higher voltage grid station terminal from independent power producers. The levelized electricity purchasing tariff over life of plant on gaseous fuel at 60 percent plant load factor was 6.47 cent per kilowatt hour with energy purchase price and capacity purchase prices of 3.54 and 2.93 cents per kilowatt hour respectively. The outcome of technical models of gas turbine, steam turbine and combined cycle gas turbine power were found in close agreement with the projects under consideration and provides opportunity of evaluation of technical and financial aspects of combined cycle power plant in a more simplified manner with relatively accurate results. At 105 Celsius exit temperature of heat recovery steam generator flue gases the net efficiency of combined cycle gas turbine was 48.8 percent whereas at 125 Celsius exit temperature of heat recovery steam generator flue gases it was 48.0 percent. Sensitivity analysis of selected influential components of electricity tariff was also carried out.

  12. Modelling irrigated maize with a combination of coupled-model simulation and uncertainty analysis, in the northwest of China

    Directory of Open Access Journals (Sweden)

    Y. Li


    Full Text Available The hydrologic model HYDRUS-1-D and the crop growth model WOFOST are coupled to efficiently manage water resources in agriculture and improve the prediction of crop production. The results of the coupled model are validated by experimental studies of irrigated-maize done in the middle reaches of northwest China's Heihe River, a semi-arid to arid region. Good agreement is achieved between the simulated evapotranspiration, soil moisture and crop production and their respective field measurements made under current maize irrigation and fertilization. Based on the calibrated model, the scenario analysis reveals that the most optimal amount of irrigation is 500–600 mm in this region. However, for regions without detailed observation, the results of the numerical simulation can be unreliable for irrigation decision making owing to the shortage of calibrated model boundary conditions and parameters. So, we develop a method of combining model ensemble simulations and uncertainty/sensitivity analysis to speculate the probability of crop production. In our studies, the uncertainty analysis is used to reveal the risk of facing a loss of crop production as irrigation decreases. The global sensitivity analysis is used to test the coupled model and further quantitatively analyse the impact of the uncertainty of coupled model parameters and environmental scenarios on crop production. This method can be used for estimation in regions with no or reduced data availability.

  13. A Combined Metabolomic and Proteomic Analysis of Gestational Diabetes Mellitus

    Directory of Open Access Journals (Sweden)

    Joanna Hajduk


    Full Text Available The aim of this pilot study was to apply a novel combined metabolomic and proteomic approach in analysis of gestational diabetes mellitus. The investigation was performed with plasma samples derived from pregnant women with diagnosed gestational diabetes mellitus (n = 18 and a matched control group (n = 13. The mass spectrometry-based analyses allowed to determine 42 free amino acids and low molecular-weight peptide profiles. Different expressions of several peptides and altered amino acid profiles were observed in the analyzed groups. The combination of proteomic and metabolomic data allowed obtaining the model with a high discriminatory power, where amino acids ethanolamine, l-citrulline, l-asparagine, and peptide ions with m/z 1488.59; 4111.89 and 2913.15 had the highest contribution to the model. The sensitivity (94.44% and specificity (84.62%, as well as the total group membership classification value (90.32% calculated from the post hoc classification matrix of a joint model were the highest when compared with a single analysis of either amino acid levels or peptide ion intensities. The obtained results indicated a high potential of integration of proteomic and metabolomics analysis regardless the sample size. This promising approach together with clinical evaluation of the subjects can also be used in the study of other diseases.

  14. Does the uncertainty in the representation of terrestrial water flows affect precipitation predictability? A WRF-Hydro ensemble analysis for Central Europe (United States)

    Arnault, Joel; Rummler, Thomas; Baur, Florian; Lerch, Sebastian; Wagner, Sven; Fersch, Benjamin; Zhang, Zhenyu; Kerandi, Noah; Keil, Christian; Kunstmann, Harald


    Precipitation predictability can be assessed by the spread within an ensemble of atmospheric simulations being perturbed in the initial, lateral boundary conditions and/or modeled processes within a range of uncertainty. Surface-related processes are more likely to change precipitation when synoptic forcing is weak. This study investigates the effect of uncertainty in the representation of terrestrial water flows on precipitation predictability. The tools used for this investigation are the Weather Research and Forecasting (WRF) model and its hydrologically-enhanced version WRF-Hydro, applied over Central Europe during April-October 2008. The WRF grid is that of COSMO-DE, with a resolution of 2.8 km. In WRF-Hydro, the WRF grid is coupled with a sub-grid at 280 m resolution to resolve lateral terrestrial water flows. Vertical flow uncertainty is considered by modifying the parameter controlling the partitioning between surface runoff and infiltration in WRF, and horizontal flow uncertainty is considered by comparing WRF with WRF-Hydro. Precipitation predictability is deduced from the spread of an ensemble based on three turbulence parameterizations. Model results are validated with E-OBS precipitation and surface temperature, ESA-CCI soil moisture, FLUXNET-MTE surface evaporation and GRDC discharge. It is found that the uncertainty in the representation of terrestrial water flows is more likely to significantly affect precipitation predictability when surface flux spatial variability is high. In comparison to the WRF ensemble, WRF-Hydro slightly improves the adjusted continuous ranked probability score of daily precipitation. The reproduction of observed daily discharge with Nash-Sutcliffe model efficiency coefficients up to 0.91 demonstrates the potential of WRF-Hydro for flood forecasting.

  15. Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

    Energy Technology Data Exchange (ETDEWEB)

    Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA


    The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

  16. Ensemble Forecasts with Useful Skill-Spread Relationships for African meningitis and Asia Streamflow Forecasting (United States)

    Hopson, T. M.


    One potential benefit of an ensemble prediction system (EPS) is its capacity to forecast its own forecast error through the ensemble spread-error relationship. In practice, an EPS is often quite limited in its ability to represent the variable expectation of forecast error through the variable dispersion of the ensemble, and perhaps more fundamentally, in its ability to provide enough variability in the ensembles dispersion to make the skill-spread relationship even potentially useful (irrespective of whether the EPS is well-calibrated or not). In this paper we examine the ensemble skill-spread relationship of an ensemble constructed from the TIGGE (THORPEX Interactive Grand Global Ensemble) dataset of global forecasts and a combination of multi-model and post-processing approaches. Both of the multi-model and post-processing techniques are based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. The methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. A context for these concepts is provided by assessing the constructed ensemble in forecasting district-level humidity impacting the incidence of meningitis in the meningitis belt of Africa, and in forecasting flooding events in the Brahmaputra and Ganges basins of South Asia.


    Directory of Open Access Journals (Sweden)

    Balamurugan Mahalingam


    Full Text Available Ensemble of classifiers combines the more than one prediction models of classifiers into single model for classifying the new instances. Unbiased samples could help the ensemble classifiers to build the efficient prediction model. Existing sampling techniques fails to give the unbiased samples. To overcome this problem, the paper introduces a k-modes random sample technique which combines the k-modes cluster algorithm and simple random sampling technique to take the sample from the dataset. In this paper, the impact of random sampling technique in the Ensemble learning algorithm is shown. Random selection was done properly by using k-modes random sampling technique. Hence, sample will reflect the characteristics of entire dataset.

  18. Comparative Visualization of Ensembles Using Ensemble Surface Slicing. (United States)

    Alabi, Oluwafemi S; Wu, Xunlei; Harter, Jonathan M; Phadke, Madhura; Pinto, Lifford; Petersen, Hannah; Bass, Steffen; Keifer, Michael; Zhong, Sharon; Healey, Chris; Taylor, Russell M


    By definition, an ensemble is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an ensemble is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call Ensemble Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators.

  19. Cost-benefit analysis for combined heat and power plant

    International Nuclear Information System (INIS)

    Sazdovski, Ace; Fushtikj, Vangel


    The paper presents a methodology and practical application of Cost-Benefit Analysis for Combined Heat and Power Plant (Cogeneration facility). Methodology include up-to-date and real data for cogeneration plant in accordance with the trends ill development of the CHP technology. As a case study a CHP plant that could be built-up in Republic of Macedonia is analyzed. The main economic parameters for project evaluation, such as NPV and IRR are calculated for a number of possible scenarios. The analyze present the economic outputs that could be used as a decision for CHP project acceptance for investment. (Author)

  20. A multi-model ensemble approach to seabed mapping (United States)

    Diesing, Markus; Stephens, David


    Seabed habitat mapping based on swath acoustic data and ground-truth samples is an emergent and active marine science discipline. Significant progress could be achieved by transferring techniques and approaches that have been successfully developed and employed in such fields as terrestrial land cover mapping. One such promising approach is the multiple classifier system, which aims at improving classification performance by combining the outputs of several classifiers. Here we present results of a multi-model ensemble applied to multibeam acoustic data covering more than 5000 km2 of seabed in the North Sea with the aim to derive accurate spatial predictions of seabed substrate. A suite of six machine learning classifiers (k-Nearest Neighbour, Support Vector Machine, Classification Tree, Random Forest, Neural Network and Naïve Bayes) was trained with ground-truth sample data classified into seabed substrate classes and their prediction accuracy was assessed with an independent set of samples. The three and five best performing models were combined to classifier ensembles. Both ensembles led to increased prediction accuracy as compared to the best performing single classifier. The improvements were however not statistically significant at the 5% level. Although the three-model ensemble did not perform significantly better than its individual component models, we noticed that the five-model ensemble did perform significantly better than three of the five component models. A classifier ensemble might therefore be an effective strategy to improve classification performance. Another advantage is the fact that the agreement in predicted substrate class between the individual models of the ensemble could be used as a measure of confidence. We propose a simple and spatially explicit measure of confidence that is based on model agreement and prediction accuracy.

  1. Quantifying Monte Carlo uncertainty in ensemble Kalman filter

    Energy Technology Data Exchange (ETDEWEB)

    Thulin, Kristian; Naevdal, Geir; Skaug, Hans Julius; Aanonsen, Sigurd Ivar


    This report is presenting results obtained during Kristian Thulin PhD study, and is a slightly modified form of a paper submitted to SPE Journal. Kristian Thulin did most of his portion of the work while being a PhD student at CIPR, University of Bergen. The ensemble Kalman filter (EnKF) is currently considered one of the most promising methods for conditioning reservoir simulation models to production data. The EnKF is a sequential Monte Carlo method based on a low rank approximation of the system covariance matrix. The posterior probability distribution of model variables may be estimated fram the updated ensemble, but because of the low rank covariance approximation, the updated ensemble members become correlated samples from the posterior distribution. We suggest using multiple EnKF runs, each with smaller ensemble size to obtain truly independent samples from the posterior distribution. This allows a point-wise confidence interval for the posterior cumulative distribution function (CDF) to be constructed. We present a methodology for finding an optimal combination of ensemble batch size (n) and number of EnKF runs (m) while keeping the total number of ensemble members ( m x n) constant. The optimal combination of n and m is found through minimizing the integrated mean square error (MSE) for the CDFs and we choose to define an EnKF run with 10.000 ensemble members as having zero Monte Carlo error. The methodology is tested on a simplistic, synthetic 2D model, but should be applicable also to larger, more realistic models. (author). 12 refs., figs.,tabs

  2. Algorithms on ensemble quantum computers. (United States)

    Boykin, P Oscar; Mor, Tal; Roychowdhury, Vwani; Vatan, Farrokh


    In ensemble (or bulk) quantum computation, all computations are performed on an ensemble of computers rather than on a single computer. Measurements of qubits in an individual computer cannot be performed; instead, only expectation values (over the complete ensemble of computers) can be measured. As a result of this limitation on the model of computation, many algorithms cannot be processed directly on such computers, and must be modified, as the common strategy of delaying the measurements usually does not resolve this ensemble-measurement problem. Here we present several new strategies for resolving this problem. Based on these strategies we provide new versions of some of the most important quantum algorithms, versions that are suitable for implementing on ensemble quantum computers, e.g., on liquid NMR quantum computers. These algorithms are Shor's factorization algorithm, Grover's search algorithm (with several marked items), and an algorithm for quantum fault-tolerant computation. The first two algorithms are simply modified using a randomizing and a sorting strategies. For the last algorithm, we develop a classical-quantum hybrid strategy for removing measurements. We use it to present a novel quantum fault-tolerant scheme. More explicitly, we present schemes for fault-tolerant measurement-free implementation of Toffoli and σ(z)(¼) as these operations cannot be implemented "bitwise", and their standard fault-tolerant implementations require measurement.

  3. Exergoeconomical analysis of coal gasification combined cycle power plants

    International Nuclear Information System (INIS)

    Avgousti, A.; Knoche, K.F.; Poptodorov, H.; Hesselmann, K.; Roth, M.


    This paper reports on combined cycle power plants with integrated coal gasification for a better utilization of primary energy sources which gained more and more importance. The established coal gasification technology offers various possibilities e.g. the TEXACO or the PRENFLO method. Recommendation for processes with these gasification methods will be evaluated energetically and exergetically. The pure thermodynamical analysis is at a considerable disadvantage in that the economical consequences of certain process improvement measures are not subjected to investigation. The connection of the exergetical with the economical evaluation will be realized in a way suggested as exergoeconomical analysis. This consideration of the reciprocal influencing of the exergy destruction and the capital depending costs is resulting in an optimization of the process and a minimization of the product costs

  4. On Constructing Ensembles for Combinatorial Optimisation. (United States)

    Hart, Emma; Sim, Kevin


    Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this article, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as an example. In particular, we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity. We find that randomly composed ensembles can outperform ensembles of high-performing algorithms under certain conditions and that judicious choice of diversity metric is required to construct good ensembles. The method and findings can be generalised to any metaheuristic ensemble, and lead to better understanding of how to undertake principled ensemble design.

  5. An automated approach to network features of protein structure ensembles (United States)

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi


    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from PMID:23934896

  6. Numerical Analysis of Combined Valve Hydrodynamic Characteristics for Turbine System

    Energy Technology Data Exchange (ETDEWEB)

    Bhowmik, P. K.; Shamim, J. A.; Gairola, A.; Arif, M.; Suh, Kune Y. [Seoul National Univ., Seoul (Korea, Republic of)


    precisely by the valve manufacturer. As a matter of fact, attempts were made to obtain flow characteristic curves resorting to analytical as well as numerical methods. The flow characteristic curve relates the stem lift with mass flow rate at a specific temperature and pressure. This paper focuses on computational and numerical analysis of the combined stop and control valve. Combined Airflow Regulation Analysis (CARA) is performed to check on the hydrodynamic characteristic, which is represented by flow coefficient characteristic. CATIA V.5 and ANSYS CFX are used for three-dimensional computer-aided design and computational fluid dynamics (CFD) analysis, respectively. Flow characteristic curves are plotted by calculating theoretical and numerical mass flow rate. Hydrodynamic analysis was made of the combined stop and control valve for the turbine system using ANSYS CFX. The result of the numerical study represented by the valve flow coefficient with different normalized values of valve stem movement L/D and different pressure ratios of valve outlet and inlet agrees well with the ideal case and other similar previous experimental results. This study also provided a solid understanding with versatile options for analyzing the hydrodynamics of the combined valve considering the various internal geometry, seat, plug, and the inlet plus outlet boundary conditions to improve the efficiency, performance and reliability of the turbine system of small as well as large power conversion system using the numerical analysis with minimal cost and time.

  7. Genetic Programming Based Ensemble System for Microarray Data Classification

    Directory of Open Access Journals (Sweden)

    Kun-Hong Liu


    Full Text Available Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP based new ensemble system (named GPES, which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

  8. Combining network analysis with Cognitive Work Analysis: insights into social organisational and cooperation analysis. (United States)

    Houghton, Robert J; Baber, Chris; Stanton, Neville A; Jenkins, Daniel P; Revell, Kirsten


    Cognitive Work Analysis (CWA) allows complex, sociotechnical systems to be explored in terms of their potential configurations. However, CWA does not explicitly analyse the manner in which person-to-person communication is performed in these configurations. Consequently, the combination of CWA with Social Network Analysis provides a means by which CWA output can be analysed to consider communication structure. The approach is illustrated through a case study of a military planning team. The case study shows how actor-to-actor and actor-to-function mapping can be analysed, in terms of centrality, to produce metrics of system structure under different operating conditions. In this paper, a technique for building social network diagrams from CWA is demonstrated.The approach allows analysts to appreciate the potential impact of organisational structure on a command system.

  9. Ensemble Equivalence for Distinguishable Particles

    Directory of Open Access Journals (Sweden)

    Antonio Fernández-Peralta


    Full Text Available Statistics of distinguishable particles has become relevant in systems of colloidal particles and in the context of applications of statistical mechanics to complex networks. In this paper, we present evidence that a commonly used expression for the partition function of a system of distinguishable particles leads to huge fluctuations of the number of particles in the grand canonical ensemble and, consequently, to nonequivalence of statistical ensembles. We will show that the alternative definition of the partition function including, naturally, Boltzmann’s correct counting factor for distinguishable particles solves the problem and restores ensemble equivalence. Finally, we also show that this choice for the partition function does not produce any inconsistency for a system of distinguishable localized particles, where the monoparticular partition function is not extensive.

  10. Advanced Atmospheric Ensemble Modeling Techniques

    Energy Technology Data Exchange (ETDEWEB)

    Buckley, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Chiswell, S. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Kurzeja, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Maze, G. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Viner, B. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Werth, D. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)


    Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two release times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL’s capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.

  11. Regional interdependency of precipitation indices across Denmark in two ensembles of high-resolution RCMs

    DEFF Research Database (Denmark)

    Sunyer Pinya, Maria Antonia; Madsen, Henrik; Rosbjerg, Dan


    all these methods is that the climate models are independent. This study addresses the validity of this assumption for two ensembles of regional climate models (RCMs) from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project based on the land cells covering Denmark....... Daily precipitation indices from an ensemble of RCMs driven by the 40-yrECMWFRe-Analysis (ERA-40) and an ensemble of the same RCMs driven by different general circulation models (GCMs) are analyzed. Two different methods are used to estimate the amount of independent information in the ensembles....... These are based on different statistical properties of a measure of climate model error. Additionally, a hierarchical cluster analysis is carried out. Regardless of the method used, the effective number of RCMs is smaller than the total number of RCMs. The estimated effective number of RCMs varies depending...

  12. Learning Outlier Ensembles

    DEFF Research Database (Denmark)

    Micenková, Barbora; McWilliams, Brian; Assent, Ira

    into the existing unsupervised algorithms. In this paper, we show how to use powerful machine learning approaches to combine labeled examples together with arbitrary unsupervised outlier scoring algorithms. We aim to get the best out of the two worlds—supervised and unsupervised. Our approach is also a viable...

  13. Data analysis using a combination of independent component analysis and empirical mode decomposition. (United States)

    Lin, Shih-Lin; Tung, Pi-Cheng; Huang, Norden E


    A combination of independent component analysis and empirical mode decomposition (ICA-EMD) is proposed in this paper to analyze low signal-to-noise ratio data. The advantages of ICA-EMD combination are these: ICA needs few sensory clues to separate the original source from unwanted noise and EMD can effectively separate the data into its constituting parts. The case studies reported here involve original sources contaminated by white Gaussian noise. The simulation results show that the ICA-EMD combination is an effective data analysis tool.

  14. Combining morphological analysis and Bayesian networks for strategic decision support

    Directory of Open Access Journals (Sweden)

    A de Waal


    Full Text Available Morphological analysis (MA and Bayesian networks (BN are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative and quantitative part. The qualitative part is a cause-and-effect, or causal graph. The quantitative part depicts the strength of the causal relationships between variables. Combining MA and BN, as two phases in a modelling process, allows us to gain the benefits of both of these methods. The strength of MA lies in defining, linking and internally evaluating the parameters of problem spaces and BN modelling allows for the definition and quantification of causal relationships between variables. Short summaries of MA and BN are provided in this paper, followed by discussions how these two computer aided methods may be combined to better facilitate modelling procedures. A simple example is presented, concerning a recent application in the field of environmental decision support.

  15. Analysis of diversification: combining phylogenetic and taxonomic data. (United States)

    Paradis, Emmanuel


    The estimation of diversification rates using phylogenetic data has attracted a lot of attention in the past decade. In this context, the analysis of incomplete phylogenies (e.g. phylogenies resolved at the family level but unresolved at the species level) has remained difficult. I present here a likelihood-based method to combine partly resolved phylogenies with taxonomic (species-richness) data to estimate speciation and extinction rates. This method is based on fitting a birth-and-death model to both phylogenetic and taxonomic data. Some examples of the method are presented with data on birds and on mammals. The method is compared with existing approaches that deal with incomplete phylogenies. Some applications and generalizations of the approach introduced in this paper are further discussed.

  16. Economical analysis of combined fuel cell generators and absorption chillers

    Directory of Open Access Journals (Sweden)

    M. Morsy El-Gohary


    Full Text Available This paper presents a co-generation system based on combined heat and power for commercial units. For installation of a co-generation system, certain estimates for this site should be performed through making assessments of electrical loads, domestic water, and thermal demand. This includes domestic hot water, selection of the type of power generator, fuel cell, and the type of air conditioning system, and absorption chillers. As a matter of fact, the co-generation system has demonstrated good results for both major aspects, economic and environmental. From the environmental point of view, this can be considered as an ideal solution for problems concerned with the usage of Chlorofluoro carbons. On the other hand, from the economic point of view, the cost analysis has revealed that the proposed system saves 4% of total cost through using the co-generation system.

  17. Ensemble system for Part-of-Speech tagging


    Dell'Orletta, Felice


    The paper contains a description of the Felice-POS-Tagger and of its performance in Evalita 2009. Felice-POS-Tagger is an ensemble system that combines six different POS taggers. When evaluated on the official test set, the ensemble system outperforms each of the single tagger components and achieves the highest accuracy score in Evalita 2009 POS Closed Task. It is shown rst that the errors made from the dierent taggers are complementary, and then how to use this complementary behavior to the...

  18. Investigation of fish otoliths by combined ion beam analysis

    International Nuclear Information System (INIS)

    Huszank, R.; Simon, A.; Keresztessy, K.


    Complete text of publication follows. This work was implemented within the framework of the Hungarian Ion beam Physics Platform ( Otoliths are small structures, 'the ear stones' of a fish, and are used to detect acceleration and orientation. They are composed of a combination of protein matrix and calcium carbonate (CaCO 3 ) forming aragonite micro crystals. They have an annually deposited layered conformation with a microstructure corresponding to the seasonal and daily increments. Trace elements, such as Sr, Zn, Fe etc., are also incorporated into the otolith from the environment and the nutrition. The elemental distribution of the otolith of fresh water fish burbot (Lota lota L.) collected in Hungary was measured with Elastic Recoil Detection Analysis (ERDA), Rutherford backscattering spectrometry (RBS) and Particle Induced X-ray Emission (PIXE) at the Nuclear Microprobe Facility of HAS ATOMKI. The spatial 3D structure of the otolith could be observed with a sub-micrometer resolution. It is confirmed that the aragonite micro-crystals are covered by an organic layer and there are some protein rich regions in the otolith, too. By applying the RBSMAST code developed for RBS on macroscopic structure, it was proven that the orientation of the needle shaped aragonite crystals is considerably different at adjacent locations in the otolith. The organic and inorganic component of the otolith could be set apart in the depth selective hydrogen and calcium maps derived by micro- ERDA and micro-RBS. Similar structural analysis could be done near the surface by combining the C, O and Ca elemental maps determined by micro-PIXE measurements. It was observed that the trace metal Zn is bound to the protein component. Acknowledgements This work was partially supported by the Hungarian OTKA Grant No. T046238 and the EU cofunded Economic Competitiveness Operative Programme (GVOP-3.2.1.-2004-04-0402/3.0)

  19. Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses. (United States)

    Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc


    In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

  20. Spectral Diagonal Ensemble Kalman Filters

    Czech Academy of Sciences Publication Activity Database

    Kasanický, Ivan; Mandel, Jan; Vejmelka, Martin


    Roč. 22, č. 4 (2015), s. 485-497 ISSN 1023-5809 R&D Projects: GA ČR GA13-34856S Grant - others:NSF(US) DMS -1216481 Institutional support: RVO:67985807 Keywords : data assimilation * ensemble Kalman filter * spectral representation Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.321, year: 2015

  1. Multimodel ensembles of wheat growth

    DEFF Research Database (Denmark)

    Martre, Pierre; Wallach, Daniel; Asseng, Senthold


    , but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24...

  2. Ensemble Risk Model of Emergency Admissions (ERMER). (United States)

    Mesgarpour, Mohsen; Chaussalet, Thierry; Chahed, Salma


    About half of hospital readmissions can be avoided with preventive interventions. Developing decision support tools for identification of patients' emergency readmission risk is an important area of research. Because, it remains unclear how to design features and develop predictive models that can adjust continuously to a fast-changing healthcare system and population characteristics. The objective of this study was to develop a generic ensemble Bayesian risk model of emergency readmission. We produced a decision support tool that predicts risk of emergency readmission using England's Hospital Episode Statistics inpatient database. Firstly, we used a framework to develop an optimal set of features. Then, a combination of Bayes Point Machine (BPM) models for different cohorts was considered to create an optimised ensemble model, which is stronger than the individual generative and non-linear classifications. The developed Ensemble Risk Model of Emergency Admissions (ERMER) was trained and tested using three time-frames: 1999-2004, 2000-05 and 2004-09, each of which includes about 20% of patients in England during the trigger year. Comparisons are made for different time-frames, sub-populations, risk cut-offs, risk bands and top risk segments. The precision was 71.6-73.9%, the specificity was 88.3-91.7% and the sensitivity was 42.1-49.2% across different time-frames. Moreover, the Area Under the Curve was 75.9-77.1%. The decision support tool performed considerably better than the previous modelling approaches, and it was robust and stable with high precision. Moreover, the framework and the Bayesian model allow the model to continuously adjust it to new significant features, different population characteristics and changes in the system. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model

    Directory of Open Access Journals (Sweden)

    D. R. Allen


    Full Text Available Wind extraction from stratospheric ozone (O3 assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar shallow water model (SWM system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z, while O3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members, with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF. This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2–0.5 for small ensembles to 0.5–1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from  ∼  35 % with 25 and 50 members to  ∼  50 % with 1518 members. For the smallest ensembles (25 and 50 members, the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members, the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.

  4. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    Marquardt algorithm by varying conditions such as inputs, hidden neurons, initialization, training sets and random Gaussian noise injection to ... Several such ensembles formed the population which was evolved to generate the fittest ensemble.

  5. Global Ensemble Forecast System (GEFS) [1 Deg. (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  6. Localization of atomic ensembles via superfluorescence

    International Nuclear Information System (INIS)

    Macovei, Mihai; Evers, Joerg; Keitel, Christoph H.; Zubairy, M. Suhail


    The subwavelength localization of an ensemble of atoms concentrated to a small volume in space is investigated. The localization relies on the interaction of the ensemble with a standing wave laser field. The light scattered in the interaction of the standing wave field and the atom ensemble depends on the position of the ensemble relative to the standing wave nodes. This relation can be described by a fluorescence intensity profile, which depends on the standing wave field parameters and the ensemble properties and which is modified due to collective effects in the ensemble of nearby particles. We demonstrate that the intensity profile can be tailored to suit different localization setups. Finally, we apply these results to two localization schemes. First, we show how to localize an ensemble fixed at a certain position in the standing wave field. Second, we discuss localization of an ensemble passing through the standing wave field

  7. Maximum Likelihood Ensemble Filter-based Data Assimilation with HSPF for Improving Water Quality Forecasting (United States)

    Kim, S.; Riazi, H.; Shin, C.; Seo, D.


    Due to the large dimensionality of the state vector and sparsity of observations, the initial conditions (IC) of water quality models are subject to large uncertainties. To reduce the IC uncertainties in operational water quality forecasting, an ensemble data assimilation (DA) procedure for the Hydrologic Simulation Program - Fortran (HSPF) model has been developed and evaluated for the Kumho River Subcatchment of the Nakdong River Basin in Korea. The procedure, referred to herein as MLEF-HSPF, uses maximum likelihood ensemble filter (MLEF) which combines strengths of variational assimilation (VAR) and ensemble Kalman filter (EnKF). The Control variables involved in the DA procedure include the bias correction factors for mean areal precipitation and mean areal potential evaporation, the hydrologic state variables, and the water quality state variables such as water temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonium (NH4), nitrate (NO3), phosphate (PO4) and chlorophyll a (CHL-a). Due to the very large dimensionality of the inverse problem, accurately specifying the parameters for the DA procdedure is a challenge. Systematic sensitivity analysis is carried out for identifying the optimal parameter settings. To evaluate the robustness of MLEF-HSPF, we use multiple subcatchments of the Nakdong River Basin. In evaluation, we focus on the performance of MLEF-HSPF on prediction of extreme water quality events.

  8. A ridge ensemble empirical mode decomposition approach to clutter rejection for ultrasound color flow imaging. (United States)

    Shen, Zhiyuan; Feng, Naizhang; Shen, Yi; Lee, Chin-Hui


    In color flow imaging, it is a challenging work to accurately extract blood flow information from ultrasound Doppler echoes dominated by the strong clutter components. In this paper, we provide an in-depth analysis of ridge ensemble empirical mode decomposition (R-EEMD) and compare it with the conventional empirical mode decomposition (EMD) framework. R-EEMD facilitates nonuniform and trial-dependent weights obtained by an optimization procedure during ensemble combination and results in less decomposition errors when compared with the conventional ensemble empirical mode decomposition techniques. A theoretic result is then extended to demonstrate that R-EEMD has an ability to solve the mode mixing problem frequently encountered in EMD and improve the decomposition performance with adequate noise strength when separating a composite two-tone signal. Based on the proposed R-EEMD framework, a novel clutter rejection filter for ultrasound color flow imaging is designed. In a series of simulations, the R-EEMD-based filter achieves a significant improvement on blood flow velocity estimation over the state-of-the-art regression filters and decomposition-based filters, such as eigen-based and EMD filters. An experiment on human carotid artery data also verifies that the R-EEMD algorithm achieves minimum clutter energy and maximum blood-to-clutter energy ratio among all the tested techniques.

  9. Statistical ensembles and molecular dynamics studies of anisotropic solids. II

    International Nuclear Information System (INIS)

    Ray, J.R.; Rahman, A.


    We have recently discussed how the Parrinello--Rahman theory can be brought into accord with the theory of the elastic and thermodynamic behavior of anisotropic media. This involves the isoenthalpic--isotension ensemble of statistical mechanics. Nose has developed a canonical ensemble form of molecular dynamics. We combine Nose's ideas with the Parrinello--Rahman theory to obtain a canonical form of molecular dynamics appropriate to the study of anisotropic media subjected to arbitrary external stress. We employ this isothermal--isotension ensemble in a study of a fcc→ close-packed structural phase transformation in a Lennard-Jones solid subjected to uniaxial compression. Our interpretation of the Nose theory does not involve a scaling of the time variable. This latter fact leads to simplifications when studying the time dependence of quantities

  10. Towards a spin-ensemble quantum memory for superconducting qubits (United States)

    Grezes, Cécile; Kubo, Yuimaru; Julsgaard, Brian; Umeda, Takahide; Isoya, Junichi; Sumiya, Hitoshi; Abe, Hiroshi; Onoda, Shinobu; Ohshima, Takeshi; Nakamura, Kazuo; Diniz, Igor; Auffeves, Alexia; Jacques, Vincent; Roch, Jean-François; Vion, Denis; Esteve, Daniel; Moelmer, Klaus; Bertet, Patrice


    This article reviews efforts to build a new type of quantum device, which combines an ensemble of electronic spins with long coherence times, and a small-scale superconducting quantum processor. The goal is to store over long times arbitrary qubit states in orthogonal collective modes of the spin-ensemble, and to retrieve them on-demand. We first present the protocol devised for such a multi-mode quantum memory. We then describe a series of experimental results using NV (as in nitrogen vacancy) center spins in diamond, which demonstrate its main building blocks: the transfer of arbitrary quantum states from a qubit into the spin ensemble, and the multi-mode retrieval of classical microwave pulses down to the single-photon level with a Hahn-echo like sequence. A reset of the spin memory is implemented in-between two successive sequences using optical repumping of the spins. xml:lang="fr"

  11. Adiabatically deformed ensemble: Engineering nonthermal states of matter (United States)

    Kennes, D. M.


    We propose a route towards engineering nonthermal states of matter, which show largely unexplored physics. The main idea relies on the adiabatic passage of a thermal ensemble under slow variations of the system Hamiltonian. If the temperature of the initial thermal ensemble is either zero or infinite, the ensemble after the passage is a simple thermal one with the same vanishing or infinite temperature. However, for any finite nonzero temperature, intriguing nonthermal ensembles can be achieved. We exemplify this in (a) a single oscillator, (b) a dimerized interacting one-dimensional chain of spinless fermions, (c) a BCS-type superconductor, and (d) the topological Kitaev chain. We solve these models with a combination of methods: either exactly, numerically using the density matrix renormalization group, or within an approximate functional renormalization group scheme. The designed states show strongly nonthermal behavior in each of the considered models. For example, for the chain of spinless fermions we exemplify how long-ranged nonthermal power-law correlations can be stabilized, and for the Kitaev chain we elucidate how the nonthermal ensemble can largely alter the transition temperature separating topological and trivial phases.

  12. Visualization and classification of physiological failure modes in ensemble hemorrhage simulation (United States)

    Zhang, Song; Pruett, William Andrew; Hester, Robert


    In an emergency situation such as hemorrhage, doctors need to predict which patients need immediate treatment and care. This task is difficult because of the diverse response to hemorrhage in human population. Ensemble physiological simulations provide a means to sample a diverse range of subjects and may have a better chance of containing the correct solution. However, to reveal the patterns and trends from the ensemble simulation is a challenging task. We have developed a visualization framework for ensemble physiological simulations. The visualization helps users identify trends among ensemble members, classify ensemble member into subpopulations for analysis, and provide prediction to future events by matching a new patient's data to existing ensembles. We demonstrated the effectiveness of the visualization on simulated physiological data. The lessons learned here can be applied to clinically-collected physiological data in the future.

  13. Generation of scenarios from calibrated ensemble forecasts with a dynamic ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.


    . The new approach which preserves the dynamical development of the ensemble members is called dynamic ensemble copula coupling (d-ECC). The ensemble based empirical copulas, ECC and d-ECC, are applied to wind forecasts from the high resolution ensemble system COSMO-DEEPS run operationally at the German...

  14. Enhancing COSMO-DE ensemble forecasts by inexpensive techniques

    Directory of Open Access Journals (Sweden)

    Zied Ben Bouallègue


    Full Text Available COSMO-DE-EPS, a convection-permitting ensemble prediction system based on the high-resolution numerical weather prediction model COSMO-DE, is pre-operational since December 2010, providing probabilistic forecasts which cover Germany. This ensemble system comprises 20 members based on variations of the lateral boundary conditions, the physics parameterizations and the initial conditions. In order to increase the sample size in a computationally inexpensive way, COSMO-DE-EPS is combined with alternative ensemble techniques: the neighborhood method and the time-lagged approach. Their impact on the quality of the resulting probabilistic forecasts is assessed. Objective verification is performed over a six months period, scores based on the Brier score and its decomposition are shown for June 2011. The combination of the ensemble system with the alternative approaches improves probabilistic forecasts of precipitation in particular for high precipitation thresholds. Moreover, combining COSMO-DE-EPS with only the time-lagged approach improves the skill of area probabilities for precipitation and does not deteriorate the skill of 2 m-temperature and wind gusts forecasts.

  15. Ensemble methods for seasonal limited area forecasts

    DEFF Research Database (Denmark)

    Arritt, Raymond W.; Anderson, Christopher J.; Takle, Eugene S.


    The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged....... The mixed-physics ensemble performed well in terms of equitable threat score, especially for higher precipitation amounts....

  16. Ensemble Kalman filtering with residual nudging

    KAUST Repository

    Luo, X.


    Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.

  17. Ensemble Kalman filtering with residual nudging

    Directory of Open Access Journals (Sweden)

    Xiaodong Luo


    Full Text Available Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF by (in effect adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.

  18. Ensembl Genomes 2013

    DEFF Research Database (Denmark)

    Kersey, Paul Julian; Allen, James E; Christensen, Mikkel


    , and provides a complementary set of resources for non-vertebrate species through a consistent set of programmatic and interactive interfaces. These provide access to data including reference sequence, gene models, transcriptional data, polymorphisms and comparative analysis. This article provides an update...

  19. Ensemble Kalman filtering with one-step-ahead smoothing

    KAUST Repository

    Raboudi, Naila F.


    The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.

  20. Constraining the ensemble Kalman filter for improved streamflow forecasting (United States)

    Maxwell, Deborah H.; Jackson, Bethanna M.; McGregor, James


    Data assimilation techniques such as the Ensemble Kalman Filter (EnKF) are often applied to hydrological models with minimal state volume/capacity constraints enforced during ensemble generation. Flux constraints are rarely, if ever, applied. Consequently, model states can be adjusted beyond physically reasonable limits, compromising the integrity of model output. In this paper, we investigate the effect of constraining the EnKF on forecast performance. A "free run" in which no assimilation is applied is compared to a completely unconstrained EnKF implementation, a 'typical' hydrological implementation (in which mass constraints are enforced to ensure non-negativity and capacity thresholds of model states are not exceeded), and then to a more tightly constrained implementation where flux as well as mass constraints are imposed to force the rate of water movement to/from ensemble states to be within physically consistent boundaries. A three year period (2008-2010) was selected from the available data record (1976-2010). This was specifically chosen as it had no significant data gaps and represented well the range of flows observed in the longer dataset. Over this period, the standard implementation of the EnKF (no constraints) contained eight hydrological events where (multiple) physically inconsistent state adjustments were made. All were selected for analysis. Mass constraints alone did little to improve forecast performance; in fact, several were significantly degraded compared to the free run. In contrast, the combined use of mass and flux constraints significantly improved forecast performance in six events relative to all other implementations, while the remaining two events showed no significant difference in performance. Placing flux as well as mass constraints on the data assimilation framework encourages physically consistent state estimation and results in more accurate and reliable forward predictions of streamflow for robust decision-making. We also

  1. Cluster Ensemble-Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoru Wang


    Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

  2. Development of Ensemble Model Based Water Demand Forecasting Model (United States)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop


    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  3. On the clustering of climate models in ensemble seasonal forecasting (United States)

    Yuan, Xing; Wood, Eric F.


    Multi-model ensemble seasonal forecasting system has expanded in recent years, with a dozen coupled climate models around the world being used to produce hindcasts or real-time forecasts. However, many models are sharing similar atmospheric or oceanic components which may result in similar forecasts. This raises questions of whether the ensemble is over-confident if we treat each model equally, or whether we can obtain an effective subset of models that can retain predictability and skill as well. In this study, we use a hierarchical clustering method based on inverse trigonometric cosine function of the anomaly correlation of pairwise model hindcasts to measure the similarities among twelve American and European seasonal forecast models. Though similarities are found between models sharing the same atmospheric component, different versions of models from the same center sometimes produce quite different temperature forecasts, which indicate that detailed physics packages such as radiation and land surface schemes need to be analyzed in interpreting the clustering result. Uncertainties in clustering for different forecast lead times also make reducing redundant models more complicated. Predictability analysis shows that multi-model ensemble is not necessarily better than a single model, while the cluster ensemble shows consistent improvement against individual models. The eight model-based cluster ensemble forecast shows comparable performance to the total twelve model ensemble in terms of probabilistic forecast skill for accuracy and discrimination. This study also manifests that models developed in U.S. and Europe are more independent from each other, suggesting the necessity of international collaboration in enhancing multi-model ensemble seasonal forecasting.

  4. Ensemble Data Assimilation to Characterize Surface-Layer Errors In Numerical Weather Prediction Models (United States)

    Hacker, Joshua; Angevine, Wayne


    Experiments with the single-column implementation of the Weather Research and Forecasting mesoscale model provide a basis for deducing land-atmosphere coupling errors in the model. Coupling occurs both through heat and moisture fluxes through the land-atmosphere interface and roughness sub-layer, and turbulent heat, moisture, and momentum fluxes through the atmospheric surface layer. This work primarily addresses the turbulent fluxes, which are parameterized following Monin-Obukhov similarity theory applied to the atmospheric surface layer. By combining ensemble data assimilation and parameter estimation, the model error can be characterized. Ensemble data assimilation of 2-m temperature and water vapor mixing ratio, and 10-m wind components, forces the model to follow observations during a month-long simulation for a column over the well-instrumented ARM Central Facility near Lamont, OK. One-hour errors in predicted observations are systematically small but non-zero, and the systematic errors measure bias as a function of local time of day. Analysis increments for state elements nearby (15-m AGL) can be too small or have the wrong sign, indicating systematically biased covariances and model error. Experiments using the ensemble filter to objectively estimate a parameter controlling the thermal land-atmosphere coupling show that the parameter adapts to offset the model errors, but that the errors cannot be eliminated. Results suggest either structural error or further parametric error that may be difficult to estimate. Experiments omitting atypical observations such as soil and flux measurements lead to qualitatively similar deductions, showing potential for assimilating common in-situ observations as an inexpensive framework for deducing and isolating model errors. We finish by presenting recent results from a deeper examination of the second-moment ensemble statistics, which demonstrate the effect of assimilation on the coupling through the stability function in

  5. Ensemble learned vaccination uptake prediction using web search queries


    Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre


    We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields...

  6. Assessment of ENSEMBLES regional climate models for the representation of monthly wind characteristics in the Aegean Sea (Greece): Mean and extremes analysis (United States)

    Anagnostopoulou, Christina; Tolika, Konstantia; Tegoulias, Ioannis; Velikou, Kondylia; Vagenas, Christos


    The main scope of the present study is the assessment of the ability of three of the most updated regional climate models, developed under the frame of the European research project ENSEMBLES (, to simulate the wind characteristics in the Aegean Sea in Greece. The examined models are KNMI-RACMO2, MPI-MREMO, and ICTP - RegCM3. They all have the same spatial resolution (25x25km) and for their future projections they are using the A1B SRES emission scenarios. Their simulated wind data (speed and direction) were compared with observational data from several stations over the domain of study for a time period of 25 years, from 1980 to 2004 on a monthly basis. The primer data were available every three or six hours from which we computed the mean daily wind speed and the prevailing daily wind direction. It should be mentioned, that the comparison was made for the grid point that was the closest to each station over land. Moreover, the extreme speed values were also calculated both for the observational and the simulated data, in order to assess the ability of the models in capturing the most intense wind conditions. The first results of the study showed that the prevailing winds during the winter and spring months have a north - northeastern or a south - south western direction in most parts of the Aegean sea. The models under examination seem to capture quite satisfactorily this pattern as well as the general characteristics of the winds in this area. During summer, winds in the Aegean Sea have mainly north direction and the models have quite good agreement both in simulating this direction and the wind speed. Concerning the extreme wind speed (percentiles) it was found that for the stations in the northern Aegean all the models overestimate the extreme wind indices. For the eastern parts of the Aegean the KNMI and the MPI model underestimate the extreme wind speeds while on the other hand the ICTP model overestimates them. Finally for the

  7. Transient regional climate change: analysis of the summer climate response in a high-resolution, century-scale, ensemble experiment over the continental United States (United States)

    Diffenbaugh, Noah S.; Ashfaq, Moetasim; Scherer, Martin


    Integrating the potential for climate change impacts into policy and planning decisions requires quantification of the emergence of sub-regional climate changes that could occur in response to transient changes in global radiative forcing. Here we report results from a high-resolution, century-scale, ensemble simulation of climate in the United States, forced by atmospheric constituent concentrations from the Special Report on Emissions Scenarios (SRES) A1B scenario. We find that 21st century summer warming permanently emerges beyond the baseline decadal-scale variability prior to 2020 over most areas of the continental U.S. Permanent emergence beyond the baseline annual-scale variability shows much greater spatial heterogeneity, with emergence occurring prior to 2030 over areas of the southwestern U.S., but not prior to the end of the 21st century over much of the southcentral and southeastern U.S. The pattern of emergence of robust summer warming contrasts with the pattern of summer warming magnitude, which is greatest over the central U.S. and smallest over the western U.S. In addition to stronger warming, the central U.S. also exhibits stronger coupling of changes in surface air temperature, precipitation, and moisture and energy fluxes, along with changes in atmospheric circulation towards increased anticylonic anomalies in the mid-troposphere and a poleward shift in the mid-latitude jet aloft. However, as a fraction of the baseline variability, the transient warming over the central U.S. is smaller than the warming over the southwestern or northeastern U.S., delaying the emergence of the warming signal over the central U.S. Our comparisons with observations and the Coupled Model Intercomparison Project Phase 3 (CMIP3) ensemble of global climate model experiments suggest that near-term global warming is likely to cause robust sub-regional-scale warming over areas that exhibit relatively little baseline variability. In contrast, where there is greater

  8. Hydrological Ensemble Prediction System (HEPS) (United States)

    Thielen-Del Pozo, J.; Schaake, J.; Martin, E.; Pailleux, J.; Pappenberger, F.


    Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Following on the success of the use of ensembles for weather forecasting, the hydrological community now moves increasingly towards Hydrological Ensemble Prediction Systems (HEPS) for improved flood forecasting using operationally available NWP products as inputs. However, these products are often generated on relatively coarse scales compared to hydrologically relevant basin units and suffer systematic biases that may have considerable impact when passed through the non-linear hydrological filters. Therefore, a better understanding on how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes is necessary. The "Hydrologic Ensemble Prediction Experiment" (HEPEX), is an international initiative consisting of hydrologists, meteorologist and end-users to advance probabilistic hydrologic forecast techniques for flood, drought and water management applications. Different aspects of the hydrological ensemble processor are being addressed including • Production of useful meteorological products relevant for hydrological applications, ranging from nowcasting products to seasonal forecasts. The importance of hindcasts that are consistent with the operational weather forecasts will be discussed to support bias correction and downscaling, statistically meaningful verification of HEPS, and the development and testing of operating rules; • Need for downscaling and post-processing of weather ensembles to reduce bias before entering hydrological applications; • Hydrological model and parameter uncertainty and how to correct and

  9. Fractal analysis of sound signals in SAMPO 3065 combine harvester

    Directory of Open Access Journals (Sweden)

    F Mahdiyeh Broujeni


    Full Text Available Introduction Nowadays, many studies were performed about noise source and its type and effects related to duration of sound emission. Most of these researches just report sound pressure level in frequency or time domain. These researches should be continued in order to find better absorber material in noise pollution. Use of fractal geometry is a new method in this filed. Wave fractal dimension value is a strong tool for diagnosis of signal instability and fractal analysis is a good method to finding sound signal characteristics. Therefore the aim of this study is on the fractal geometry of SAMPO 3065 combine harvester signals and determine the fractal dimension value of these signals in different operational conditions by Katz, Sevcik, Higuchi and MRBC methods. Materials and Methods In this research, sound signals of SAMPO 3065 harvester combine that were recorded by Maleki and Lashgari (2014, were analyzed. Engine speed (high and low, gear ratio (neutral, 1st, 2nd, 3rd gear, type of operation (traveling and harvesting and microphone position (in and out of the cabin were the main factors of this research. For determining signal fractal dimension value in time domain, wave shape supposed as a geometrical shape and for calculation of fractal dimension value of these signals, total area of wave shape was divided into boxes in 50, 100, 200 milliseconds with an interval 25 millisecond box. Then Fractal dimension value of these boxes was calculated by Katz, Sevcik, Higuchi and MRBC methods using MATLAB (2010a software. SPSS (Ver.20 software was used for further analysis. Results and Discussion Results showed mean effects of engine speed, microphone position, gear ratio, type of operation, box length, calculation method and all of two way interaction effects were significant. Means of Fractal Dimension in the road and field position were 1.4 and 1.28 respectively. The Maximum growth ratio of fractal dimension value during engine speed levels was

  10. Using histograms to introduce randomization in the generation of ensembles of decision trees (United States)

    Kamath, Chandrika; Cantu-Paz, Erick; Littau, David


    A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

  11. Analysis of the Nonlinear Trends and Non-Stationary Oscillations of Regional Precipitation in Xinjiang, Northwestern China, Using Ensemble Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    Bin Guo


    Full Text Available Changes in precipitation could have crucial influences on the regional water resources in arid regions such as Xinjiang. It is necessary to understand the intrinsic multi-scale variations of precipitation in different parts of Xinjiang in the context of climate change. In this study, based on precipitation data from 53 meteorological stations in Xinjiang during 1960–2012, we investigated the intrinsic multi-scale characteristics of precipitation variability using an adaptive method named ensemble empirical mode decomposition (EEMD. Obvious non-linear upward trends in precipitation were found in the north, south, east and the entire Xinjiang. Changes in precipitation in Xinjiang exhibited significant inter-annual scale (quasi-2 and quasi-6 years and inter-decadal scale (quasi-12 and quasi-23 years. Moreover, the 2–3-year quasi-periodic fluctuation was dominant in regional precipitation and the inter-annual variation had a considerable effect on the regional-scale precipitation variation in Xinjiang. We also found that there were distinctive spatial differences in variation trends and turning points of precipitation in Xinjiang. The results of this study indicated that compared to traditional decomposition methods, the EEMD method, without using any a priori determined basis functions, could effectively extract the reliable multi-scale fluctuations and reveal the intrinsic oscillation properties of climate elements.

  12. Analysis of the Nonlinear Trends and Non-Stationary Oscillations of Regional Precipitation in Xinjiang, Northwestern China, Using Ensemble Empirical Mode Decomposition. (United States)

    Guo, Bin; Chen, Zhongsheng; Guo, Jinyun; Liu, Feng; Chen, Chuanfa; Liu, Kangli


    Changes in precipitation could have crucial influences on the regional water resources in arid regions such as Xinjiang. It is necessary to understand the intrinsic multi-scale variations of precipitation in different parts of Xinjiang in the context of climate change. In this study, based on precipitation data from 53 meteorological stations in Xinjiang during 1960-2012, we investigated the intrinsic multi-scale characteristics of precipitation variability using an adaptive method named ensemble empirical mode decomposition (EEMD). Obvious non-linear upward trends in precipitation were found in the north, south, east and the entire Xinjiang. Changes in precipitation in Xinjiang exhibited significant inter-annual scale (quasi-2 and quasi-6 years) and inter-decadal scale (quasi-12 and quasi-23 years). Moreover, the 2-3-year quasi-periodic fluctuation was dominant in regional precipitation and the inter-annual variation had a considerable effect on the regional-scale precipitation variation in Xinjiang. We also found that there were distinctive spatial differences in variation trends and turning points of precipitation in Xinjiang. The results of this study indicated that compared to traditional decomposition methods, the EEMD method, without using any a priori determined basis functions, could effectively extract the reliable multi-scale fluctuations and reveal the intrinsic oscillation properties of climate elements.

  13. Data driven computing by the morphing fast Fourier transform ensemble Kalman filter in epidemic spread simulations (United States)

    Mandel, Jan; Beezley, Jonathan D.; Cobb, Loren; Krishnamurthy, Ashok


    The FFT EnKF data assimilation method is proposed and applied to a stochastic cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF combines spatial statistics and ensemble filtering methodologies into a localized and computationally inexpensive version of EnKF with a very small ensemble, and it is further combined with the morphing EnKF to assimilate changes in the position of the epidemic. PMID:21031155


    software and manual TEVA-SPOT is used by water utilities to optimize the number and location of contamination detection sensors so that economic and/or public health consequences are minimized. TEVA-SPOT is interactive, allowing a user to specify the minimization objective (e.g., the number of people exposed, the time to detection, or the extent of pipe length contaminated). It also allows a user to specify constraints. For example, a TEVA-SPOT user can employ expert knowledge during the design process by identifying either existing or unfeasible sensor locations. Installation and maintenance costs for sensor placement can also be factored into the analysis. Python and Java are required to run TEVA-SPOT

  15. Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy

    Directory of Open Access Journals (Sweden)

    Justin A. Schulte


    Full Text Available In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster skill score. Additional sub-ensemble and composite skill scores are proposed for assessing the forecast skill of a clustered ensemble forecast. A recently proposed method for statistically increasing the number of ensemble members is used to improve sub-ensemble probabilistic estimates. Through the application of the proposed methodology to Sandy coastal flood reforecasts, it is demonstrated that statistics computed using only ensemble members belonging to a specific cluster are more representative than those computed using all ensemble members simultaneously. A cluster skill-cluster uncertainty index relationship is identified, which is the cluster analog of the documented spread-skill relationship. Two sub-ensemble skill scores are shown to be positively correlated with cluster forecast skill, suggesting that skillfully forecasting the cluster into which the observation will fall is important to overall forecast skill. The identified relationships also suggest that the number of ensemble members within in each cluster can be used as guidance for assessing the potential for forecast error. The inevitable existence of ensemble member clusters in tidally dominated total water level prediction systems suggests that clustering is a necessary post-processing step for producing representative and skillful total water level forecasts.

  16. Ensembl Genomes 2013: scaling up access to genome-wide data (United States)

    Ensembl Genomes ( is an integrating resource for genome-scale data from non-vertebrate species. The project exploits and extends technologies for genome annotation, analysis and dissemination, developed in the context of the vertebrate-focused Ensembl project, and provi...

  17. An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter

    KAUST Repository

    Song, Hajoon


    A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.

  18. Heat fluctuations and initial ensembles (United States)

    Kim, Kwangmoo; Kwon, Chulan; Park, Hyunggyu


    Time-integrated quantities such as work and heat increase incessantly in time during nonequilibrium processes near steady states. In the long-time limit, the average values of work and heat become asymptotically equivalent to each other, since they only differ by a finite energy change in average. However, the fluctuation theorem (FT) for the heat is found not to hold with the equilibrium initial ensemble, while the FT for the work holds. This reveals an intriguing effect of everlasting initial memory stored in rare events. We revisit the problem of a Brownian particle in a harmonic potential dragged with a constant velocity, which is in contact with a thermal reservoir. The heat and work fluctuations are investigated with initial Boltzmann ensembles at temperatures generally different from the reservoir temperature. We find that, in the infinite-time limit, the FT for the work is fully recovered for arbitrary initial temperatures, while the heat fluctuations significantly deviate from the FT characteristics except for the infinite initial-temperature limit (a uniform initial ensemble). Furthermore, we succeed in calculating finite-time corrections to the heat and work distributions analytically, using the modified saddle point integral method recently developed by us. Interestingly, we find noncommutativity between the infinite-time limit and the infinite-initial-temperature limit for the probability distribution function (PDF) of the heat.

  19. Geographical classification of Epimedium based on HPLC fingerprint analysis combined with multi-ingredients quantitative analysis. (United States)

    Xu, Ning; Zhou, Guofu; Li, Xiaojuan; Lu, Heng; Meng, Fanyun; Zhai, Huaqiang


    A reliable and comprehensive method for identifying the origin and assessing the quality of Epimedium has been developed. The method is based on analysis of HPLC fingerprints, combined with similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and multi-ingredient quantitative analysis. Nineteen batches of Epimedium, collected from different areas in the western regions of China, were used to establish the fingerprints and 18 peaks were selected for the analysis. Similarity analysis, HCA and PCA all classified the 19 areas into three groups. Simultaneous quantification of the five major bioactive ingredients in the Epimedium samples was also carried out to confirm the consistency of the quality tests. These methods were successfully used to identify the geographical origin of the Epimedium samples and to evaluate their quality. Copyright © 2016 John Wiley & Sons, Ltd.

  20. Ensemble flare forecasting: using numerical weather prediction techniques to improve space weather operations (United States)

    Murray, S.; Guerra, J. A.


    One essential component of operational space weather forecasting is the prediction of solar flares. Early flare forecasting work focused on statistical methods based on historical flaring rates, but more complex machine learning methods have been developed in recent years. A multitude of flare forecasting methods are now available, however it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Current operational space weather centres cannot rely on automated methods, and generally use statistical forecasts with a little human intervention. Space weather researchers are increasingly looking towards methods used in terrestrial weather to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. It has proved useful in areas such as magnetospheric modelling and coronal mass ejection arrival analysis, however has not yet been implemented in operational flare forecasting. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASSA, ASAP, MAG4, MOSWOC, NOAA, and Solar Monitor). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. The results provide space weather forecasters with a set of parameters (combination weights, thresholds) that allow them to select the most appropriate values for constructing the 'best' ensemble forecast probability value, according to the performance metric of their choice. In this way different forecasts can be made to fit different end-user needs.

  1. Failure mode effect analysis and fault tree analysis as a combined methodology in risk management (United States)

    Wessiani, N. A.; Yoshio, F.


    There have been many studies reported the implementation of Failure Mode Effect Analysis (FMEA) and Fault Tree Analysis (FTA) as a method in risk management. However, most of the studies usually only choose one of these two methods in their risk management methodology. On the other side, combining these two methods will reduce the drawbacks of each methods when implemented separately. This paper aims to combine the methodology of FMEA and FTA in assessing risk. A case study in the metal company will illustrate how this methodology can be implemented. In the case study, this combined methodology will assess the internal risks that occur in the production process. Further, those internal risks should be mitigated based on their level of risks.

  2. A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly (United States)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong


    This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

  3. Ensembles of NLP Tools for Data Element Extraction from Clinical Notes. (United States)

    Kuo, Tsung-Ting; Rao, Pallavi; Maehara, Cleo; Doan, Son; Chaparro, Juan D; Day, Michele E; Farcas, Claudiu; Ohno-Machado, Lucila; Hsu, Chun-Nan


    Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.

  4. Ensemble dispersion forecasting - Part 1. Concept, approach and indicators

    DEFF Research Database (Denmark)

    Galmarini, S.; Bianconi, R.; Klug, W.


    The paper presents an approach to the treatment and analysis of long-range transport and dispersion model forecasts. Long-range is intended here as the space scale of the order of few thousands of kilometers known also as continental scale. The method is called multi-model ensemble dispersion and...

  5. Light localization in cold and dense atomic ensemble

    International Nuclear Information System (INIS)

    Sokolov, Igor


    We report on results of theoretical analysis of possibilities of light strong (Anderson) localization in a cold atomic ensemble. We predict appearance of localization in dense atomic systems in strong magnetic field. We prove that in absence of the field the light localization is impossible. (paper)

  6. Exergy analysis, parametric analysis and optimization for a novel combined power and ejector refrigeration cycle

    International Nuclear Information System (INIS)

    Dai Yiping; Wang Jiangfeng; Gao Lin


    A new combined power and refrigeration cycle is proposed, which combines the Rankine cycle and the ejector refrigeration cycle. This combined cycle produces both power output and refrigeration output simultaneously. It can be driven by the flue gas of gas turbine or engine, solar energy, geothermal energy and industrial waste heats. An exergy analysis is performed to guide the thermodynamic improvement for this cycle. And a parametric analysis is conducted to evaluate the effects of the key thermodynamic parameters on the performance of the combined cycle. In addition, a parameter optimization is achieved by means of genetic algorithm to reach the maximum exergy efficiency. The results show that the biggest exergy loss due to the irreversibility occurs in heat addition processes, and the ejector causes the next largest exergy loss. It is also shown that the turbine inlet pressure, the turbine back pressure, the condenser temperature and the evaporator temperature have significant effects on the turbine power output, refrigeration output and exergy efficiency of the combined cycle. The optimized exergy efficiency is 27.10% under the given condition.

  7. A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: application in the Lorenz system

    Directory of Open Access Journals (Sweden)

    Lili Lei


    Full Text Available A hybrid data assimilation approach combining nudging and the ensemble Kalman filter (EnKF for dynamic analysis and numerical weather prediction is explored here using the non-linear Lorenz three-variable model system with the goal of a smooth, continuous and accurate data assimilation. The hybrid nudging-EnKF (HNEnKF computes the hybrid nudging coefficients from the flow-dependent, time-varying error covariance matrix from the EnKF's ensemble forecasts. It extends the standard diagonal nudging terms to additional off-diagonal statistical correlation terms for greater inter-variable influence of the innovations in the model's predictive equations to assist in the data assimilation process. The HNEnKF promotes a better fit of an analysis to data compared to that achieved by either nudging or incremental analysis update (IAU. When model error is introduced, it produces similar or better root mean square errors compared to the EnKF while minimising the error spikes/discontinuities created by the intermittent EnKF. It provides a continuous data assimilation with better inter-variable consistency and improved temporal smoothness than that of the EnKF. Data assimilation experiments are also compared to the ensemble Kalman smoother (EnKS. The HNEnKF has similar or better temporal smoothness than that of the EnKS, and with much smaller central processing unit (CPU time and data storage requirements.

  8. Combined seismic plus live-load analysis of highway bridges. (United States)


    "The combination of seismic and vehicle live loadings on bridges is an important design consideration. There are well-established design : provisions for how the individual loadings affect bridge response: structural components that carry vertical li...

  9. Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

    KAUST Repository

    Liu, Bo


    We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.

  10. Wind Power Prediction using Ensembles

    DEFF Research Database (Denmark)

    Giebel, Gregor; Badger, Jake; Landberg, Lars


    offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...

  11. Multiscale Clock Ensembling Using Wavelets (United States)


    allows an energy decomposition of the signal as well, referred to as the wavelet variance. This variance is defined by ) var ()( 2 llX Wv  (11...and it can be shown that for a very wide class of signals and for an appropriately chosen wavelet that ) var ()( 1 2 Xv l lX     . One such...42 nd Annual Precise Time and Time Interval (PTTI) Meeting 527 MULTISCALE CLOCK ENSEMBLING USING WAVELETS Ken Senior Naval Center

  12. Statistical ensembles in quantum mechanics

    International Nuclear Information System (INIS)

    Blokhintsev, D.


    The interpretation of quantum mechanics presented in this paper is based on the concept of quantum ensembles. This concept differs essentially from the canonical one by that the interference of the observer into the state of a microscopic system is of no greater importance than in any other field of physics. Owing to this fact, the laws established by quantum mechanics are not of less objective character than the laws governing classical statistical mechanics. The paradoxical nature of some statements of quantum mechanics which result from the interpretation of the wave functions as the observer's notebook greatly stimulated the development of the idea presented. (Auth.)

  13. Improving wave forecasting by integrating ensemble modelling and machine learning (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.


    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  14. Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

    Directory of Open Access Journals (Sweden)

    Federico Divina


    Full Text Available The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.

  15. Exploiting ensemble learning for automatic cataract detection and grading. (United States)

    Yang, Ji-Jiang; Li, Jianqiang; Shen, Ruifang; Zeng, Yang; He, Jian; Bi, Jing; Li, Yong; Zhang, Qinyan; Peng, Lihui; Wang, Qing


    Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  16. Security Enrichment in Intrusion Detection System Using Classifier Ensemble

    Directory of Open Access Journals (Sweden)

    Uma R. Salunkhe


    Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

  17. Complementarity of structure ensembles in protein-protein binding. (United States)

    Grünberg, Raik; Leckner, Johan; Nilges, Michael


    Protein-protein association is often accompanied by changes in receptor and ligand structure. This interplay between protein flexibility and protein-protein recognition is currently the largest obstacle both to our understanding of and to the reliable prediction of protein complexes. We performed two sets of molecular dynamics simulations for the unbound receptor and ligand structures of 17 protein complexes and applied shape-driven rigid body docking to all combinations of representative snapshots. The crossdocking of structure ensembles increased the likelihood of finding near-native solutions. The free ensembles appeared to contain multiple complementary conformations. These were in general not related to the bound structure. We suggest that protein-protein binding follows a three-step mechanism of diffusion, free conformer selection, and refolding. This model combines previously conflicting ideas and is in better agreement with the current data on interaction forces, time scales, and kinetics.

  18. Multi-Model Ensemble Wake Vortex Prediction (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.


    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  19. Urban runoff forecasting with ensemble weather predictions

    DEFF Research Database (Denmark)

    Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca

    This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice.......This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice....

  20. Combining morphological analysis and Bayesian Networks for strategic decision support

    CSIR Research Space (South Africa)

    De Waal, AJ


    Full Text Available problem spaces. BNs are graphical models which consist of a qualitative and quantitative part. The qualitative part is a cause-and-effect, or causal graph. The quantitative part depicts the strength of the causal relationships between variables. Combining...

  1. Microscopes and computers combined for analysis of chromosomes (United States)

    Butler, J. W.; Butler, M. K.; Stroud, A. N.


    Scanning machine CHLOE, developed for photographic use, is combined with a digital computer to obtain quantitative and statistically significant data on chromosome shapes, distribution, density, and pairing. CHLOE permits data acquisition about a chromosome complement to be obtained two times faster than by manual pairing.

  2. Analysis of induction machines with combined stator windings

    Czech Academy of Sciences Publication Activity Database

    Schreier, Luděk; Bendl, Jiří; Chomát, Miroslav


    Roč. 60, č. 2 (2015), s. 155-171 ISSN 0001-7043 R&D Projects: GA ČR GA13-35370S Institutional support: RVO:61388998 Keywords : induction machines * symmetrical components * combined stator winding Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering

  3. Analysis of combining ability and heredity parameters of ...

    African Journals Online (AJOL)


    heterosis of anti-cancer glucosinolates of their hybrid was very high. By analyzing the SCA effects, 1 × 2 ... The in vitro test of human rectal cancer cells has proved that sulforaphane has the function of inhibiting .... The value of general combining ability effect of parents. Parents. PRO. Significant level of 5%. Significant.

  4. Model Based Analysis of the Variance Estimators for the Combined ...

    African Journals Online (AJOL)

    In this paper we study the variance estimators for the combined ratio estimator under an appropriate asymptotic framework. An alternative bias-robust variance estimator, different from that suggested by Valliant (1987), is derived. Several variance estimators are compared in an empirical study using a real population.

  5. Combining Linguistic and Spatial Information for Document Analysis

    NARCIS (Netherlands)

    Aiello, Marco; Monz, Christof; Todoran, Leon


    We present a framework to analyze color documents of complex layout. In addition, no assumption is made on the layout. Our framework combines in a content-driven bottom-up approach two different sources of information: textual and spatial. To analyze the text, shallow natural language processing

  6. Repeater Analysis for Combining Information from Different Assessments (United States)

    Haberman, Shelby; Yao, Lili


    Admission decisions frequently rely on multiple assessments. As a consequence, it is important to explore rational approaches to combine the information from different educational tests. For example, U.S. graduate schools usually receive both TOEFL iBT® scores and GRE® General scores of foreign applicants for admission; however, little guidance…

  7. A short-range ensemble prediction system for southern Africa

    CSIR Research Space (South Africa)

    Park, R


    Full Text Available numerical weather prediction system over southern Africa using the Conformal- Cubic Atmospheric Model (CCAM). An ensemble prediction system (EPS) combines several individual weather model setups into an average forecast system where each member... that are categorical and exact, with no statistical component. This indicates the ability of the model to predict exact values of precipitation events compared to observed data. From the results it can be seen that for a four-day lead time, the model performs well...

  8. Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time

    KAUST Repository

    Kelly, D. T B


    The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so for small ensemble size. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with respect to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz \\'63 and \\'96 models, together with the incompressible Navier-Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier-Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise.

  9. Online Learning through Moving Ensemble Teachers — An Exact Solution of a Linear Model — (United States)

    Nabetani, Takahiro; Miyoshi, Seiji


    Since a model in which a student learns from two or more teachers who themselves are learning has a certain similarity with actual human society, the analysis of such a model is interesting. In this paper, a model composed of a true teacher, multiple moving ensemble teachers existing around the true teacher, and a student, which are all linear perceptions, is analyzed using the statistical-mechanical method in the framework of on-line learning. The dependences of the generalization performance on the ensemble teachers' learning rate, the student's learning rate, and the number of ensemble teachers are clarified. Furthermore, it is shown that the generalization error can be reduced to the lower bound in the case of moving ensemble teachers, while there are unattainable generalization errors in the case of stationary ensemble teachers. These results show that it is important for teachers to continue learning in order to educate students.

  10. Continuous updating of a coupled reservoir-seismic model using an ensemble Kalman filter technique

    Energy Technology Data Exchange (ETDEWEB)

    Skjervheim, Jan-Arild


    that when time difference data are used in the EnKF, a combination of the ensemble Kalman filter and the ensemble Kalman smoother has to be applied. However, the method is still completely recursive, with little additional cost compared to the standard EnKF. An underestimation of the uncertainty of the model variables may become a problem when few ensemble members and a large amount of data are used during the assimilation. Thus, to improve the reservoir model updating we have proposed a methodology based on a combination of a global and a local analysis scheme. (author)

  11. A Statistical Ensemble for Soft Granular Matter (United States)

    Henkes, Silke; O'Hern, Corey; Chakraborty, Bulbul


    Work on packings of soft spheres (PRE 68, 011306 (2003)) has shown the existence of a Jamming transition and has highlighted the need for a general statistical framework to describe granular packings. This work presents an extension of the formalism proposed by Edwards (Physica A 157, 1080 (1989)) to packings of soft particles. We base our analysis on a height formalism developed in two dimensions (PRL 88, 115505 (2002)) to extract a topological invariant γ, the trace of the global stress tensor, which is conserved under internal rearrangements of the system. Upon assuming a flat measure in γ-space, we can derive a canonical distribution of the local γ-values in a grain packing. We then check the predictions of this ensemble against distributions of mechanically stable packings of frictionless disks obtained from computer simulations. Work supported by NSF-DMR 0549762.

  12. Combining Formal Logic and Machine Learning for Sentiment Analysis

    DEFF Research Database (Denmark)

    Petersen, Niklas Christoffer; Villadsen, Jørgen


    This paper presents a formal logical method for deep structural analysis of the syntactical properties of texts using machine learning techniques for efficient syntactical tagging. To evaluate the method it is used for entity level sentiment analysis as an alternative to pure machine learning...... methods for sentiment analysis, which often work on sentence or word level, and are argued to have difficulties in capturing long distance dependencies....

  13. Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook (United States)

    Sofiev, Mikhail; Ritenberga, Olga; Albertini, Roberto; Arteta, Joaquim; Belmonte, Jordina; Geller Bernstein, Carmi; Bonini, Maira; Celenk, Sevcan; Damialis, Athanasios; Douros, John; Elbern, Hendrik; Friese, Elmar; Galan, Carmen; Oliver, Gilles; Hrga, Ivana; Kouznetsov, Rostislav; Krajsek, Kai; Magyar, Donat; Parmentier, Jonathan; Plu, Matthieu; Prank, Marje; Robertson, Lennart; Steensen, Birthe Marie; Thibaudon, Michel; Segers, Arjo; Stepanovich, Barbara; Valdebenito, Alvaro M.; Vira, Julius; Vokou, Despoina


    The paper presents the first modelling experiment of the European-scale olive pollen dispersion, analyses the quality of the predictions, and outlines the research needs. A 6-model strong ensemble of Copernicus Atmospheric Monitoring Service (CAMS) was run throughout the olive season of 2014, computing the olive pollen distribution. The simulations have been compared with observations in eight countries, which are members of the European Aeroallergen Network (EAN). Analysis was performed for individual models, the ensemble mean and median, and for a dynamically optimised combination of the ensemble members obtained via fusion of the model predictions with observations. The models, generally reproducing the olive season of 2014, showed noticeable deviations from both observations and each other. In particular, the season was reported to start too early by 8 days, but for some models the error mounted to almost 2 weeks. For the end of the season, the disagreement between the models and the observations varied from a nearly perfect match up to 2 weeks too late. A series of sensitivity studies carried out to understand the origin of the disagreements revealed the crucial role of ambient temperature and consistency of its representation by the meteorological models and heat-sum-based phenological model. In particular, a simple correction to the heat-sum threshold eliminated the shift of the start of the season but its validity in other years remains to be checked. The short-term features of the concentration time series were reproduced better, suggesting that the precipitation events and cold/warm spells, as well as the large-scale transport, were represented rather well. Ensemble averaging led to more robust results. The best skill scores were obtained with data fusion, which used the previous days' observations to identify the optimal weighting coefficients of the individual model forecasts. Such combinations were tested for the forecasting period up to 4 days and

  14. Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF) (United States)

    Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.


    Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.

  15. Combining Formal Logic and Machine Learning for Sentiment Analysis

    DEFF Research Database (Denmark)

    Petersen, Niklas Christoffer; Villadsen, Jørgen


    This paper presents a formal logical method for deep structural analysis of the syntactical properties of texts using machine learning techniques for efficient syntactical tagging. To evaluate the method it is used for entity level sentiment analysis as an alternative to pure machine learning...

  16. Inserting Stress Analysis of Combined Hexagonal Aluminum Honeycombs

    Directory of Open Access Journals (Sweden)

    Xiangcheng Li


    Full Text Available Two kinds of hexagonal aluminum honeycombs are tested to study their out-of-plane crushing behavior. In the tests, honeycomb samples, including single hexagonal aluminum honeycomb (SHAH samples and two stack-up combined hexagonal aluminum honeycombs (CHAH samples, are compressed at a fixed quasistatic loading rate. The results show that the inserting process of CHAH can erase the initial peak stress that occurred in SHAH. Meanwhile, energy-absorbing property of combined honeycomb samples is more beneficial than the one of single honeycomb sample with the same thickness if the two types of honeycomb samples are completely crushed. Then, the applicability of the existing theoretical model for single hexagonal honeycomb is discussed, and an area equivalent method is proposed to calculate the crushing stress for nearly regular hexagonal honeycombs. Furthermore, a semiempirical formula is proposed to calculate the inserting plateau stress of two stack-up CHAH, in which structural parameters and mechanics properties of base material are concerned. The results show that the predicted stresses of three kinds of two stack-up combined honeycombs are in good agreement with the experimental data. Based on this study, stress-displacement curve of aluminum honeycombs can be designed in detail, which is very beneficial to optimize the energy-absorbing structures in engineering fields.

  17. A Thermorisk framework for the analysis of energy systems by combining risk and exergy analysis

    International Nuclear Information System (INIS)

    Cassetti, G.; Colombo, E.; Zio, E.


    Highlights: • An exergy based analysis for improving efficiency and safety of energy systems is presented. • The relation between thermodynamic parameters and the safety characteristics is identified. • Possible modifications in the process are indicated to improve the safety of the system. - Abstract: The impact of energy production, transformation and use on the environmental resources encourage to understand the mechanisms of resource degradation and to develop proper analyses to reduce the impact of the energy systems on the environment. At the technical level, most attempts for reducing the environmental impact of energy systems focus on the improvement of process efficiency. One way toward an integrated approach is that of adopting exergy analysis for assessing efficiency and test improving design and operation solutions. The paper presents an exergy based analysis for improving efficiency and safety of energy systems, named Thermorisk analysis. The purpose of the Thermorisk analysis is to supply information to control, and eventually reduce, the risk of the systems (i.e. risk of accidents) by acting on the thermodynamic parameters and safety characteristics in the same frame. The proper combination of exergy and risk analysis allows monitoring the effects of efficiency improvement on the safety of the systems analyzed. A case study is presented, showing the potential of the analysis to identify the relation between the exergy efficiency and the risk of the system analyzed, and the contribution of inefficiencies on the safety of the process. Possible modifications in the process are indicated to improve the safety of the system.

  18. EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms

    KAUST Repository

    Rapakoulia, Trisevgeni


    Motivation: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem ofmissing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a twostep algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. © The Author 2014.

  19. Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation

    Directory of Open Access Journals (Sweden)

    E. Crestani


    Full Text Available Estimating the spatial variability of hydraulic conductivity K in natural aquifers is important for predicting the transport of dissolved compounds. Especially in the nonreactive case, the plume evolution is mainly controlled by the heterogeneity of K. At the local scale, the spatial distribution of K can be inferred by combining the Lagrangian formulation of the transport with a Kalman-filter-based technique and assimilating a sequence of time-lapse concentration C measurements, which, for example, can be evaluated on site through the application of a geophysical method. The objective of this work is to compare the ensemble Kalman filter (EnKF and the ensemble smoother (ES capabilities to retrieve the hydraulic conductivity spatial distribution in a groundwater flow and transport modeling framework. The application refers to a two-dimensional synthetic aquifer in which a tracer test is simulated. Moreover, since Kalman-filter-based methods are optimal only if each of the involved variables fit to a Gaussian probability density function (pdf and since this condition may not be met by some of the flow and transport state variables, issues related to the non-Gaussianity of the variables are analyzed and different transformation of the pdfs are considered in order to evaluate their influence on the performance of the methods. The results show that the EnKF reproduces with good accuracy the hydraulic conductivity field, outperforming the ES regardless of the pdf of the concentrations.

  20. Ensembling Variable Selectors by Stability Selection for the Cox Model

    Directory of Open Access Journals (Sweden)

    Qing-Yan Yin


    Full Text Available As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010, a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

  1. In silico design of Mycobacterium tuberculosis epitope ensemble vaccines. (United States)

    Shah, Preksha; Mistry, Jaymisha; Reche, Pedro A; Gatherer, Derek; Flower, Darren R


    Effective control of Mycobacterium tuberculosis is a global necessity. In 2015, tuberculosis (TB) caused more deaths than HIV. Considering the increasing prevalence of multi-drug resistant forms of M. tuberculosis, the need for effective TB vaccines becomes imperative. Currently, the only licensed TB vaccine is Bacillus Calmette-Guérin (BCG). Yet, BCG has many drawbacks limiting its efficacy and applicability. We applied advanced computational procedures to derive a universal TB vaccine and one targeting East Africa. Our approach selects an optimal set of highly conserved, experimentally validated epitopes, with high projected population coverage (PPC). Through rigorous data analysis, five different potential vaccine combinations were selected each with PPC above 80% for East Africa and above 90% for the World. Two potential vaccines only contained CD8+ epitopes, while the others included both CD4+ and CD8+ epitopes. Our prime vaccine candidate was a putative seven-epitope ensemble comprising: SRGWSLIKSVRLGNA, KPRIITLTMNPALDI, AAHKGLMNIALAISA, FPAGGSTGSL, MLLAVTVSL, QSSFYSDW and KMRCGAPRY, with a 97.4% global PPC and a 92.7% East African PPC. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Popular Music and the Instrumental Ensemble. (United States)

    Boespflug, George


    Discusses popular music, the role of the musical performer as a creator, and the styles of jazz and popular music. Describes the pop ensemble at the college level, focusing on improvisation, rehearsals, recording, and performance. Argues that pop ensembles be used in junior and senior high school. (CMK)

  3. Impact of climate change on precipitation distribution and water availability in the Nile using CMIP5 GCM ensemble. (United States)

    Mekonnen, Z. T.; Gebremichael, M.


    ABSTRACT In a basin like the Nile where millions of people depend on rainfed agriculture and surface water resources for their livelihoods, changes in precipitation will have tremendous social and economic consequences. General circulation models (GCMs) have been associated with high uncertainty in their projection of future precipitation for the Nile basin. Some studies tried to compare performance of different GCMs by doing a Multi-Model comparison for the region. Many indicated that there is no single model that gives the "best estimate" of precipitation for a very complex and large basin like the Nile. In this study, we used a combination of satellite and long term rain gauge precipitation measurements (TRMM and CenTrends) to evaluate the performance of 10 GCMs from the 5th Coupled Model Intercomparison Project (CMIP5) at different spatial and seasonal scales and produce a weighted ensemble projection. Our results confirm that there is no single model that gives best estimate over the region, hence the approach of creating an ensemble depending on how the model performed in specific areas and seasons resulted in an improved estimate of precipitation compared with observed values. Following the same approach, we created an ensemble of future precipitation projections for four different time periods (2000-2024, 2025-2049 and 2050-2100). The analysis showed that all the major sub-basins of the Nile will get will get more precipitation with time, even though the distribution with in the sub basin might be different. Overall the analysis showed a 15 % increase (125 mm/year) by the end of the century averaged over the area up to the Aswan dam. KEY WORDS: Climate Change, CMIP5, Nile, East Africa, CenTrends, Precipitation, Weighted Ensembles

  4. Path planning in uncertain flow fields using ensemble method

    KAUST Repository

    Wang, Tong


    An ensemble-based approach is developed to conduct optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of the sampling strategy and to develop insight into extensions dealing with general circulation ocean models. In particular, the ensemble method enables us to perform a statistical analysis of travel times and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.

  5. Reducing Risk of Noise-Induced Hearing Loss in Collegiate Music Ensembles Using Ambient Technology. (United States)

    Powell, Jason; Chesky, Kris


    Student musicians are at risk for noise-induced hearing loss (NIHL) as they develop skills and perform during instructional activities. Studies using longitudinal dosimeter data show that pedagogical procedures and instructor behaviors are highly predictive of NIHL risk, thus implying the need for innovative approaches to increase instructor competency in managing instructional activities without interfering with artistic and academic freedom. Ambient information systems, an emerging trend in human-computer interaction that infuses psychological behavioral theories into technologies, can help construct informative risk-regulating systems. The purpose of this study was to determine the effects of introducing an ambient information system into the ensemble setting. The system used two ambient displays and a counterbalanced within-subjects treatment study design with six jazz ensemble instructors to determine if the system could induce a behavior change that alters trends in measures resulting from dosimeter data. This study assessed efficacy using time series analysis to determine changes in eight statistical measures of behavior over a 9-wk period. Analysis showed that the system was effective, as all instructors showed changes in a combination of measures. This study is in an important step in developing non-interfering technology to reduce NIHL among academic musicians.

  6. Characterizing Ensembles of Superconducting Qubits (United States)

    Sears, Adam; Birenbaum, Jeff; Hover, David; Rosenberg, Danna; Weber, Steven; Yoder, Jonilyn L.; Kerman, Jamie; Gustavsson, Simon; Kamal, Archana; Yan, Fei; Oliver, William

    We investigate ensembles of up to 48 superconducting qubits embedded within a superconducting cavity. Such arrays of qubits have been proposed for the experimental study of Ising Hamiltonians, and efficient methods to characterize and calibrate these types of systems are still under development. Here we leverage high qubit coherence (> 70 μs) to characterize individual devices as well as qubit-qubit interactions, utilizing the common resonator mode for a joint readout. This research was funded by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under Air Force Contract No. FA8721-05-C-0002. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the US Government.

  7. Percolation in the canonical ensemble (United States)

    Hu, Hao; Blöte, Henk W. J.; Deng, Youjin


    We study the bond percolation problem under the constraint that the total number of occupied bonds is fixed, so that the canonical ensemble applies. We show via an analytical approach that at criticality, the constraint can induce new finite-size corrections with exponent ycan = 2yt - d both in energy-like and magnetic quantities, where yt = 1/ν is the thermal renormalization exponent and d is the spatial dimension. Furthermore, we find that while most of the universal parameters remain unchanged, some universal amplitudes, like the excess cluster number, can be modified and become non-universal. We confirm these predictions by extensive Monte Carlo simulations of the two-dimensional percolation problem which has ycan = -1/2. This article is part of ‘Lattice models and integrability’, a special issue of Journal of Physics A: Mathematical and Theoretical in honour of F Y Wu's 80th birthday.

  8. Ensemble methods with outliers for phonocardiogram classification. (United States)

    Nabhan Homsi, Masun; Warrick, Philip


    Heart sound classification and analysis play an important role in the early diagnosis and prevention of cardiovascular disease. To this end, this paper introduces a novel method for automatic classification of normal and abnormal heart sound recordings. Signals are first preprocessed to extract a total of 131 features in the time, frequency, wavelet and statistical domains from the entire signal and from the timings of the states. Outlier signals are then detected and separated from those with a standard range using an interquartile range algorithm. After that, feature extreme values are given special consideration, and finally features are reduced to the most significant ones using a feature reduction technique. In the classification stage, the selected features either for standard or outlier signals are fed separately into an ensemble of 20 two-step classifiers for the classification task. The first step of the classifier is represented by a nested set of ensemble algorithms which was cross-validated on the training dataset provided by PhysioNet Challenge 2016, while the second one uses a voting rule of the class label. The results show that this method is able to recognize heart sound recordings efficiently, achieving an overall score of 96.30% for standard signals and 90.18% for outlier signals on a cross-validated experiment using the available training data. The approach of our proposed method helped reduce overfitting and improved classification performance, achieving an overall score on the hidden test set of 80.1% (79.6% sensitivity and 80.6% specificity).

  9. Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0

    Directory of Open Access Journals (Sweden)

    M. Rautenhaus


    Full Text Available We present "Met.3D", a new open-source tool for the interactive three-dimensional (3-D visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns; however, it is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output – 3-D visualization, ensemble visualization and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts (ECMWF and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantities. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign.

  10. Texture, residual stress and structural analysis of thin films using a combined X-ray analysis

    International Nuclear Information System (INIS)

    Lutterotti, L.; Chateigner, D.; Ferrari, S.; Ricote, J.


    Advanced thin films for today's industrial and research needs require highly specialized methodologies for a successful quantitative characterization. In particular, in the case of multilayer and/or unknown phases a global approach is necessary to obtain some or all the required information. A full approach has been developed integrating novel texture and residual stress methodologies with the Rietveld method (Acta Cryst. 22 (1967) 151) (for crystal structure analysis) and it has been coupled with the reflectivity analysis. The complete analysis can be done at once and offers several benefits: the thicknesses obtained from reflectivity can be used to correct the diffraction spectra, the phase analysis help to identify the layers and to determine the electron density profile for reflectivity; quantitative texture is needed for quantitative phase and residual stress analyses; crystal structure determination benefits of the previous. To achieve this result, it was necessary to develop some new methods, especially for texture and residual stresses. So it was possible to integrate them in the Rietveld, full profile fitting of the patterns. The measurement of these spectra required a special reflectometer/diffractometer that combines a thin parallel beam (for reflectivity) and a texture/stress goniometer with a curved large position sensitive detector. This new diffraction/reflectivity X-ray machine has been used to test the combined approach. Several spectra and the reflectivity patterns have been collected at different tilting angles and processed at once by the special software incorporating the aforementioned methodologies. Some analysis examples will be given to show the possibilities offered by the method

  11. Stochastic analysis of residential micro combined heat and power system

    DEFF Research Database (Denmark)

    Karami, H.; Sanjari, M. J.; Gooi, H. B.


    In this paper the combined heat and power functionality of a fuel-cell in a residential hybrid energy system, including a battery, is studied. The demand uncertainties are modeled by investigating the stochastic load behavior by applying Monte Carlo simulation. The colonial competitive algorithm...... is adopted to the hybrid energy system scheduling problem and different energy resources are optimally scheduled to have optimal operating cost of hybrid energy system. In order to show the effectiveness of the colonial competitive algorithm, the results are compared with the results of the harmony search...... algorithm. The optimized scheduling of different energy resources is listed in an efficient look-up table for all time intervals. The effects of time of use and the battery efficiency and its size are investigated on the operating cost of the hybrid energy system. The results of this paper are expected...

  12. An estimation of Erinaceidae phylogeny: a combined analysis approach. (United States)

    He, Kai; Chen, Jian-Hai; Gould, Gina C; Yamaguchi, Nobuyuki; Ai, Huai-Sen; Wang, Ying-Xiang; Zhang, Ya-Ping; Jiang, Xue-Long


    Erinaceidae is a family of small mammals that include the spiny hedgehogs (Erinaceinae) and the silky-furred moonrats and gymnures (Galericinae). These animals are widely distributed across Eurasia and Africa, from the tundra to the tropics and the deserts to damp forests. The importance of these animals lies in the fact that they are the oldest known living placental mammals, which are well represented in the fossil record, a rarity fact given their size and vulnerability to destruction during fossilization. Although the Family has been well studied, their phylogenetic relationships remain controversial. To test previous phylogenetic hypotheses, we combined molecular and morphological data sets, including representatives of all the genera. We included in the analyses 3,218 bp mitochondrial genes, one hundred and thirty-five morphological characters, twenty-two extant erinaceid taxa, and five outgroup taxa. Phylogenetic relationships were reconstructed using both partitioned and combined data sets. As in previous analyses, our results strongly support the monophyly of both subfamilies (Galericinae and Erinaceinae), the Hylomys group (to include Neotetracus and Neohylomys), and a sister-relationship of Atelerix and Erinaceus. As well, we verified that the extremely long branch lengths within the Galericinae are consistent with their fossil records. Not surprisingly, we found significant incongruence between the phylogenetic signals of the genes and the morphological characters, specifically in the case of Hylomys parvus, Mesechinus, and relationships between Hemiechinus and Paraechinus. Although we discovered new clues to understanding the evolutionary relationships within the Erinaceidae, our results nonetheless, strongly suggest that more robust analyses employing more complete taxon sampling (to include fossils) and multiple unlinked genes would greatly enhance our understanding of the Erinaceidae. Until then, we have left the nomenclature of the taxa unchanged

  13. An estimation of Erinaceidae phylogeny: a combined analysis approach.

    Directory of Open Access Journals (Sweden)

    Kai He

    Full Text Available BACKGROUND: Erinaceidae is a family of small mammals that include the spiny hedgehogs (Erinaceinae and the silky-furred moonrats and gymnures (Galericinae. These animals are widely distributed across Eurasia and Africa, from the tundra to the tropics and the deserts to damp forests. The importance of these animals lies in the fact that they are the oldest known living placental mammals, which are well represented in the fossil record, a rarity fact given their size and vulnerability to destruction during fossilization. Although the Family has been well studied, their phylogenetic relationships remain controversial. To test previous phylogenetic hypotheses, we combined molecular and morphological data sets, including representatives of all the genera. METHODOLOGY AND PRINCIPAL FINDINGS: We included in the analyses 3,218 bp mitochondrial genes, one hundred and thirty-five morphological characters, twenty-two extant erinaceid taxa, and five outgroup taxa. Phylogenetic relationships were reconstructed using both partitioned and combined data sets. As in previous analyses, our results strongly support the monophyly of both subfamilies (Galericinae and Erinaceinae, the Hylomys group (to include Neotetracus and Neohylomys, and a sister-relationship of Atelerix and Erinaceus. As well, we verified that the extremely long branch lengths within the Galericinae are consistent with their fossil records. Not surprisingly, we found significant incongruence between the phylogenetic signals of the genes and the morphological characters, specifically in the case of Hylomys parvus, Mesechinus, and relationships between Hemiechinus and Paraechinus. CONCLUSIONS: Although we discovered new clues to understanding the evolutionary relationships within the Erinaceidae, our results nonetheless, strongly suggest that more robust analyses employing more complete taxon sampling (to include fossils and multiple unlinked genes would greatly enhance our understanding of the

  14. Probability Maps for the Visualization of Assimilation Ensemble Flow Data

    KAUST Repository

    Hollt, Thomas


    Ocean forecasts nowadays are created by running ensemble simulations in combination with data assimilation techniques. Most of these techniques resample the ensemble members after each assimilation cycle. This means that in a time series, after resampling, every member can follow up on any of the members before resampling. Tracking behavior over time, such as all possible paths of a particle in an ensemble vector field, becomes very difficult, as the number of combinations rises exponentially with the number of assimilation cycles. In general a single possible path is not of interest but only the probabilities that any point in space might be reached by a particle at some point in time. In this work we present an approach using probability-weighted piecewise particle trajectories to allow such a mapping interactively, instead of tracing quadrillions of individual particles. We achieve interactive rates by binning the domain and splitting up the tracing process into the individual assimilation cycles, so that particles that fall into the same bin after a cycle can be treated as a single particle with a larger probability as input for the next time step. As a result we loose the possibility to track individual particles, but can create probability maps for any desired seed at interactive rates.

  15. On the incidence of meteorological and hydrological processors: Effect of resolution, sharpness and reliability of hydrological ensemble forecasts (United States)

    Abaza, Mabrouk; Anctil, François; Fortin, Vincent; Perreault, Luc


    Meteorological and hydrological ensemble prediction systems are imperfect. Their outputs could often be improved through the use of a statistical processor, opening up the question of the necessity of using both processors (meteorological and hydrological), only one of them, or none. This experiment compares the predictive distributions from four hydrological ensemble prediction systems (H-EPS) utilising the Ensemble Kalman filter (EnKF) probabilistic sequential data assimilation scheme. They differ in the inclusion or not of the Distribution Based Scaling (DBS) method for post-processing meteorological forecasts and the ensemble Bayesian Model Averaging (ensemble BMA) method for hydrological forecast post-processing. The experiment is implemented on three large watersheds and relies on the combination of two meteorological reforecast products: the 4-member Canadian reforecasts from the Canadian Centre for Meteorological and Environmental Prediction (CCMEP) and the 10-member American reforecasts from the National Oceanic and Atmospheric Administration (NOAA), leading to 14 members at each time step. Results show that all four tested H-EPS lead to resolution and sharpness values that are quite similar, with an advantage to DBS + EnKF. The ensemble BMA is unable to compensate for any bias left in the precipitation ensemble forecasts. On the other hand, it succeeds in calibrating ensemble members that are otherwise under-dispersed. If reliability is preferred over resolution and sharpness, DBS + EnKF + ensemble BMA performs best, making use of both processors in the H-EPS system. Conversely, for enhanced resolution and sharpness, DBS is the preferred method.

  16. Combined Aero and Underhood Thermal Analysis for Heavy Duty Trucks

    Energy Technology Data Exchange (ETDEWEB)

    Vegendla, Prasad [Argonne National Lab. (ANL), Argonne, IL (United States); Sofu, Tanju [Argonne National Lab. (ANL), Argonne, IL (United States); Saha, Rohit [Cummins Inc., Columbus, IN (United States); Madurai Kumar, Mahesh [Cummins Inc., Columbus, IN (United States); Hwang, L. K [Cummins Inc., Columbus, IN (United States)


    Aerodynamic analysis of the medium-duty delivery truck was performed to achieve vehicle design optimization. Three dimensional CFD simulations were carried out for several improved designs, with a detailed external component analysis of wheel covers, side skirts, roof fairings, and rounded trailer corners. The overall averaged aerodynamics drag reduction through the design modifications were shown up to 22.3% through aerodynamic considerations alone, which is equivalent to 11.16% fuel savings. The main identified fuel efficiencies were based on second generation devices, including wheel covers, side skirts, roof fairings, and rounded trailer corners. The important findings of this work were; (i) the optimum curvature radius of the rounded trailer edges found to be 125 mm, with an arc length of 196.3 mm, (ii) aerodynamic drag reduction increases with dropping clearance of side skirts between wheels and ground, and (iii) aerodynamic drag reduction increases with an extension of front bumper towards the ground.


    Ahn, Yeong Hee; Kim, Jin Young; Yoo, Jong Shin


    Mass spectrometry (MS) has been a core technology for high sensitive and high-throughput analysis of the enriched glycoproteome in aspects of quantitative assays as well as qualitative profiling of glycoproteins. Because it has been widely recognized that aberrant glycosylation in a glycoprotein may involve in progression of a certain disease, the development of efficient analysis tool for the aberrant glycoproteins is very important for deep understanding about pathological function of the glycoprotein and new biomarker development. This review first describes the protein glycosylation-targeting enrichment technologies mainly employing solid-phase extraction methods such as hydrizide-capturing, lectin-specific capturing, and affinity separation techniques based on porous graphitized carbon, hydrophilic interaction chromatography, or immobilized boronic acid. Second, MS-based quantitative analysis strategies coupled with the protein glycosylation-targeting enrichment technologies, by using a label-free MS, stable isotope-labeling, or targeted multiple reaction monitoring (MRM) MS, are summarized with recent published studies. © 2014 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc. Rapid Commun. Mass Spec Rev 34:148–165, 2015. PMID:24889823

  18. Thermodynamic analysis of a novel integrated solar combined cycle

    International Nuclear Information System (INIS)

    Li, Yuanyuan; Yang, Yongping


    Highlights: • A novel ISCC scheme with two-stage DSG fields has been proposed and analyzed. • HRSG and steam turbine working parameters have been optimized to match the solar integration. • New scheme exhibits higher solar shares in the power output and solar-to-electricity efficiency. • Thermodynamic performances between new and reference systems have been investigated and compared. - Abstract: Integrated solar combined cycle (ISCC) systems have become more and more popular due to their high fuel and solar energy utilization efficiencies. Conventional ISCC systems with direct steam generation (DSG) have only one-stage solar input. A novel ISCC with DSG system has been proposed and analyzed in this paper. The new system consists two-stage solar input, which would significantly increase solar share in the total power output. Moreover, how and where solar energy is input into ISCC system would have impact on the solar and system overall efficiencies, which have been analyzed in the paper. It has been found that using solar heat to supply latent heat for vaporization of feedwater would be superior to that to be used for sensible heating purposes (e.g. Superheating steam). The study shows that: (1) producing both the high- and low-pressure saturated steam in the DSG trough collector could be an efficient way to improve process and system performance; (2) for a given live steam pressure, the optimum secondary and reheat steam conditions could be matched to reach the highest system thermal efficiency and net solar-to-electricity efficiency; (3) the net solar-to-electricity efficiency could reach up to 30% in the novel two-stage ISCC system, higher than that in the one-stage ISCC power plant; (4) compared with the conventional combined cycle gas turbine (CCGT) power system, lower stack temperature could be achieved, owing to the elimination of the approach-temperature-difference constraint, resulting in better thermal match in the heat recovery steam generator

  19. Stochastic analysis of residential micro combined heat and power system

    International Nuclear Information System (INIS)

    Karami, H.; Sanjari, M.J.; Gooi, H.B.; Gharehpetian, G.B.; Guerrero, J.M.


    Highlights: • Applying colonial competitive algorithm to the problem of optimal dispatching. • Economic modeling of the residential integrated energy system. • Investigating differences of stand-alone and system-connected modes of fuel cell operation. • Considering uncertainty on the electrical load. • The effects of battery capacity and its efficiency on the system is investigated. - Abstract: In this paper the combined heat and power functionality of a fuel-cell in a residential hybrid energy system, including a battery, is studied. The demand uncertainties are modeled by investigating the stochastic load behavior by applying Monte Carlo simulation. The colonial competitive algorithm is adopted to the hybrid energy system scheduling problem and different energy resources are optimally scheduled to have optimal operating cost of hybrid energy system. In order to show the effectiveness of the colonial competitive algorithm, the results are compared with the results of the harmony search algorithm. The optimized scheduling of different energy resources is listed in an efficient look-up table for all time intervals. The effects of time of use and the battery efficiency and its size are investigated on the operating cost of the hybrid energy system. The results of this paper are expected to be used effectively in a real hybrid energy system.

  20. Combined analysis of fourteen nuclear genes refines the Ursidae phylogeny. (United States)

    Pagès, Marie; Calvignac, Sébastien; Klein, Catherine; Paris, Mathilde; Hughes, Sandrine; Hänni, Catherine


    Despite numerous studies, questions remain about the evolutionary history of Ursidae and additional independent genetic markers were needed to elucidate these ambiguities. For this purpose, we sequenced ten nuclear genes for all the eight extant bear species. By combining these new sequences with those of four other recently published nuclear markers, we provide new insights into the phylogenetic relationships of the Ursidae family members. The hypothesis that the giant panda was the first species to diverge among ursids is definitively confirmed and the precise branching order within the Ursus genus is clarified for the first time. Moreover, our analyses indicate that the American and the Asiatic black bears do not cluster as sister taxa, as had been previously hypothesised. Sun and sloth bears clearly appear as the most basal ursine species but uncertainties about their exact relationships remain. Since our larger dataset did not enable us to clarify this last question, identifying rare genomic changes in bear genomes could be a promising solution for further studies.

  1. Ensemble learned vaccination uptake prediction using web search queries

    DEFF Research Database (Denmark)

    Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre


    We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official...... vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the ?first study to predict vaccination uptake...

  2. Ensemble prediction of floods – catchment non-linearity and forecast probabilities

    Directory of Open Access Journals (Sweden)

    C. Reszler


    Full Text Available Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to examine how the ensemble distribution of precipitation forecasts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km2 Kamp catchment in Austria as an example where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed continuous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analyses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment response. In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and less, the variability of the precipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed precipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncertainty in forecast precipitation as the dominant source of uncertainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Operating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.

  3. Preliminary Assessment of Tecplot Chorus for Analyzing Ensemble of CTH Simulations

    Energy Technology Data Exchange (ETDEWEB)

    Agelastos, Anthony Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stevenson, Joel O. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Attaway, Stephen W. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Peterson, David


    The exploration of large parameter spaces in search of problem solution and uncertainty quantifcation produces very large ensembles of data. Processing ensemble data will continue to require more resources as simulation complexity and HPC platform throughput increase. More tools are needed to help provide rapid insight into these data sets to decrease manual processing time by the analyst and to increase knowledge the data can provide. One such tool is Tecplot Chorus, whose strengths are visualizing ensemble metadata and linked images. This report contains the analysis and conclusions from evaluating Tecplot Chorus with an example problem that is relevant to Sandia National Laboratories.

  4. Hybrid quantum circuit with a superconducting qubit coupled to a spin ensemble (United States)

    Kubo, Yuimaru; Grezes, Cecile; Dewes, Andreas; Vion, Denis; Isoya, Junichi; Jacques, Vincent; Dreau, Anais; Roch, Jean-Francois; Diniz, Igor; Auffeves, Alexia; Esteve, Daniel; Bertet, Patrice


    We report the experimental realization of a hybrid quantum circuit combining a superconducting qubit and an ensemble of electronic spins. The qubit, of the transmon type, is coherently coupled to the spin ensemble consisting of nitrogen-vacancy (NV) centers in a diamond crystal via a frequency-tunable superconducting resonator acting as a quantum bus [1,2]. Using this circuit, we prepare arbitrary superpositions of the qubit states that we store into collective excitations of the spin ensemble and retrieve back later on into the qubit [3]. These results constitute a first proof of concept of spin-ensemble based quantum memory for superconducting qubits.[4pt] [1] Y. Kubo et al., Phys. Rev. Lett. 105, 140502 (2010).[0pt] [2] Y. Kubo et al., arXiv: 1109.3960.[0pt] [3] Y. Kubo et al., arXiv: 1110.2978.


    Directory of Open Access Journals (Sweden)

    B.N. Prathibha


    Full Text Available Breast cancer is a primary cause of mortality and morbidity in women. Reports reveal that earlier the detection of abnormalities, better the improvement in survival. Digital mammograms are one of the most effective means for detecting possible breast anomalies at early stages. Digital mammograms supported with Computer Aided Diagnostic (CAD systems help the radiologists in taking reliable decisions. The proposed CAD system extracts wavelet features and spectral features for the better classification of mammograms. The Support Vector Machines classifier is used to analyze 206 mammogram images from Mias database pertaining to the severity of abnormality, i.e., benign and malign. The proposed system gives 93.14% accuracy for discrimination between normal-malign and 87.25% accuracy for normal-benign samples and 89.22% accuracy for benign-malign samples. The study reveals that features extracted in hybrid transform domain with SVM classifier proves to be a promising tool for analysis of mammograms.

  6. Combined QCD and electroweak analysis of HERA data

    CERN Document Server

    Abramowicz, H; Adamczyk, L; Adamus, M; Antonelli, S; Aushev, V; Behnke, O; Behrens, U; Bertolin, A; Bloch, I; Boos, EG; Brock, I; Brook, NH; Brugnera, R; Bruni, A; Bussey, PJ; Caldwell, A; Capua, M; Catterall, CD; Chwastowski, J; Ciborowski, J; Ciesielski, R; Cooper-Sarkar, AM; Corradi, M; Dementiev, RK; Devenish, RCE; Dusini, S; Foster, B; Gach, G; Gallo, E; Garfagnini, A; Geiser, A; Gizhko, A; Gladilin, LK; Golubkov, Yu A; Grzelak, G; Guzik, M; Hain, W; Hochman, D; Hori, R; Ibrahim, ZA; Iga, Y; Ishitsuka, M; Januschek, F; Jomhari, NZ; Kadenko, I; Kananov, S; Karshon, U; Kaur, P; Kisielewska, D; Klanner, R; Klein, U; Korzhavina, IA; Kotański, A; Kötz, U; Kovalchuk, N; Kowalski, H; Krupa, B; Kuprash, O; Kuze, M; Levchenko, BB; Levy, A; Limentani, S; Lisovyi, M; Lobodzinska, E; Löhr, B; Lohrmann, E; Longhin, A; Lontkovskyi, D; Lukina, OYu; Makarenko, I; Malka, J; Mohamad Idris, F; Mohammad Nasir, N; Myronenko, V; Nagano, K; Nobe, T; Nowak, RJ; Onishchuk, Yu; Paul, E; Perlański, W; Pokrovskiy, NS; Przybycien, M; Roloff, P; Ruspa, M; Saxon, DH; Schioppa, M; Schneekloth, U; Schörner-Sadenius, T; Shcheglova, LM; Shevchenko, R; Shkola, O; Shyrma, Yu; Singh, I; Skillicorn, IO; Słomiński, W; Solano, A; Stanco, L; Stefaniuk, N; Stern, A; Stopa, P; Sztuk-Dambietz, J; Tassi, E; Tokushuku, K; Tomaszewska, J; Tsurugai, T; Turcato, M; Turkot, O; Tymieniecka, T; Verbytskyi, A; Wan Abdullah, WAT; Wichmann, K; Wing, M; Yamada, S; Yamazaki, Y; Zakharchuk, N; Żarnecki, AF; Zawiejski, L; Zenaiev, O; Zhautykov, BO; Zotkin, DS; Bhadra, S; Gwenlan, C; Hlushchenko, O; Polini, A; Mastroberardino, A


    A simultaneous fit of parton distribution functions (PDFs) and electroweak parameters to HERA data on deep inelastic scattering is presented. The input data are the neutral current and charged current inclusive cross sections which were previously used in the QCD analysis leading to the HERAPDF2.0 PDFs. In addition, the polarisation of the electron beam was taken into account for the ZEUS data recorded between 2004 and 2007. Results on the vector and axial-vector couplings of the Z boson to u- and d-type quarks, on the value of the electroweak mixing angle and the mass of the W boson are presented. The values obtained for the electroweak parameters are in agreement with Standard Model predictions.

  7. The limit shape problem for ensembles of Young diagrams

    CERN Document Server

    Hora, Akihito


    This book treats ensembles of Young diagrams originating from group-theoretical contexts and investigates what statistical properties are observed there in a large-scale limit. The focus is mainly on analyzing the interesting phenomenon that specific curves appear in the appropriate scaling limit for the profiles of Young diagrams. This problem is regarded as an important origin of recent vital studies on harmonic analysis of huge symmetry structures. As mathematics, an asymptotic theory of representations is developed of the symmetric groups of degree n as n goes to infinity. The framework of rigorous limit theorems (especially the law of large numbers) in probability theory is employed as well as combinatorial analysis of group characters of symmetric groups and applications of Voiculescu's free probability. The central destination here is a clear description of the asymptotic behavior of rescaled profiles of Young diagrams in the Plancherel ensemble from both static and dynamic points of view.

  8. Mass Conservation and Positivity Preservation with Ensemble-type Kalman Filter Algorithms (United States)

    Janjic, Tijana; McLaughlin, Dennis B.; Cohn, Stephen E.; Verlaan, Martin


    Maintaining conservative physical laws numerically has long been recognized as being important in the development of numerical weather prediction (NWP) models. In the broader context of data assimilation, concerted efforts to maintain conservation laws numerically and to understand the significance of doing so have begun only recently. In order to enforce physically based conservation laws of total mass and positivity in the ensemble Kalman filter, we incorporate constraints to ensure that the filter ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. We show that the analysis steps of ensemble transform Kalman filter (ETKF) algorithm and ensemble Kalman filter algorithm (EnKF) can conserve the mass integral, but do not preserve positivity. Further, if localization is applied or if negative values are simply set to zero, then the total mass is not conserved either. In order to ensure mass conservation, a projection matrix that corrects for localization effects is constructed. In order to maintain both mass conservation and positivity preservation through the analysis step, we construct a data assimilation algorithms based on quadratic programming and ensemble Kalman filtering. Mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate constraints. Some simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. The results show clear improvements in both analyses and forecasts, particularly in the presence of localized features. Behavior of the algorithm is also tested in presence of model error.

  9. Derivation of Mayer Series from Canonical Ensemble

    International Nuclear Information System (INIS)

    Wang Xian-Zhi


    Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula. (paper)

  10. Derivation of Mayer Series from Canonical Ensemble (United States)

    Wang, Xian-Zhi


    Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula.

  11. Evaluation of stability of k-means cluster ensembles with respect to random initialization. (United States)

    Kuncheva, Ludmila I; Vetrov, Dmitry P


    Many clustering algorithms, including cluster ensembles, rely on a random component. Stability of the results across different runs is considered to be an asset of the algorithm. The cluster ensembles considered here are based on k-means clusterers. Each clusterer is assigned a random target number of clusters, k and is started from a random initialization. Here, we use 10 artificial and 10 real data sets to study ensemble stability with respect to random k, and random initialization. The data sets were chosen to have a small number of clusters (two to seven) and a moderate number of data points (up to a few hundred). Pairwise stability is defined as the adjusted Rand index between pairs of clusterers in the ensemble, averaged across all pairs. Nonpairwise stability is defined as the entropy of the consensus matrix of the ensemble. An experimental comparison with the stability of the standard k-means algorithm was carried out for k from 2 to 20. The results revealed that ensembles are generally more stable, markedly so for larger k. To establish whether stability can serve as a cluster validity index, we first looked at the relationship between stability and accuracy with respect to the number of clusters, k. We found that such a relationship strongly depends on the data set, varying from almost perfect positive correlation (0.97, for the glass data) to almost perfect negative correlation (-0.93, for the crabs data). We propose a new combined stability index to be the sum of the pairwise individual and ensemble stabilities. This index was found to correlate better with the ensemble accuracy. Following the hypothesis that a point of stability of a clustering algorithm corresponds to a structure found in the data, we used the stability measures to pick the number of clusters. The combined stability index gave best results.

  12. A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Francesco Cavrini


    Full Text Available We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control.

  13. μ-PIV measurements of the ensemble flow fields surrounding a migrating semi-infinite bubble. (United States)

    Yamaguchi, Eiichiro; Smith, Bradford J; Gaver, Donald P


    Microscale particle image velocimetry (μ-PIV) measurements of ensemble flow fields surrounding a steadily-migrating semi-infinite bubble through the novel adaptation of a computer controlled linear motor flow control system. The system was programmed to generate a square wave velocity input in order to produce accurate constant bubble propagation repeatedly and effectively through a fused glass capillary tube. We present a novel technique for re-positioning of the coordinate axis to the bubble tip frame of reference in each instantaneous field through the analysis of the sudden change of standard deviation of centerline velocity profiles across the bubble interface. Ensemble averages were then computed in this bubble tip frame of reference. Combined fluid systems of water/air, glycerol/air, and glycerol/Si-oil were used to investigate flows comparable to computational simulations described in Smith and Gaver (2008) and to past experimental observations of interfacial shape. Fluorescent particle images were also analyzed to measure the residual film thickness trailing behind the bubble. The flow fields and film thickness agree very well with the computational simulations as well as existing experimental and analytical results. Particle accumulation and migration associated with the flow patterns near the bubble tip after long experimental durations are discussed as potential sources of error in the experimental method.

  14. μ-PIV measurements of the ensemble flow fields surrounding a migrating semi-infinite bubble (United States)

    Yamaguchi, Eiichiro; Smith, Bradford J.; Gaver, Donald P.


    Microscale particle image velocimetry (μ-PIV) measurements of ensemble flow fields surrounding a steadily-migrating semi-infinite bubble through the novel adaptation of a computer controlled linear motor flow control system. The system was programmed to generate a square wave velocity input in order to produce accurate constant bubble propagation repeatedly and effectively through a fused glass capillary tube. We present a novel technique for re-positioning of the coordinate axis to the bubble tip frame of reference in each instantaneous field through the analysis of the sudden change of standard deviation of centerline velocity profiles across the bubble interface. Ensemble averages were then computed in this bubble tip frame of reference. Combined fluid systems of water/air, glycerol/air, and glycerol/Si-oil were used to investigate flows comparable to computational simulations described in Smith and Gaver (2008) and to past experimental observations of interfacial shape. Fluorescent particle images were also analyzed to measure the residual film thickness trailing behind the bubble. The flow fields and film thickness agree very well with the computational simulations as well as existing experimental and analytical results. Particle accumulation and migration associated with the flow patterns near the bubble tip after long experimental durations are discussed as potential sources of error in the experimental method. PMID:23049158

  15. Generating precipitation ensembles for flood alert and risk management (United States)

    Caseri, Angelica; Javelle, Pierre; Ramos, Maria-Helena; Leblois, Etienne


    Floods represent one of the major natural disasters that are often responsible for fatalities and economic losses. Flood warning systems are needed to anticipate the arrival of severe events and mitigate their impacts. Flood alerts are particularly important for risk management and response in the nowcasting of flash floods. In this case, precipitation fields observed in real time play a crucial role and observational uncertainties must be taken into account. In this study, we investigate the potential of a framework which combines a geostatistical conditional simulation method that considers information from precipitation radar and rain gauges, and a distributed rainfall-runoff model to generate an ensemble of precipitation fields and produce probabilistic flood alert maps. We adapted the simulation method proposed by Leblois and Creutin (2013), based on the Turning Band Method (TBM) and a conditional simulation approach, to consider the temporal and spatial characteristics of radar data and rain gauge measurements altogether and generate precipitation ensembles. The AIGA system developed by Irstea and Météo-France for predicting flash floods in the French Mediterranean region (Javelle et al., 2014) was used to transform the generated precipitation ensembles into ensembles of discharge at the outlet of the studied catchments. Finally, discharge ensembles were translated into maps providing information on the probability of exceeding a given flood threshold. A total of 19 events that occurred between 2009 and 2013 in the Var region (southeastern France), a region prone to flash floods, was used to illustrate the approach. Results show that the proposed method is able to simulate an ensemble of realistic precipitation fields and capture peak flows of flash floods. This was shown to be particularly useful at ungauged catchments, where uncertainties on the evaluation of flood peaks are high. The results obtained also show that the approach developed can be used to

  16. An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions

    Directory of Open Access Journals (Sweden)

    M. He


    Full Text Available The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions, the stand-alone ISURF and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile. The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic forecasting method in terms of providing improved single-valued (e.g., ensemble mean streamflow predictions as well as meaningful ensemble predictions.

  17. Calibration of decadal ensemble predictions (United States)

    Pasternack, Alexander; Rust, Henning W.; Bhend, Jonas; Liniger, Mark; Grieger, Jens; Müller, Wolfgang; Ulbrich, Uwe


    Decadal climate predictions are of great socio-economic interest due to the corresponding planning horizons of several political and economic decisions. Due to uncertainties of weather and climate, forecasts (e.g. due to initial condition uncertainty), they are issued in a probabilistic way. One issue frequently observed for probabilistic forecasts is that they tend to be not reliable, i.e. the forecasted probabilities are not consistent with the relative frequency of the associated observed events. Thus, these kind of forecasts need to be re-calibrated. While re-calibration methods for seasonal time scales are available and frequently applied, these methods still have to be adapted for decadal time scales and its characteristic problems like climate trend and lead time dependent bias. Regarding this, we propose a method to re-calibrate decadal ensemble predictions that takes the above mentioned characteristics into account. Finally, this method will be applied and validated to decadal forecasts from the MiKlip system (Germany's initiative for decadal prediction).

  18. Application of an Ensemble Kalman filter to a 1-D coupled hydrodynamic-ecosystem model of the Ligurian Sea

    NARCIS (Netherlands)

    Lenartz, F.; Raick, C.; Soetaert, K.E.R.; Grégoire, M.


    The Ensemble Kalman filter (EnKF) has been applied to a 1-D complex ecosystem model coupled with a hydrodynamic model of the Ligurian Sea. In order to improve the performance of the EnKF, an ensemble subsampling strategy has been used to better represent the covariance matrices and a pre-analysis

  19. Adaptive Ensemble with Human Memorizing Characteristics for Data Stream Mining

    Directory of Open Access Journals (Sweden)

    Yanhuang Jiang


    Full Text Available Combining several classifiers on sequential chunks of training instances is a popular strategy for data stream mining with concept drifts. This paper introduces human recalling and forgetting mechanisms into a data stream mining system and proposes a Memorizing Based Data Stream Mining (MDSM model. In this model, each component classifier is regarded as a piece of knowledge that a human obtains through learning some materials and has a memory retention value reflecting its usefulness in the history. The classifiers with high memory retention values are reserved in a “knowledge repository.” When a new data chunk comes, most useful classifiers will be selected (recalled from the repository and compose the current target ensemble. Based on MDSM, we put forward a new algorithm, MAE (Memorizing Based Adaptive Ensemble, which uses Ebbinghaus forgetting curve as the forgetting mechanism and adopts ensemble pruning as the recalling mechanism. Compared with four popular data stream mining approaches on the datasets with different concept drifts, the experimental results show that MAE achieves high and stable predicting accuracy, especially for the applications with recurring or complex concept drifts. The results also prove the effectiveness of MDSM model.

  20. GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering

    Directory of Open Access Journals (Sweden)

    Yanhua Wang


    Full Text Available Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.

  1. GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering. (United States)

    Wang, Yanhua; Liu, Xiyu; Xiang, Laisheng


    Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.

  2. HIPPI: highly accurate protein family classification with ensembles of HMMs

    Directory of Open Access Journals (Sweden)

    Nam-phuong Nguyen


    Full Text Available Abstract Background Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. Results We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification. HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy. Conclusion HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at .

  3. Hydrological ensemble predictions for reservoir inflow management (United States)

    Zalachori, Ioanna; Ramos, Maria-Helena; Garçon, Rémy; Gailhard, Joel


    Hydrologic forecasting is a topic of special importance for a variety of users with different purposes. It concerns operational hydrologists interested in forecasting hazardous events (eg., floods and droughts) for early warning and prevention, as well as planners and managers searching to optimize the management of water resources systems at different space-time scales. The general aim of this study is to investigate the benefits of using hydrological ensemble predictions for reservoir inflow management. Ensemble weather forecasts are used as input to a hydrologic forecasting model and daily ensemble streamflow forecasts are generated up to a lead time of 7 days. Forecasts are then integrated into a heuristic decision model for reservoir management procedures. Performance is evaluated in terms of potential gain in energy production. The sensitivity of the results to various reservoir characteristics and future streamflow scenarios is assessed. A set of 11 catchments in France is used to illustrate the added value of ensemble streamflow forecasts for reservoir management.

  4. Ensemble Machine Learning Methods and Applications

    CERN Document Server

    Ma, Yunqian


    It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

  5. Sensitivity of regional ensemble data assimilation spread to perturbations of lateral boundary conditions

    Directory of Open Access Journals (Sweden)

    Rachida El Ouaraini


    Full Text Available The implementation of a regional ensemble data assimilation and forecasting system requires the specification of appropriate perturbations of lateral boundary conditions (LBCs, in order to simulate associated errors. The sensitivity of analysis and 6-h forecast ensemble spread to these perturbations is studied here formally and experimentally by comparing three different LBC configurations for the ensemble data assimilation system of the ALADIN-France limited-area model (LAM. While perturbed initial LBCs are provided by the perturbed LAM analyses in each ensemble, the three ensemble configurations differ with respect to LBCs used at 3- and 6-h forecast ranges, which respectively correspond to: (1 perturbed LBCs provided by the operational global ensemble data assimilation system (GLBC, which is considered as a reference configuration; (2 unperturbed LBCs (ULBC obtained from the global deterministic model; (3 perturbed LBCs obtained by adding random draws of an error covariance model (PLBC to the global deterministic system. A formal analysis of error and perturbation equations is first carried out, in order to provide an insight of the relative effects of observation perturbations and of LBC perturbations at different ranges, in the various ensemble configurations. Horizontal variations of time-averaged ensemble spread are then examined for 6-h forecasts. Despite the use of perturbed initial LBCs, the regional ensemble ULBC is underdispersive not only near the lateral boundaries, but also in approximately one-third of the inner area, due to advection during the data assimilation cycle. This artefact is avoided in PLBC through the additional use of non-zero LBC perturbations at 3- and 6-h ranges, and the sensitivity to the amplitude scaling of the covariance model is illustrated for this configuration. Some aspects of the temporal variation of ensemble spread and associated sensitivities to LBC perturbations are also studied. These results

  6. A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting

    KAUST Repository

    Raboudi, Naila


    The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme\\'s behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated

  7. Ensemble-based Experimental Atmospheric Reanalysis using a Global Coupled Atmosphere-Ocean GCM (United States)

    Komori, N.; Enomoto, T.; Miyoshi, T.; Yamazaki, A.; Kuwano-Yoshida, A.; Taguchi, B.


    To enhance the capability of the local ensemble transform Kalman filter (LETKF) with the Atmospheric general circulation model (GCM) for the Earth Simulator (AFES), a new system has been developed by replacing AFES with the Coupled atmosphere-ocean GCM for the Earth Simulator (CFES). An initial test of the prototype of the CFES-LETKF system has been completed successfully, assimilating atmospheric observational data (NCEP PREPBUFR archived at UCAR) every 6 hours to update the atmospheric variables, whereas the oceanic variables are kept unchanged throughout the assimilation procedure. An experimental retrospective analysis-forecast cycle with the coupled system (CLERA-A) starts on August 1, 2008, and the atmospheric initial conditions (63 members) are taken from the second generation of AFES-LETKF experimental ensemble reanalysis (ALERA2). The ALERA2 analyses are also used as forcing of stand-alone 63-member ensemble simulations with the Ocean GCM for the Earth Simulator (EnOFES), from which the oceanic initial conditions for the CLERA-A are taken. The ensemble spread of SST is larger in CLERA-A than in EnOFES, suggesting positive feedback between the ocean and the atmosphere. Although SST in CLERA-A suffers from the common biases among many coupled GCMs, the ensemble spreads of air temperature and specific humidity in the lower troposphere are larger in CLERA-A than in ALERA2. Thus replacement of AFES with CFES successfully contributes to mitigate an underestimation of the ensemble spread near the surface resulting from the single boundary condition for all ensemble members and the lack of atmosphere-ocean interaction. In addition, the basin-scale structure of surface atmospheric variables over the tropical Pacific is well reconstructed from the ensemble correlation in CLERA-A but not ALERA2. This suggests that use of a coupled GCM rather than an atmospheric GCM could be important even for atmospheric reanalysis with an ensemble-based data assimilation system.

  8. Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR Soil Moisture Retrieval Errors

    Directory of Open Access Journals (Sweden)

    Ju Hyoung Lee


    Full Text Available Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE, while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

  9. A multi-stage intelligent approach based on an ensemble of two-way interaction model for forecasting the global horizontal radiation of India

    International Nuclear Information System (INIS)

    Jiang, He; Dong, Yao; Xiao, Ling


    Highlights: • Ensemble learning system is proposed to forecast the global solar radiation. • LASSO is utilized as feature selection method for subset model. • GSO is used to select the weight vector aggregating the response of subset model. • A simple and efficient algorithm is designed based on thresholding function. • Theoretical analysis focusing on error rate is provided. - Abstract: Forecasting of effective solar irradiation has developed a huge interest in recent decades, mainly due to its various applications in grid connect photovoltaic installations. This paper develops and investigates an ensemble learning based multistage intelligent approach to forecast 5 days global horizontal radiation at four given locations of India. The two-way interaction model is considered with purpose of detecting the associated correlation between the features. The main structure of the novel method is the ensemble learning, which is based on Divide and Conquer principle, is applied to enhance the forecasting accuracy and model stability. An efficient feature selection method LASSO is performed in the input space with the regularization parameter selected by Cross-Validation. A weight vector which best represents the importance of each individual model in ensemble system is provided by glowworm swarm optimization. The combination of feature selection and parameter selection are helpful in creating the diversity of the ensemble learning. In order to illustrate the validity of the proposed method, the datasets at four different locations of the India are split into training and test datasets. The results of the real data experiments demonstrate the efficiency and efficacy of the proposed method comparing with other competitors.

  10. Ozone ensemble forecast with machine learning algorithms


    Mallet , Vivien; Stoltz , Gilles; Mauricette , Boris


    International audience; We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrati...

  11. Orbital magnetism in ensembles of ballistic billiards

    International Nuclear Information System (INIS)

    Ullmo, D.; Richter, K.; Jalabert, R.A.


    The magnetic response of ensembles of small two-dimensional structures at finite temperatures is calculated. Using semiclassical methods and numerical calculation it is demonstrated that only short classical trajectories are relevant. The magnetic susceptibility is enhanced in regular systems, where these trajectories appear in families. For ensembles of squares large paramagnetic susceptibility is obtained, in good agreement with recent measurements in the ballistic regime. (authors). 20 refs., 2 figs

  12. Ensembles of Classifiers based on Dimensionality Reduction


    Schclar, Alon; Rokach, Lior; Amit, Amir


    We present a novel approach for the construction of ensemble classifiers based on dimensionality reduction. Dimensionality reduction methods represent datasets using a small number of attributes while preserving the information conveyed by the original dataset. The ensemble members are trained based on dimension-reduced versions of the training set. These versions are obtained by applying dimensionality reduction to the original training set using different values of the input parameters. Thi...

  13. Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China (United States)

    Liu, Li; Xu, Yue-Ping


    Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

  14. Ensemble forecasts of road surface temperatures (United States)

    Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr; Pešice, Petr; Škuthan, Miroslav


    This paper describes a new ensemble technique for road surface temperature (RST) forecasting using an energy balance and heat conduction model. Compared to currently used deterministic forecasts, the proposed technique allows the estimation of forecast uncertainty and probabilistic forecasts. The ensemble technique is applied to the METRo-CZ model and stems from error covariance analyses of the forecasted air temperature and humidity 2 m above the ground, wind speed at 10 m and total cloud cover N in octas by the numerical weather prediction (NWP) model. N is used to estimate the shortwave and longwave radiation fluxes. These variables are used to calculate the boundary conditions in the METRo-CZ model. We found that the variable N is crucial for generating the ensembles. Nevertheless, the ensemble spread is too small and underestimates the uncertainty in the RST forecast. One of the reasons is not considering errors in the rain and snow forecast by the NWP model when generating ensembles. Technical issues, such as incorrect sky view factors and the current state of road surface conditions also contribute to errors. Although the ensemble technique underestimates the uncertainty in the RST forecasts, it provides additional information to road authorities who provide winter road maintenance.

  15. [CIPA (Comprehensive Individualized Process Analysis)--a method for combining quantitative and qualitative individual case analysis]. (United States)

    Regli, D; Grawe, K; Gassmann, D; Dick, A


    The main question of psychotherapy research nowadays is how psychotherapy works. Hence, interest focuses mainly on the process of psychotherapy. General change mechanisms as well as the therapeutic interaction are in the center of research interest. On the basis of process outcome findings (Orlinsky, Grawe, Parks 1994) and the schema theory of Grawe (1987) we developed a research instrument allowing the analysis of therapies from a theoretically and quantitative approaches. The research instrument (CIPA-Comprehensive Individualized Process Analysis) consist of three parts: The scales of the first part assess the general working mechanisms and the therapeutic relationship. The second part allows a rating of the patient's interactions inside and outside the therapy. In the third part the individual schemata are rated. The instrument and the research strategy are being illustrated by means of a selected therapy recorded completely on video tape. The results are interpreted on the basis of the individual schema structure as well as the therapy outcome. The possibility offered by the new instrument to combine quantitative and qualitative research strategies is discussed.

  16. Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response

    Directory of Open Access Journals (Sweden)

    Anna Maria Stellacci


    Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined

  17. Current path in light emitting diodes based on nanowire ensembles

    International Nuclear Information System (INIS)

    Limbach, F; Hauswald, C; Lähnemann, J; Wölz, M; Brandt, O; Trampert, A; Hanke, M; Jahn, U; Calarco, R; Geelhaar, L; Riechert, H


    Light emitting diodes (LEDs) have been fabricated using ensembles of free-standing (In, Ga)N/GaN nanowires (NWs) grown on Si substrates in the self-induced growth mode by molecular beam epitaxy. Electron-beam-induced current analysis, cathodoluminescence as well as biased μ-photoluminescence spectroscopy, transmission electron microscopy, and electrical measurements indicate that the electroluminescence of such LEDs is governed by the differences in the individual current densities of the single-NW LEDs operated in parallel, i.e. by the inhomogeneity of the current path in the ensemble LED. In addition, the optoelectronic characterization leads to the conclusion that these NWs exhibit N-polarity and that the (In, Ga)N quantum well states in the NWs are subject to a non-vanishing quantum confined Stark effect. (paper)

  18. The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation (United States)

    Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie


    The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

  19. Stability evaluation of short-circuiting gas metal arc welding based on ensemble empirical mode decomposition (United States)

    Huang, Yong; Wang, Kehong; Zhou, Zhilan; Zhou, Xiaoxiao; Fang, Jimi


    The arc of gas metal arc welding (GMAW) contains abundant information about its stability and droplet transition, which can be effectively characterized by extracting the arc electrical signals. In this study, ensemble empirical mode decomposition (EEMD) was used to evaluate the stability of electrical current signals. The welding electrical signals were first decomposed by EEMD, and then transformed to a Hilbert-Huang spectrum and a marginal spectrum. The marginal spectrum is an approximate distribution of amplitude with frequency of signals, and can be described by a marginal index. Analysis of various welding process parameters showed that the marginal index of current signals increased when the welding process was more stable, and vice versa. Thus EEMD combined with the marginal index can effectively uncover the stability and droplet transition of GMAW.

  20. Using ensemble models to classify the sentiment expressed in suicide notes. (United States)

    McCart, James A; Finch, Dezon K; Jarman, Jay; Hickling, Edward; Lind, Jason D; Richardson, Matthew R; Berndt, Donald J; Luther, Stephen L


    In 2007, suicide was the tenth leading cause of death in the U.S. Given the significance of this problem, suicide was the focus of the 2011 Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing (NLP) shared task competition (track two). Specifically, the challenge concentrated on sentiment analysis, predicting the presence or absence of 15 emotions (labels) simultaneously in a collection of suicide notes spanning over 70 years. Our team explored multiple approaches combining regular expression-based rules, statistical text mining (STM), and an approach that applies weights to text while accounting for multiple labels. Our best submission used an ensemble of both rules and STM models to achieve a micro-averaged F(1) score of 0.5023, slightly above the mean from the 26 teams that competed (0.4875).

  1. Ensemble of ground subsidence hazard maps using fuzzy logic (United States)

    Park, Inhye; Lee, Jiyeong; Saro, Lee


    Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

  2. Rotationally invariant family of Levy-like random matrix ensembles

    International Nuclear Information System (INIS)

    Choi, Jinmyung; Muttalib, K A


    We introduce a family of rotationally invariant random matrix ensembles characterized by a parameter λ. While λ = 1 corresponds to well-known critical ensembles, we show that λ ≠ 1 describes 'Levy-like' ensembles, characterized by power-law eigenvalue densities. For λ > 1 the density is bounded, as in Gaussian ensembles, but λ < 1 describes ensembles characterized by densities with long tails. In particular, the model allows us to evaluate, in terms of a novel family of orthogonal polynomials, the eigenvalue correlations for Levy-like ensembles. These correlations differ qualitatively from those in either the Gaussian or the critical ensembles. (fast track communication)

  3. Ensemble perception of emotions in autistic and typical children and adolescents. (United States)

    Karaminis, Themelis; Neil, Louise; Manning, Catherine; Turi, Marco; Fiorentini, Chiara; Burr, David; Pellicano, Elizabeth


    Ensemble perception, the ability to assess automatically the summary of large amounts of information presented in visual scenes, is available early in typical development. This ability might be compromised in autistic children, who are thought to present limitations in maintaining summary statistics representations for the recent history of sensory input. Here we examined ensemble perception of facial emotional expressions in 35 autistic children, 30 age- and ability-matched typical children and 25 typical adults. Participants received three tasks: a) an 'ensemble' emotion discrimination task; b) a baseline (single-face) emotion discrimination task; and c) a facial expression identification task. Children performed worse than adults on all three tasks. Unexpectedly, autistic and typical children were, on average, indistinguishable in their precision and accuracy on all three tasks. Computational modelling suggested that, on average, autistic and typical children used ensemble-encoding strategies to a similar extent; but ensemble perception was related to non-verbal reasoning abilities in autistic but not in typical children. Eye-movement data also showed no group differences in the way children attended to the stimuli. Our combined findings suggest that the abilities of autistic and typical children for ensemble perception of emotions are comparable on average. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Bayesian refinement of protein structures and ensembles against SAXS data using molecular dynamics. (United States)

    Shevchuk, Roman; Hub, Jochen S


    Small-angle X-ray scattering is an increasingly popular technique used to detect protein structures and ensembles in solution. However, the refinement of structures and ensembles against SAXS data is often ambiguous due to the low information content of SAXS data, unknown systematic errors, and unknown scattering contributions from the solvent. We offer a solution to such problems by combining Bayesian inference with all-atom molecular dynamics simulations and explicit-solvent SAXS calculations. The Bayesian formulation correctly weights the SAXS data versus prior physical knowledge, it quantifies the precision or ambiguity of fitted structures and ensembles, and it accounts for unknown systematic errors due to poor buffer matching. The method further provides a probabilistic criterion for identifying the number of states required to explain the SAXS data. The method is validated by refining ensembles of a periplasmic binding protein against calculated SAXS curves. Subsequently, we derive the solution ensembles of the eukaryotic chaperone heat shock protein 90 (Hsp90) against experimental SAXS data. We find that the SAXS data of the apo state of Hsp90 is compatible with a single wide-open conformation, whereas the SAXS data of Hsp90 bound to ATP or to an ATP-analogue strongly suggest heterogenous ensembles of a closed and a wide-open state.

  5. Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches. (United States)

    Kilic, Niyazi; Hosgormez, Erkan


    Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 (five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations.

  6. Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation

    Directory of Open Access Journals (Sweden)

    Mahmoud O. Elish


    Full Text Available Accurate estimation of software development effort is essential for effective management and control of software development projects. Many software effort estimation methods have been proposed in the literature including computational intelligence models. However, none of the existing models proved to be suitable under all circumstances; that is, their performance varies from one dataset to another. The goal of an ensemble model is to manage each of its individual models’ strengths and weaknesses automatically, leading to the best possible decision being taken overall. In this paper, we have developed different homogeneous and heterogeneous ensembles of optimized hybrid computational intelligence models for software development effort estimation. Different linear and nonlinear combiners have been used to combine the base hybrid learners. We have conducted an empirical study to evaluate and compare the performance of these ensembles using five popular datasets. The results confirm that individual models are not reliable as their performance is inconsistent and unstable across different datasets. Although none of the ensemble models was consistently the best, many of them were frequently among the best models for each dataset. The homogeneous ensemble of support vector regression (SVR, with the nonlinear combiner adaptive neurofuzzy inference systems-subtractive clustering (ANFIS-SC, was the best model when considering the average rank of each model across the five datasets.

  7. Multi-dimensional project evaluation: Combining cost-benefit analysis and multi-criteria analysis with the COSIMA software system

    DEFF Research Database (Denmark)

    This paper proposes a methodology that integrates quantitative and qualitative assessment. The methodology proposed combines conventional cost-benefit analysis (CBA) with multi-criteria analysis (MCA). The CBA methodology, based on welfare theory, assures that the project with the highest welfare...... down a problem into its constituent parts in order to better understand the problem and consequently arrive at a decision. However, while MCA opens up for the possibility to include non-market impacts, it does not provide the decision makers with guidance combining the CBA with MCA. In the paper...... different methods for combining cost-benefit analysis and multi-criteria analysis are examined and compared and a software system is presented. The software system gives the decision makers some possibilities regarding preference analysis, sensitivity and risk analysis. The aim of the software...

  8. Comparison of extended medium-range forecast skill between KMA ensemble, ocean coupled ensemble, and GloSea5 (United States)

    Park, Sangwook; Kim, Dong-Joon; Lee, Seung-Woo; Lee, Kie-Woung; Kim, Jongkhun; Song, Eun-Ji; Seo, Kyong-Hwan


    This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.

  9. Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters (United States)

    Ierodiaconou, Daniel; Schimel, Alexandre C. G.; Kennedy, David; Monk, Jacquomo; Gaylard, Grace; Young, Mary; Diesing, Markus; Rattray, Alex


    Habitat mapping data are increasingly being recognised for their importance in underpinning marine spatial planning. The ability to collect ultra-high resolution (cm) multibeam echosounder (MBES) data in shallow waters has facilitated understanding of the fine-scale distribution of benthic habitats in these areas that are often prone to human disturbance. Developing quantitative and objective approaches to integrate MBES data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for habitat mapping products. Whilst supervised classification approaches are becoming more common, users are often faced with a decision whether to implement a pixel based (PB) or an object based (OB) image analysis approach, with often limited understanding of the potential influence of that decision on final map products and relative importance of data inputs to patterns observed. In this study, we apply an ensemble learning approach capable of integrating PB and OB Image Analysis from ultra-high resolution MBES bathymetry and backscatter data for mapping benthic habitats in Refuge Cove, a temperate coastal embayment in south-east Australia. We demonstrate the relative importance of PB and OB seafloor derivatives for the five broad benthic habitats that dominate the site. We found that OB and PB approaches performed well with differences in classification accuracy but not discernible statistically. However, a model incorporating elements of both approaches proved to be significantly more accurate than OB or PB methods alone and demonstrate the benefits of using MBES bathymetry and backscatter combined for class discrimination.

  10. Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models.

    Directory of Open Access Journals (Sweden)

    Nikola Simidjievski

    Full Text Available Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting, significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.

  11. Feature-based ordering algorithm for data presentation of fuzzy ARTMAP ensembles. (United States)

    Oong, Tatt Hee; Isa, Nor Ashidi Mat


    This brief presents a new ordering algorithm for data presentation of fuzzy ARTMAP (FAM) ensembles. The proposed ordering algorithm manipulates the presentation order of the training data for each member of a FAM ensemble such that the categories created in each ensemble member are biased toward the vector of the chosen input feature. Diversity is created by varying the training presentation order based on the ascending order of the values from the most uncorrelated input features. Analysis shows that the categories created in two FAMs are compulsively diverse when the chosen input features used to determine the presentation order of the training data are uncorrelated. The proposed ordering algorithm was tested on 10 classification benchmark problems from the University of California, Irvine, machine learning repository and a cervical cancer problem as a case study. The experimental results show that the proposed method can produce a diverse, yet well generalized, FAM ensemble.

  12. Orchestrating Distributed Resource Ensembles for Petascale Science

    Energy Technology Data Exchange (ETDEWEB)

    Baldin, Ilya; Mandal, Anirban; Ruth, Paul; Yufeng, Xin


    Distributed, data-intensive computational science applications of interest to DOE scientific com- munities move large amounts of data for experiment data management, distributed analysis steps, remote visualization, and accessing scientific instruments. These applications need to orchestrate ensembles of resources from multiple resource pools and interconnect them with high-capacity multi- layered networks across multiple domains. It is highly desirable that mechanisms are designed that provide this type of resource provisioning capability to a broad class of applications. It is also important to have coherent monitoring capabilities for such complex distributed environments. In this project, we addressed these problems by designing an abstract API, enabled by novel semantic resource descriptions, for provisioning complex and heterogeneous resources from multiple providers using their native provisioning mechanisms and control planes: computational, storage, and multi-layered high-speed network domains. We used an extensible resource representation based on semantic web technologies to afford maximum flexibility to applications in specifying their needs. We evaluated the effectiveness of provisioning using representative data-intensive ap- plications. We also developed mechanisms for providing feedback about resource performance to the application, to enable closed-loop feedback control and dynamic adjustments to resource allo- cations (elasticity). This was enabled through development of a novel persistent query framework that consumes disparate sources of monitoring data, including perfSONAR, and provides scalable distribution of asynchronous notifications.

  13. Statistic complexity: combining kolmogorov complexity with an ensemble approach. (United States)

    Emmert-Streib, Frank


    The evaluation of the complexity of an observed object is an old but outstanding problem. In this paper we are tying on this problem introducing a measure called statistic complexity. This complexity measure is different to all other measures in the following senses. First, it is a bivariate measure that compares two objects, corresponding to pattern generating processes, on the basis of the normalized compression distance with each other. Second, it provides the quantification of an error that could have been encountered by comparing samples of finite size from the underlying processes. Hence, the statistic complexity provides a statistical quantification of the statement ' is similarly complex as Y'. The presented approach, ultimately, transforms the classic problem of assessing the complexity of an object into the realm of statistics. This may open a wider applicability of this complexity measure to diverse application areas.

  14. Statistic complexity: combining kolmogorov complexity with an ensemble approach.

    Directory of Open Access Journals (Sweden)

    Frank Emmert-Streib

    Full Text Available BACKGROUND: The evaluation of the complexity of an observed object is an old but outstanding problem. In this paper we are tying on this problem introducing a measure called statistic complexity. METHODOLOGY/PRINCIPAL FINDINGS: This complexity measure is different to all other measures in the following senses. First, it is a bivariate measure that compares two objects, corresponding to pattern generating processes, on the basis of the normalized compression distance with each other. Second, it provides the quantification of an error that could have been encountered by comparing samples of finite size from the underlying processes. Hence, the statistic complexity provides a statistical quantification of the statement ' is similarly complex as Y'. CONCLUSIONS: The presented approach, ultimately, transforms the classic problem of assessing the complexity of an object into the realm of statistics. This may open a wider applicability of this complexity measure to diverse application areas.

  15. Space Applications for Ensemble Detection and Analysis (United States)

    National Aeronautics and Space Administration — NASA makes extensive investments to circumvent the engineering challenges posed by naturally occurring random processes for which conventional statistics do not...

  16. A general model for preload calculation and stiffness analysis for combined angular contact ball bearings (United States)

    Zhang, Jinhua; Fang, Bin; Hong, Jun; Wan, Shaoke; Zhu, Yongsheng


    The combined angular contact ball bearings are widely used in automatic, aerospace and machine tools, but few researches on the combined angular contact ball bearings have been reported. It is shown that the preload and stiffness of combined bearings are mutual influenced rather than simply the superposition of multiple single bearing, therefore the characteristic calculation of combined bearings achieved by coupling the load and deformation analysis of a single bearing. In this paper, based on the Jones quasi-static model and stiffness analytical model, a new iterative algorithm and model are proposed for the calculation of combined bearings preload and stiffness, and the dynamic effects include centrifugal force and gyroscopic moment have to be considered. It is demonstrated that the new method has general applicability, the preload factors of combined bearings are calculated according to the different design preloads, and the static and dynamic stiffness for various arrangements of combined bearings are comparatively studied and analyzed, and the influences of the design preload magnitude, axial load and rotating speed are discussed in detail. Besides, the change rule of dynamic contact angles of combined bearings with respect to the rotating speed is also discussed. The results show that bearing arrangement modes, rotating speed and design preload magnitude have a significant influence on the preload and stiffness of combined bearings. The proposed formulation provides a useful tool in dynamic analysis of the complex bearing-rotor system.

  17. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    KAUST Repository

    Toye, Habib


    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  18. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing (United States)

    Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim


    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  19. Wang-Landau Reaction Ensemble Method: Simulation of Weak Polyelectrolytes and General Acid-Base Reactions. (United States)

    Landsgesell, Jonas; Holm, Christian; Smiatek, Jens


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

  20. Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model (United States)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long


    This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

  1. Multi-objective optimization for generating a weighted multi-model ensemble (United States)

    Lee, H.


    Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

  2. Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.


    approach, called d-ECC, is applied to wind forecasts from the high resolution ensemble system COSMO-DE-EPS run operationally at the German weather service. Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and in a product......Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts...... is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from...

  3. Combining Cloud Networks and Course Management Systems for Enhanced Analysis in Teaching Laboratories (United States)

    Abrams, Neal M.


    A cloud network system is combined with standard computing applications and a course management system to provide a robust method for sharing data among students. This system provides a unique method to improve data analysis by easily increasing the amount of sampled data available for analysis. The data can be shared within one course as well as…

  4. Thermo-economic analysis of combined power plants with changing economic parameters

    International Nuclear Information System (INIS)

    Bidini, G.; Desideri, U.; Facchini, B.


    A method of thermo-economic analysis for the choice of optimal thermodynamic parameters of steam bottoming cycles in combined cycle power plants is presented. By keeping the thermodynamic aspects separated from the economic aspects, this method allows designers to easily perform a sensitivity analysis of the change in the economic parameters

  5. Stochastic Approaches Within a High Resolution Rapid Refresh Ensemble (United States)

    Jankov, I.


    It is well known that global and regional numerical weather prediction (NWP) ensemble systems are under-dispersive, producing unreliable and overconfident ensemble forecasts. Typical approaches to alleviate this problem include the use of multiple dynamic cores, multiple physics suite configurations, or a combination of the two. While these approaches may produce desirable results, they have practical and theoretical deficiencies and are more difficult and costly to maintain. An active area of research that promotes a more unified and sustainable system is the use of stochastic physics. Stochastic approaches include Stochastic Parameter Perturbations (SPP), Stochastic Kinetic Energy Backscatter (SKEB), and Stochastic Perturbation of Physics Tendencies (SPPT). The focus of this study is to assess model performance within a convection-permitting ensemble at 3-km grid spacing across the Contiguous United States (CONUS) using a variety of stochastic approaches. A single physics suite configuration based on the operational High-Resolution Rapid Refresh (HRRR) model was utilized and ensemble members produced by employing stochastic methods. Parameter perturbations (using SPP) for select fields were employed in the Rapid Update Cycle (RUC) land surface model (LSM) and Mellor-Yamada-Nakanishi-Niino (MYNN) Planetary Boundary Layer (PBL) schemes. Within MYNN, SPP was applied to sub-grid cloud fraction, mixing length, roughness length, mass fluxes and Prandtl number. In the RUC LSM, SPP was applied to hydraulic conductivity and tested perturbing soil moisture at initial time. First iterative testing was conducted to assess the initial performance of several configuration settings (e.g. variety of spatial and temporal de-correlation lengths). Upon selection of the most promising candidate configurations using SPP, a 10-day time period was run and more robust statistics were gathered. SKEB and SPPT were included in additional retrospective tests to assess the impact of using

  6. Simulations in generalized ensembles through noninstantaneous switches (United States)

    Giovannelli, Edoardo; Cardini, Gianni; Chelli, Riccardo


    Generalized-ensemble simulations, such as replica exchange and serial generalized-ensemble methods, are powerful simulation tools to enhance sampling of free energy landscapes in systems with high energy barriers. In these methods, sampling is enhanced through instantaneous transitions of replicas, i.e., copies of the system, between different ensembles characterized by some control parameter associated with thermodynamical variables (e.g., temperature or pressure) or collective mechanical variables (e.g., interatomic distances or torsional angles). An interesting evolution of these methodologies has been proposed by replacing the conventional instantaneous (trial) switches of replicas with noninstantaneous switches, realized by varying the control parameter in a finite time and accepting the final replica configuration with a Metropolis-like criterion based on the Crooks nonequilibrium work (CNW) theorem. Here we revise these techniques focusing on their correlation with the CNW theorem in the framework of Markovian processes. An outcome of this report is the derivation of the acceptance probability for noninstantaneous switches in serial generalized-ensemble simulations, where we show that explicit knowledge of the time dependence of the weight factors entering such simulations is not necessary. A generalized relationship of the CNW theorem is also provided in terms of the underlying equilibrium probability distribution at a fixed control parameter. Illustrative calculations on a toy model are performed with serial generalized-ensemble simulations, especially focusing on the different behavior of instantaneous and noninstantaneous replica transition schemes.

  7. [Candidemia combined with bacterial bloodstream infection: analysis of clinical features and associated risk factors]. (United States)

    Liu, Yong; Sun, Yongchang; Zhuo, Jie; Liu, Xiaofang


    To investigate the clinical characteristics of and risk factors for candidemia combined with bacterial bloodstream infection(BSI) by retrospective analysis of cases. The clinical data of cases diagnosed as candidemia combined with BSI confirmed by blood culture were compared with those of cases with mono-candidemia in Beiing Tongren Hospital from January 2009 to December 2011. A logistic regression analysis was performed to investigate the independent risk factors. Forty-two cases diagnosed as candidemia were analyzed including 14 cases of candidemia combined with BSI and 28 cases of mono-candidemia. Ten strains of gram-positive cocci and 4 strains of gram-negative bacilli were isolated from candidemia combined with BSI group.Six strains of C.albicans, 4 strains of C.glabrata, 3 strains of C.tropicalis and 1 strain of C.krosei were isolated. There was no C.parapsilosis isolated from candidemia combined with BSI group but 9 strains in the mono-candidemia group. The septic shock rate of the candidemia combined with BSI group was higher than that of the mono-candidemia group (12/14 vs 7/28, P = 0.000). The mortality rate of the candidemia combined with BSI group was higher than that of the mono-candidemia group (10/14 vs 15/28), but the difference did not reach statistical significance (P = 0.266).Four factors were found statistically different by univariate analysis, including hospitalization more than 4 weeks (P = 0.001), bacteremia before candidemia(P = 0.005), hematological tumor (P = 0.01) and abdominal infection (P = 0.001). Multivariate analysis showed that hospitalization more than 4 weeks was the independent risk factor. Gram-positive cocci were the predominant species and septic shock was more common in candidemia combined with BSI. Hospitalization more than 4 weeks was the independent risk factor for candidemia combined with BSI.

  8. Social Sustainability Assessment across Provinces in China: An Analysis of Combining Intermediate Approach with Data Envelopment Analysis (DEA Window Analysis

    Directory of Open Access Journals (Sweden)

    Aizhen Zhang


    Full Text Available There are two categories (i.e., radial and non-radial category in conventional DEA (Data Envelopment Analysis. Recently, intermediate approach was put forward as a new third category. Intermediate approach is a newly proposed approach and there are quite limited related studies. This study contributes to the DEA studies by putting forward an analytical framework of combining intermediate approach and DEA window analysis along with the concepts of natural and managerial disposability. Such combination is quite meaningful and this new approach has three important features. To the best of our knowledge, such type of research has never been investigated by the existing studies. As an application, this approach is used to evaluate the performance of provinces in China from 2007 to 2014. Furthermore, this study develops a series of performance indices from different perspectives. This study identifies the three important findings. Firstly, eco-technology advancements can achieve economic prosperity and environmental protection simultaneously, and thus should become a new direction of climate policies. Secondly, considerable differences exist in a series of indices that evaluates the performance of various provinces and pollutants from different respective. Then, sufficient attention should be given to the provinces and the pollutants with poor performance. Finally, the Chinese government should promote efficiency improvement by “catching up” for provinces with poor performance in the short term. In addition, the central government should reduce regional disparity in order to promote the social sustainability in the long term.

  9. Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples

    Directory of Open Access Journals (Sweden)

    Jike Chen


    Full Text Available Kernel-based methods and ensemble learning are two important paradigms for the classification of hyperspectral remote sensing images. However, they were developed in parallel with different principles. In this paper, we aim to combine the advantages of kernel and ensemble methods by proposing a kernel supervised ensemble classification method. In particular, the proposed method, namely RoF-KOPLS, combines the merits of ensemble feature learning (i.e., Rotation Forest (RoF and kernel supervised learning (i.e., Kernel Orthonormalized Partial Least Square (KOPLS. In particular, the feature space is randomly split into K disjoint subspace and KOPLS is applied to each subspace to produce the new features set for the training of decision tree classifier. The final classification result is assigned to the corresponding class by the majority voting rule. Experimental results on two hyperspectral airborne images demonstrated that RoF-KOPLS with radial basis function (RBF kernel yields the best classification accuracies due to the ability of improving the accuracies of base classifiers and the diversity within the ensemble, especially for the very limited training set. Furthermore, our proposed method is insensitive to the number of subsets.

  10. Global Ensemble Generation Using Perturbed Observations in the Navy Coupled Ocean Data Assimilation System (NCODA) (United States)

    Rowley, C. D.; Frolov, S.; Stokes, M.; Hogan, P. J.; Wei, M.; Bishop, C. H.


    A perturbed-observation analysis capability has been developed for the Navy Coupled Ocean Data Assimilation system (NCODA). The resulting analysis is used to represent analysis error in the initial conditions of a global ocean forecast ensemble using the Hybrid Coordinate Ocean Model (HYCOM). For cycling with HYCOM, the NCODA system performs a 3D variational analysis of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. Sea surface height is assimilated through synthetic temperature and salinity profiles generated using the Modular Ocean Data Assimilation System (MODAS) historical regression database with surface height and surface temperature as inputs. Perturbations to the surface observations use random samples from a normal distribution scaled by the observation error standard deviation, which combines estimates of instrument and representation error. Perturbations to the synthetic profiles are generated by supplying the perturbed surface inputs to the MODAS system, resulting in correlated profile changes with vertical correlations associated with historical uncertainty about thermocline depth and gradients. For in situ profile observations, representation error is much larger than instrument error, so a technique is implemented to create correlated perturbations associated with large, mesoscale errors. Initial results from a cycling regional analysis show the resulting analysis perturbations have scales and amplitudes consistent with short term forecast error covariances. Results using the perturbed observation analysis in regional and global cycling forecast systems will be presented.

  11. An Ensemble Generator for Quantitative Precipitation Estimation Based on Censored Shifted Gamma Distributions (United States)

    Wright, D.; Kirschbaum, D.; Yatheendradas, S.


    The considerable uncertainties associated with quantitative precipitation estimates (QPE), whether from satellite platforms, ground-based weather radar, or numerical weather models, suggest that such QPE should be expressed as distributions or ensembles of possible values, rather than as single values. In this research, we borrow a framework from the weather forecast verification community, to "correct" satellite precipitation and generate ensemble QPE. This approach is based on the censored shifted gamma distribution (CSGD). The probability of precipitation, central tendency (i.e. mean), and the uncertainty can be captured by the three parameters of the CSGD. The CSGD can then be applied for simulation of rainfall ensembles using a flexible nonlinear regression framework, whereby the CSGD parameters can be conditioned on one or more reference rainfall datasets and on other time-varying covariates such as modeled or measured estimates of precipitable water and relative humidity. We present the framework and initial results by generating precipitation ensembles based on the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) dataset, using both NLDAS and PERSIANN-CDR precipitation datasets as references. We also incorporate a number of covariates from MERRA2 reanalysis including model-estimated precipitation, precipitable water, relative humidity, and lifting condensation level. We explore the prospects for applying the framework and other ensemble error models globally, including in regions where high-quality "ground truth" rainfall estimates are lacking. We compare the ensemble outputs against those of an independent rain gage-based ensemble rainfall dataset. "Pooling" of regional rainfall observations is explored as one option for improving ensemble estimates of rainfall extremes. The approach has potential applications in near-realtime, retrospective, and scenario modeling of rainfall-driven hazards such as floods and landslides

  12. A Single-column Model Ensemble Approach Applied to the TWP-ICE Experiment (United States)

    Davies, L.; Jakob, C.; Cheung, K.; DelGenio, A.; Hill, A.; Hume, T.; Keane, R. J.; Komori, T.; Larson, V. E.; Lin, Y.; hide


    Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

  13. Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

    Directory of Open Access Journals (Sweden)

    Guofeng Wang


    Full Text Available Tool condition monitoring (TCM plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM, hidden Markov model (HMM and radius basis function (RBF are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

  14. A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS Soil Moisture: Retrieval Ensembles

    Directory of Open Access Journals (Sweden)

    Ju Hyoung Lee


    Full Text Available Bias correction is a very important pre-processing step in satellite data assimilation analysis, as data assimilation itself cannot circumvent satellite biases. We introduce a retrieval algorithm-specific and spatially heterogeneous Instantaneous Field of View (IFOV bias correction method for Soil Moisture and Ocean Salinity (SMOS soil moisture. To the best of our knowledge, this is the first paper to present the probabilistic presentation of SMOS soil moisture using retrieval ensembles. We illustrate that retrieval ensembles effectively mitigated the overestimation problem of SMOS soil moisture arising from brightness temperature errors over West Africa in a computationally efficient way (ensemble size: 12, no time-integration. In contrast, the existing method of Cumulative Distribution Function (CDF matching considerably increased the SMOS biases, due to the limitations of relying on the imperfect reference data. From the validation at two semi-arid sites, Benin (moderately wet and vegetated area and Niger (dry and sandy bare soils, it was shown that the SMOS errors arising from rain and vegetation attenuation were appropriately corrected by ensemble approaches. In Benin, the Root Mean Square Errors (RMSEs decreased from 0.1248 m3/m3 for CDF matching to 0.0678 m3/m3 for the proposed ensemble approach. In Niger, the RMSEs decreased from 0.14 m3/m3 for CDF matching to 0.045 m3/m3 for the ensemble approach.

  15. JEnsembl: a version-aware Java API to Ensembl data systems. (United States)

    Paterson, Trevor; Law, Andy


    The Ensembl Project provides release-specific Perl APIs for efficient high-level programmatic access to data stored in various Ensembl database schema. Although Perl scripts are perfectly suited for processing large volumes of text-based data, Perl is not ideal for developing large-scale software applications nor embedding in graphical interfaces. The provision of a novel Java API would facilitate type-safe, modular, object-orientated development of new Bioinformatics tools with which to access, analyse and visualize Ensembl data. The JEnsembl API implementation provides basic data retrieval and manipulation functionality from the Core, Compara and Variation databases for all species in Ensembl and EnsemblGenomes and is a platform for the development of a richer API to Ensembl datasources. The JEnsembl architecture uses a text-based configuration module to provide evolving, versioned mappings from database schema to code objects. A single installation of the JEnsembl API can therefore simultaneously and transparently connect to current and previous database instances (such as those in the public archive) thus facilitating better analysis repeatability and allowing 'through time' comparative analyses to be performed. Project development, released code libraries, Maven repository and documentation are hosted at SourceForge (

  16. Pre- and post-processing of hydro-meteorological ensembles for the Norwegian flood forecasting system in 145 basins. (United States)

    Jahr Hegdahl, Trine; Steinsland, Ingelin; Merete Tallaksen, Lena; Engeland, Kolbjørn


    Probabilistic flood forecasting has an added value for decision making. The Norwegian flood forecasting service is based on a flood forecasting model that run for 145 basins. Covering all of Norway the basins differ in both size and hydrological regime. Currently the flood forecasting is based on deterministic meteorological forecasts, and an auto-regressive procedure is used to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The hydrological ensembles are based on forcing a hydrological model with meteorological ensemble forecasts of precipitation and temperature. However, the ensembles of precipitation are often biased and the spread is too small, especially for the shortest lead times, i.e. they are not calibrated. These properties will, to some extent, propagate to hydrological ensembles, that most likely will be uncalibrated as well. Pre- and post-processing methods are commonly used to obtain calibrated meteorological and hydrological ensembles respectively. Quantitative studies showing the effect of the combined processing of the meteorological (pre-processing) and the hydrological (post-processing) ensembles are however few. The aim of this study is to evaluate the influence of pre- and post-processing on the skill of streamflow predictions, and we will especially investigate if the forecasting skill depends on lead-time, basin size and hydrological regime. This aim is achieved by applying the 51 medium-range ensemble forecast of precipitation and temperature provided by the European Center of Medium-Range Weather Forecast (ECMWF). These ensembles are used as input to the operational Norwegian flood forecasting model, both raw and pre-processed. Precipitation ensembles are calibrated using a zero-adjusted gamma distribution. Temperature ensembles are calibrated using a Gaussian distribution and altitude corrected by a constant gradient

  17. Embedded random matrix ensembles in quantum physics

    CERN Document Server

    Kota, V K B


    Although used with increasing frequency in many branches of physics, random matrix ensembles are not always sufficiently specific to account for important features of the physical system at hand. One refinement which retains the basic stochastic approach but allows for such features consists in the use of embedded ensembles.  The present text is an exhaustive introduction to and survey of this important field. Starting with an easy-to-read introduction to general random matrix theory, the text then develops the necessary concepts from the beginning, accompanying the reader to the frontiers of present-day research. With some notable exceptions, to date these ensembles have primarily been applied in nuclear spectroscopy. A characteristic example is the use of a random two-body interaction in the framework of the nuclear shell model. Yet, topics in atomic physics, mesoscopic physics, quantum information science and statistical mechanics of isolated finite quantum systems can also be addressed using these ensemb...

  18. Lattice gauge theory in the microcanonical ensemble

    International Nuclear Information System (INIS)

    Callaway, D.J.E.; Rahman, A.


    The microcanonical-ensemble formulation of lattice gauge theory proposed recently is examined in detail. Expectation values in this new ensemble are determined by solving a large set of coupled ordinary differential equations, after the fashion of a molecular dynamics simulation. Following a brief review of the microcanonical ensemble, calculations are performed for the gauge groups U(1), SU(2), and SU(3). The results are compared and contrasted with standard methods of computation. Several advantages of the new formalism are noted. For example, no random numbers are required to update the system. Also, this update is performed in a simultaneous fashion. Thus the microcanonical method presumably adapts well to parallel processing techniques, especially when the p action is highly nonlocal (such as when fermions are included)

  19. Cosmological ensemble and directional averages of observables

    CERN Document Server

    Bonvin, Camille; Durrer, Ruth; Maartens, Roy; Umeh, Obinna


    We show that at second order ensemble averages of observables and directional averages do not commute due to gravitational lensing. In principle this non-commutativity is significant for a variety of quantities we often use as observables. We derive the relation between the ensemble average and the directional average of an observable, at second-order in perturbation theory. We discuss the relevance of these two types of averages for making predictions of cosmological observables, focussing on observables related to distances and magnitudes. In particular, we show that the ensemble average of the distance is increased by gravitational lensing, whereas the directional average of the distance is decreased. We show that for a generic observable, there exists a particular function of the observable that is invariant under second-order lensing perturbations.

  20. A Online NIR Sensor for the Pilot-Scale Extraction Process in Fructus Aurantii Coupled with Single and Ensemble Methods

    Directory of Open Access Journals (Sweden)

    Xiaoning Pan


    Full Text Available Model performance of the partial least squares method (PLS alone and bagging-PLS was investigated in online near-infrared (NIR sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing methods and synergy interval partial least squares (SiPLS and moving window partial least squares (MWPLS variable selection methods were compared. Single quantification models (PLS and ensemble methods combined with partial least squares (bagging-PLS were developed for quantitative analysis of naringin, hesperidin and neohesperidin. SiPLS was compared to SiPLS combined with bagging-PLS. Final results showed the root mean square error of prediction (RMSEP of bagging-PLS to be lower than that of PLS regression alone. For this reason, an ensemble method of online NIR sensor is here proposed as a means of monitoring the pilot-scale extraction process in Fructus aurantii, which may also constitute a suitable strategy for online NIR monitoring of CHM.

  1. Growth of Errors and Uncertainties in Medium Range Ensemble Forecasts of U.S. East Coast Cool Season Extratropical Cyclones (United States)

    Zheng, Minghua

    strength. The initial differences in forecasting the ridge along the west coast of North America impact the EOF1 pattern most. For PC2, it was shown that the shift of the tri-polar structure is most significantly related to the cyclone track forecasts. The EOF/fuzzy clustering tool was applied to diagnose the scenarios in operational ensemble forecast of East Coast winter storms. It was shown that the clustering method could efficiently separate the forecast scenarios associated with East Coast storms based on the 90-member multi-model ensemble. A scenario-based ensemble verification method has been proposed and applied it to examine the capability of different EPSs in capturing the analysis scenarios for historical East Coast cyclone cases at lead times of 1-9 days. The results suggest that the NCEP model performs better in short-range forecasts in capturing the analysis scenario although it is under-dispersed. The ECMWF ensemble shows the best performance in the medium range. The CMC model is found to show the smallest percentage of members in the analysis group and a relatively high missing rate, suggesting that it is less reliable regarding capturing the analysis scenario when compared with the other two EPSs. A combination of NCEP and CMC models has been found to reduce the missing rate and improve the error-spread skill in medium- to extended-range forecasts. Based on the orthogonal features of the EOF patterns, the model errors for 1-6-day forecasts have been decomposed for the leading two EOF patterns. The results for error decomposition show that the NCEP model tends to better represent both EOF1 and EOF2 patterns by showing less intensity and displacement errors during 1-3 days. The ECMWF model is found to have the smallest errors in both EOF1 and EOF2 patterns during 4-6 days. We have also found that East Coast cyclones in the ECMWF forecast tend to be towards the southwest of the other two models in representing the EOF2 pattern, which is associated with the

  2. Parametric analysis for a new combined power and ejector-absorption refrigeration cycle

    International Nuclear Information System (INIS)

    Wang Jiangfeng; Dai Yiping; Zhang Taiyong; Ma Shaolin


    A new combined power and ejector-absorption refrigeration cycle is proposed, which combines the Rankine cycle and the ejector-absorption refrigeration cycle, and could produce both power output and refrigeration output simultaneously. This combined cycle, which originates from the cycle proposed by authors previously, introduces an ejector between the rectifier and the condenser, and provides a performance improvement without greatly increasing the complexity of the system. A parametric analysis is conducted to evaluate the effects of the key thermodynamic parameters on the cycle performance. It is shown that heat source temperature, condenser temperature, evaporator temperature, turbine inlet pressure, turbine inlet temperature, and basic solution ammonia concentration have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. It is evident that the ejector can improve the performance of the combined cycle proposed by authors previously.

  3. Parametric analysis and optimization for a combined power and refrigeration cycle

    International Nuclear Information System (INIS)

    Wang Jiangfeng; Dai Yiping; Gao Lin


    A combined power and refrigeration cycle is proposed, which combines the Rankine cycle and the absorption refrigeration cycle. This combined cycle uses a binary ammonia-water mixture as the working fluid and produces both power output and refrigeration output simultaneously with only one heat source. A parametric analysis is conducted to evaluate the effects of thermodynamic parameters on the performance of the combined cycle. It is shown that heat source temperature, environment temperature, refrigeration temperature, turbine inlet pressure, turbine inlet temperature, and basic solution ammonia concentration have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. A parameter optimization is achieved by means of genetic algorithm to reach the maximum exergy efficiency. The optimized exergy efficiency is 43.06% under the given condition

  4. A unified MGF-based capacity analysis of diversity combiners over generalized fading channels

    KAUST Repository

    Yilmaz, Ferkan


    Unified exact ergodic capacity results for L-branch coherent diversity combiners including equal-gain combining (EGC) and maximal-ratio combining (MRC) are not known. This paper develops a novel generic framework for the capacity analysis of L-branch EGC/MRC over generalized fading channels. The framework is used to derive new results for the gamma-shadowed generalized Nakagami-m fading model which can be a suitable model for the fading environments encountered by high frequency (60 GHz and above) communications. The mathematical formalism is illustrated with some selected numerical and simulation results confirming the correctness of our newly proposed framework. © 2012 IEEE.

  5. Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China. (United States)

    Zhai, Binxu; Chen, Jianguo


    A stacked ensemble model is developed for forecasting and analyzing the daily average concentrations of fine particulate matter (PM 2.5 ) in Beijing, China. Special feature extraction procedures, including those of simplification, polynomial, transformation and combination, are conducted before modeling to identify potentially significant features based on an exploratory data analysis. Stability feature selection and tree-based feature selection methods are applied to select important variables and evaluate the degrees of feature importance. Single models including LASSO, Adaboost, XGBoost and multi-layer perceptron optimized by the genetic algorithm (GA-MLP) are established in the level 0 space and are then integrated by support vector regression (SVR) in the level 1 space via stacked generalization. A feature importance analysis reveals that nitrogen dioxide (NO 2 ) and carbon monoxide (CO) concentrations measured from the city of Zhangjiakou are taken as the most important elements of pollution factors for forecasting PM 2.5 concentrations. Local extreme wind speeds and maximal wind speeds are considered to extend the most effects of meteorological factors to the cross-regional transportation of contaminants. Pollutants found in the cities of Zhangjiakou and Chengde have a stronger impact on air quality in Beijing than other surrounding factors. Our model evaluation shows that the ensemble model generally performs better than a single nonlinear forecasting model when applied to new data with a coefficient of determination (R 2 ) of 0.90 and a root mean squared error (RMSE) of 23.69μg/m 3 . For single pollutant grade recognition, the proposed model performs better when applied to days characterized by good air quality than when applied to days registering high levels of pollution. The overall classification accuracy level is 73.93%, with most misclassifications made among adjacent categories. The results demonstrate the interpretability and generalizability of

  6. Comparative performance analysis of combined-cycle pulse detonation turbofan engines (PDTEs

    Directory of Open Access Journals (Sweden)

    Sudip Bhattrai


    Full Text Available Combined-cycle pulse detonation engines are promising contenders for hypersonic propulsion systems. In the present study, design and propulsive performance analysis of combined-cycle pulse detonation turbofan engines (PDTEs is presented. Analysis is done with respect to Mach number at two consecutive modes of operation: (1 Combined-cycle PDTE using a pulse detonation afterburner mode (PDA-mode and (2 combined-cycle PDTE in pulse detonation ramjet engine mode (PDRE-mode. The performance of combined-cycle PDTEs is compared with baseline afterburning turbofan and ramjet engines. The comparison of afterburning modes is done for Mach numbers from 0 to 3 at 15.24 km altitude conditions, while that of pulse detonation ramjet engine (PDRE is done for Mach 1.5 to Mach 6 at 18.3 km altitude conditions. The analysis shows that the propulsive performance of a turbine engine can be greatly improved by replacing the conventional afterburner with a pulse detonation afterburner (PDA. The PDRE also outperforms its ramjet counterpart at all flight conditions considered herein. The gains obtained are outstanding for both the combined-cycle PDTE modes compared to baseline turbofan and ramjet engines.

  7. Precipitation Ensembles from Single-Value Forecasts for Hydrological Ensemble Forecasting (United States)

    Demargne, J.; Schaake, J.; Wu, L.; Welles, E.; Herr, H.; Seo, D.


    An ensemble pre-processor was developed to produce short-term precipitation ensembles using operational single-value forecasts. The methodology attempts to quantify the uncertainty in the single-value forecast and to capture the skill therein. These precipitation ensemble forecasts could be then ingested in the NOAA/National Weather Service (NWS) Ensemble Streamflow Prediction (ESP) system to produce probabilistic hydrological forecasts that reflect the uncertainty in forecast precipitation. The procedure constructs the joint distribution of forecast and observed precipitation from historical pairs of forecast and observed values. The probability distribution function of the future events that may occur given a particular single-value forecast is then the conditional distribution of observed precipitation given the forecast. To generate individual ensemble members for each lead time and each location, the historical observed values are replaced with values sampled from the conditional distribution given the single-value forecast. The replacement procedure matches the ranks of historical and rescaled values to preserve the space-time properties of observed precipitation in the ensemble traces. Currently, the ensemble pre-processor is being tested and evaluated at four NOAA/NWS River Forecast Centers (RFCs) in the U.S.A. In this contribution, we present the results thus far from the field and retrospective evaluations, and key science issues that must be addressed toward national operational implementation.

  8. Cardiac arrhythmia detection using combination of heart rate variability analyses and PUCK analysis. (United States)

    Mahananto, Faizal; Igasaki, Tomohiko; Murayama, Nobuki


    This paper presents cardiac arrhythmia detection using the combination of a heart rate variability (HRV) analysis and a "potential of unbalanced complex kinetics" (PUCK) analysis. Detection performance was improved by adding features extracted from the PUCK analysis. Initially, R-R interval data were extracted from the original electrocardiogram (ECG) recordings and were cut into small segments and marked as either normal or arrhythmia. HRV analyses then were conducted using the segmented R-R interval data, including a time-domain analysis, frequency-domain analysis, and nonlinear analysis. In addition to the HRV analysis, PUCK analysis, which has been implemented successfully in a foreign exchange market series to characterize change, was employed. A decision-tree algorithm was applied to all of the obtained features for classification. The proposed method was tested using the MIT-BIH arrhythmia database and had an overall classification accuracy of 91.73%. After combining features obtained from the PUCK analysis, the overall accuracy increased to 92.91%. Therefore, we suggest that the use of a PUCK analysis in conjunction with HRV analysis might improve performance accuracy for the detection of cardiac arrhythmia.

  9. Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter

    NARCIS (Netherlands)

    Leeuwenburgh, O.; Arts, R.


    The Ensemble Kalman Filter (EnKF), in combination with travel-time parameterization, provides a robust and flexible method for quantitative multi-model history matching to time-lapse seismic data. A disadvantage of the parameterization in terms of travel-times is that it requires simulation of

  10. BRAID: A Unifying Paradigm for the Analysis of Combined Drug Action. (United States)

    Twarog, Nathaniel R; Stewart, Elizabeth; Hammill, Courtney Vowell; A Shelat, Anang


    With combination therapies becoming increasingly vital to understanding and combatting disease, a reliable method for analyzing combined dose response is essential. The importance of combination studies both in basic and translational research necessitates a method that can be applied to a wide range of experimental and analytical conditions. However, despite increasing demand, no such unified method has materialized. Here we introduce the Bivariate Response to Additive Interacting Doses (BRAID) model, a response surface model that combines the simplicity and intuitiveness needed for basic interaction classifications with the versatility and depth needed to analyze a combined response in the context of pharmacological and toxicological constraints. We evaluate the model in a series of simulated combination experiments, a public combination dataset, and several experiments on Ewing's Sarcoma. The resulting interaction classifications are more consistent than those produced by traditional index methods, and show a strong relationship between compound mechanisms and nature of interaction. Furthermore, analysis of fitted response surfaces in the context of pharmacological constraints yields a more concrete prediction of combination efficacy that better agrees with in vivo evaluations.

  11. Determination of the input parameters for inelastic background analysis combined with HAXPES using a reference sample

    DEFF Research Database (Denmark)

    Zborowski, C.; Renault, O; Torres, A


    The recent progress in HAXPES combined with Inelastic Background Analysis makes this method a powerful, non-destructive solution to get quantitative information on deeply buried layers and interfaces at depths up to 70. nm. However, we recently highlighted the need for carefully choosing the scat......The recent progress in HAXPES combined with Inelastic Background Analysis makes this method a powerful, non-destructive solution to get quantitative information on deeply buried layers and interfaces at depths up to 70. nm. However, we recently highlighted the need for carefully choosing...

  12. Choice of implant combinations in total hip replacement: systematic review and network meta-analysis. (United States)

    López-López, José A; Humphriss, Rachel L; Beswick, Andrew D; Thom, Howard H Z; Hunt, Linda P; Burston, Amanda; Fawsitt, Christopher G; Hollingworth, William; Higgins, Julian P T; Welton, Nicky J; Blom, Ashley W; Marques, Elsa M R


    Objective  To compare the survival of different implant combinations for primary total hip replacement (THR). Design  Systematic review and network meta-analysis. Data sources  Medline, Embase, The Cochrane Library,, WHO International Clinical Trials Registry Platform, and the EU Clinical Trials Register. Review methods  Published randomised controlled trials comparing different implant combinations. Implant combinations were defined by bearing surface materials (metal-on-polyethylene, ceramic-on-polyethylene, ceramic-on-ceramic, or metal-on-metal), head size (large ≥36 mm or small meta-analysis for revision. There was no evidence that the risk of revision surgery was reduced by other implant combinations compared with the reference implant combination. Although estimates are imprecise, metal-on-metal, small head, cemented implants (hazard ratio 4.4, 95% credible interval 1.6 to 16.6) and resurfacing (12.1, 2.1 to 120.3) increase the risk of revision at 0-2 years after primary THR compared with the reference implant combination. Similar results were observed for the 2-10 years period. 31 studies (2888 patients) were included in the analysis of Harris hip score. No implant combination had a better score than the reference implant combination. Conclusions  Newer implant combinations were not found to be better than the reference implant combination (metal-on-polyethylene (not highly cross linked), small head, cemented) in terms of risk of revision surgery or Harris hip score. Metal-on-metal, small head, cemented implants and resurfacing increased the risk of revision surgery compared with the reference implant combination. The results were consistent with observational evidence and were replicated in sensitivity analysis but were limited by poor reporting across studies. Systematic review registration  PROSPERO CRD42015019435. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence

  13. Microarray gene cluster identification and annotation through cluster ensemble and EM-based informative textual summarization. (United States)

    Hu, Xiaohua; Park, E K; Zhang, Xiaodan


    Generating high-quality gene clusters and identifying the underlying biological mechanism of the gene clusters are the important goals of clustering gene expression analysis. To get high-quality cluster results, most of the current approaches rely on choosing the best cluster algorithm, in which the design biases and assumptions meet the underlying distribution of the dataset. There are two issues for this approach: 1) usually, the underlying data distribution of the gene expression datasets is unknown and 2) there are so many clustering algorithms available and it is very challenging to choose the proper one. To provide a textual summary of the gene clusters, the most explored approach is the extractive approach that essentially builds upon techniques borrowed from the information retrieval, in which the objective is to provide terms to be used for query expansion, and not to act as a stand-alone summary for the entire document sets. Another drawback is that the clustering quality and cluster interpretation are treated as two isolated research problems and are studied separately. In this paper, we design and develop a unified system Gene Expression Miner to address these challenging issues in a principled and general manner by integrating cluster ensemble, text clustering, and multidocument summarization and provide an environment for comprehensive gene expression data analysis. We present a novel cluster ensemble approach to generate high-quality gene cluster. In our text summarization module, given a gene cluster, our expectation-maximization based algorithm can automatically identify subtopics and extract most probable terms for each topic. Then, the extracted top k topical terms from each subtopic are combined to form the biological explanation of each gene cluster. Experimental results demonstrate that our system can obtain high-quality clusters and provide informative key terms for the gene clusters.

  14. Combination of contrast-enhanced wall motion analysis and myocardial deformation imaging during dobutamine stress echocardiography. (United States)

    Nagy, Anikó I; Sahlén, Anders; Manouras, Aristomenis; Henareh, Loghman; da Silva, Cristina; Günyeli, Elif; Apor, Astrid A; Merkely, Béla; Winter, Reidar


    The combination of deformation analysis with conventional wall motion scoring (WMS) has been shown to increase the diagnostic sensitivity of dobutamine stress echocardiography (DSE). The feasibility and diagnostic power of WMS is largely improved by contrast agents; however, they are not used in combination with deformation analysis, as contrast agents are generally considered to render strain measurement unfeasible. To assess the feasibility of tissue velocity (TVI)- and 2D speckle tracking (ST)-based strain analysis during contrast-enhanced DSE; and to show whether there is an incremental value in combining deformation analysis with contrast-enhanced WMS. DS echocardiograms containing native, tissue Doppler, and contrast-enhanced loops of 60 patients were analysed retrospectively. The feasibility of WMS, TVI-, and ST-strain measurement was determined in 40 patients according to pre-defined criteria. The diagnostic ability of a combined protocol integrating data from contrast-WMS and TVI-strain measurement was then compared with contrast-WMS alone in all 60 patients, using coronary angiograms as a gold standard. Both TVI- and ST-based strain analysis were feasible during contrast-DSE (feasibility at peak stress: 87 and 75%). At the patient level, the diagnostic accuracy of the combined method did not prove superior to contrast-WMS (82 vs. 78%); a trend towards improved sensitivity and specificity for detecting coronary artery disease in the right coronary artery circulation (sensitivity: 85 vs. 77%, P = NS; specificity: 96 vs. 94%) was, however, observed. Both TVI- and ST-based myocardial deformation analysis are feasible during contrast-enhanced DSE, however, our results fail to demonstrate a clear diagnostic benefit of additional strain analysis over expert WMS alone. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email:

  15. two-body random matrix ensembles

    Indian Academy of Sciences (India)


    Feb 3, 2015 ... Random matrix theory (RMT) introduced to describe statistical properties of the energy levels of complex nuclei has seen tremendous growth recently [1]. It is now well rec- ognized that, the quantum system whose classical counterpart is chaotic, will follow one of the three classical random matrix ensembles ...

  16. The Phantasmagoria of Competition in School Ensembles (United States)

    Abramo, Joseph Michael


    Participation in competition festivals--where students and ensembles compete against each other for high scores and accolades--is a widespread practice in North American formal music education. In this article, I use Marx's theories of labor, value, and phantasmagoria to suggest a capitalist logic that structures these competitions. Marx's…

  17. Agonism/antagonism switching in allosteric ensembles. (United States)

    Motlagh, Hesam N; Hilser, Vincent J


    Ligands for several transcription factors can act as agonists under some conditions and antagonists under others. The structural and molecular bases of such effects are unknown. Previously, we demonstrated how the folding of intrinsically disordered (ID) protein sequences, in particular, and population shifts, in general, could be used to mediate allosteric coupling between different functional domains, a model that has subsequently been validated in several systems. Here it is shown that population redistribution within allosteric systems can be used as a mechanism to tune protein ensembles such that a given ligand can act as both an agonist and an antagonist. Importantly, this mechanism can be robustly encoded in the ensemble, and does not require that the interactions between the ligand and the protein differ when it is acting either as an agonist or an antagonist. Instead, the effect is due to the relative probabilities of states prior to the addition of the ligand. The ensemble view of allostery that is illuminated by these studies suggests that rather than being seen as switches with fixed responses to allosteric activation, ensembles can evolve to be "functionally pluripotent," with the capacity to up or down regulate activity in response to a stimulus. This result not only helps to explain the prevalence of intrinsic disorder in transcription factors and other cell signaling proteins, it provides important insights about the energetic ground rules governing site-to-site communication in all allosteric systems.

  18. Marking up lattice QCD configurations and ensembles

    Energy Technology Data Exchange (ETDEWEB)

    P.Coddington; B.Joo; C.M.Maynard; D.Pleiter; T.Yoshie


    QCDml is an XML-based markup language designed for sharing QCD configurations and ensembles world-wide via the International Lattice Data Grid (ILDG). Based on the latest release, we present key ingredients of the QCDml in order to provide some starting points for colleagues in this community to markup valuable configurations and submit them to the ILDG.

  19. NYYD Ensemble ja Riho Sibul / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-


    Gavin Bryarsi teos "Jesus' Blood Never Failed Me Yet" NYYD Ensemble'i ja Riho Sibula esituses 27. detsembril Pauluse kirikus Tartus ja 28. detsembril Rootsi- Mihkli kirikus Tallinnas. Kaastegevad Tartu Ülikooli Kammerkoor (Tartus) ja kammerkoor Voces Musicales (Tallinnas). Kunstiline juht Olari Elts

  20. Understanding the Ensemble Pianist: A Theoretical Framework (United States)

    Kokotsaki, Dimitra


    The aim of this study was to develop a theoretical model of the attainment of high quality in musical ensemble performance as perceived by the pianist and to identify the factors affecting this process. The research has followed an inductive interpretative approach, applying qualitative methods. The analytic material was collected through the…

  1. HPLC Fingerprint Analysis Combined with Chemometrics for Authentication of Kaempferia galanga from Related Species

    Directory of Open Access Journals (Sweden)

    Cahya Septyanti


    Full Text Available Fingerprint analysis using high performance liquid chromatography (HPLC has been developed for authentication of Kaempferia galanga from related species, such as Kaempferia pandurata and K. rotunda. By comparing the fingerprint chromatograms of K. galanga, K. pandurata and K. rotunda, we could identify K. galanga samples and detect adulteration of K. galanga from K. pandurata and K. rotunda by using their marker peaks. We also combined HPLC fingerprint with chemometrics for discrimination the three species and also for authentication of K. galanga. All the three species and K. galanga adulterated with K. pandurata and K. rotunda were discriminated successfully by using principal component analysis (PCA and discriminant analysis (DA. This result indicates that HPLC fingerprint analysis in combination with PCA (PC1 = 30.06% and PC2 = 34.74% and DA (DF1 = 94.59% and DF2 = 3.32% could be used for authentication of K. galanga samples from the related species.

  2. Analysis of the Interactions of Botanical Extract Combinations Against the Viability of Prostate Cancer Cell Lines

    Directory of Open Access Journals (Sweden)

    Lynn S. Adams


    Full Text Available Herbal medicines are often combinations of botanical extracts that are assumed to have additive or synergistic effects. The purpose of this investigation was to compare the effect of individual botanical extracts with combinations of extracts on prostate cell viability. We then modeled the interactions between botanical extracts in combination isobolographically. Scutellaria baicalensis, Rabdosia rubescens, Panax-pseudo ginseng, Dendranthema morifolium, Glycyrrhiza uralensis and Serenoa repens were collected, taxonomically identified and extracts prepared. Effects of the extracts on cell viability were quantitated in prostate cell lines using a luminescent ATP cell viability assay. Combinations of two botanical extracts of the four most active extracts were tested in the 22Rv1 cell line and their interactions assessed using isobolographic analysis. Each extract significantly inhibited the proliferation of prostate cell lines in a time- and dose-dependent manner except repens. The most active extracts, baicalensis, D. morifolium, G. uralensis and R. rubescens were tested as two-extract combinations. baicalensis and D. morifolium when combined were additive with a trend toward synergy, whereas D. morifolium and R. rubescens together were additive. The remaining two-extract combinations showed antagonism. The four extracts together were significantly more effective than the two-by-two combinations and the individual extracts alone. Combining the four herbal extracts significantly enhanced their activity in the cell lines tested compared with extracts alone. The less predictable nature of the two-way combinations suggests a need for careful characterization of the effects of each individual herb based on their intended use.

  3. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP


    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  4. Global Ensemble Forecast System (GEFS) [2.5 Deg. (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  5. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA. (United States)

    Biggs, Matthew B; Papin, Jason A


    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  6. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Directory of Open Access Journals (Sweden)

    Matthew B Biggs


    Full Text Available Genome-scale metabolic network reconstructions (GENREs are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA. We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  7. Fingerprint prediction using classifier ensembles

    CSIR Research Space (South Africa)

    Molale, P


    Full Text Available discrimination (LgDA): Logistic Discrimination Analysis (LgDA), due to Cox (1966) is related to logistic regression analysis. The dependent variable can only take values of 0 and 1, say, given two classes. This technique is partially parametric... approaches (i.e., no assumptions about the data are made). They are represented by connections between a very large number of simple computing processors or elements (neurons). They have been used for a variety of classification and regression problems...

  8. A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects. (United States)

    Cacha, L A; Parida, S; Dehuri, S; Cho, S-B; Poznanski, R R


    The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

  9. An Integrated Strategy Framework (ISF) for Combining Porter's 5-Forces, Diamond, PESTEL, and SWOT Analysis


    Anton, Roman


    INTRODUCTION Porter's Five-Forces, Porter's Diamond, PESTEL, the 6th-Forths, and Humphrey's SWOT analysis are among the most important and popular concepts taught in business schools around the world. A new integrated strategy framework (ISF) combines all major concepts. PURPOSE Porter's Five-Forces, Porter's Diamond, PESTEL, the 6th-Forths, and Humphrey's SWOT analysis are among the most important and popular concepts taught in business schools around the world. A new integrated strategy fr...

  10. Near field analysis of CSG and BSG combined element under high power laser condition (United States)

    Yao, Xin; Gao, Fuhua; Zhang, Yixiao; Wang, Lei; Guo, Yongkang; Hou, Xi


    In high power laser system, it is of great interest to combine two or more diffractive structures, in particular, the beam-sampling gratings (BSG) and the color separation gratings (CSG), onto one element. However, the combined element with diffractive structure on both surfaces, may cause serious laser induced damage to the element itself. So, this paper use Fourier modal method to analyze the near field characteristic of CSG and BSG combined element. Through theoretically analysis and numerical calculation, amplitude and phase distribution of electric field are present both inside and outside the diffractive structural region, and the maximum peak-to-average modulation in near field is also given. Based on this study, the most possibility of optical damage induced by beam modulation of CSG and BSG combined element appears in the neighborhood of the interface.

  11. Ensemble-averaged Rabi oscillations in a ferromagnetic CoFeB film. (United States)

    Capua, Amir; Rettner, Charles; Yang, See-Hun; Phung, Timothy; Parkin, Stuart S P


    Rabi oscillations describe the process whereby electromagnetic radiation interacts coherently with spin states in a non-equilibrium interaction. To date, Rabi oscillations have not been studied in one of the most common spin ensembles in nature: spins in ferromagnets. Here, using a combination of femtosecond laser pulses and microwave excitations, we report the classical analogue of Rabi oscillations in ensemble-averaged spins of a ferromagnet. The microwave stimuli are shown to extend the coherence-time resulting in resonant spin amplification. The results we present in a dense magnetic system are qualitatively similar to those reported previously in semiconductors which have five orders of magnitude fewer spins and which require resonant optical excitations to spin-polarize the ensemble. Our study is a step towards connecting concepts used in quantum processing with spin-transport effects in ferromagnets. For example, coherent control may become possible without the complications of driving an electromagnetic field but rather by using spin-polarized currents.

  12. Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

    DEFF Research Database (Denmark)

    Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas


    Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored...... in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments...... on the Washington RGB-D Object Dataset show that our best performing ensemble improves the recognition performance with 0.7% compared to using the baseline model alone....

  13. Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

    International Nuclear Information System (INIS)

    Baraldi, Piero; Razavi-Far, Roozbeh; Zio, Enrico


    An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

  14. Ensemble-averaged Rabi oscillations in a ferromagnetic CoFeB film (United States)

    Capua, Amir; Rettner, Charles; Yang, See-Hun; Phung, Timothy; Parkin, Stuart S. P.


    Rabi oscillations describe the process whereby electromagnetic radiation interacts coherently with spin states in a non-equilibrium interaction. To date, Rabi oscillations have not been studied in one of the most common spin ensembles in nature: spins in ferromagnets. Here, using a combination of femtosecond laser pulses and microwave excitations, we report the classical analogue of Rabi oscillations in ensemble-averaged spins of a ferromagnet. The microwave stimuli are shown to extend the coherence-time resulting in resonant spin amplification. The results we present in a dense magnetic system are qualitatively similar to those reported previously in semiconductors which have five orders of magnitude fewer spins and which require resonant optical excitations to spin-polarize the ensemble. Our study is a step towards connecting concepts used in quantum processing with spin-transport effects in ferromagnets. For example, coherent control may become possible without the complications of driving an electromagnetic field but rather by using spin-polarized currents.

  15. Multi-Year Combination of Tide Gauge Benchmark Monitoring (TIGA) Analysis Center Products (United States)

    Hunegnaw, A.; Teferle, F. N.


    In 2013 the International GNSS Service (IGS) Tide Gauge Benchmark Monitoring (TIGA) Working Group (WG) started their reprocessing campaign, which proposes to re-analyze all relevant Global Positioning System (GPS) observations from 1994 to 2013. This re-processed dataset will provide high quality estimates of land motions, enabling regional and global high-precision geophysical/geodetic studies. Several of the individual TIGA Analysis Centers (TACs) have completed processing the full history of GPS observations recorded by the IGS global network, as well as, many other GPS stations at or close to tide gauges, which are available from the TIGA data centre at the University of La Rochelle ( The TAC solutions contain a total of over 700 stations. Following the recent improvements in processing models and strategies, this is the first complete reprocessing attempt by the TIGA WG to provide homogeneous position time series. The TIGA Combination Centre (TCC) at the University of Luxembourg (UL) has computed a first multi-year weekly combined solution using two independent combination software packages: CATREF and GLOBK. These combinations allow an evaluation of any effects from the combination software and of the individual TAC contributions and their influences on the combined solution. In this study we will present the first UL TIGA multi-year combination results and discuss these in terms of geocentric sea level changes

  16. The role of model dynamics in ensemble Kalman filter performance for chaotic systems (United States)

    Ng, G.-H.C.; McLaughlin, D.; Entekhabi, D.; Ahanin, A.


    The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or 'diverging', when applied to large chaotic systems such as atmospheric and ocean models. Past studies have demonstrated the adverse impact of sampling error during the filter's update step. We examine how system dynamics affect EnKF performance, and whether the absence of certain dynamic features in the ensemble may lead to divergence. The EnKF is applied to a simple chaotic model, and ensembles are checked against singular vectors of the tangent linear model, corresponding to short-term growth and Lyapunov vectors, corresponding to long-term growth. Results show that the ensemble strongly aligns itself with the subspace spanned by unstable Lyapunov vectors. Furthermore, the filter avoids divergence only if the full linearized long-term unstable subspace is spanned. However, short-term dynamics also become important as non-linearity in the system increases. Non-linear movement prevents errors in the long-term stable subspace from decaying indefinitely. If these errors then undergo linear intermittent growth, a small ensemble may fail to properly represent all important modes, causing filter divergence. A combination of long and short-term growth dynamics are thus critical to EnKF performance. These findings can help in developing practical robust filters based on model dynamics. ?? 2011 The Authors Tellus A ?? 2011 John Wiley & Sons A/S.

  17. Ensemble perception of emotions in autistic and typical children and adolescents

    Directory of Open Access Journals (Sweden)

    Themelis Karaminis


    Full Text Available Ensemble perception, the ability to assess automatically the summary of large amounts of information presented in visual scenes, is available early in typical development. This ability might be compromised in autistic children, who are thought to present limitations in maintaining summary statistics representations for the recent history of sensory input. Here we examined ensemble perception of facial emotional expressions in 35 autistic children, 30 age- and ability-matched typical children and 25 typical adults. Participants received three tasks: a an ‘ensemble’ emotion discrimination task; b a baseline (single-face emotion discrimination task; and c a facial expression identification task. Children performed worse than adults on all three tasks. Unexpectedly, autistic and typical children were, on average, indistinguishable in their precision and accuracy on all three tasks. Computational modelling suggested that, on average, autistic and typical children used ensemble-encoding strategies to a similar extent; but ensemble perception was related to non-verbal reasoning abilities in autistic but not in typical children. Eye-movement data also showed no group differences in the way children attended to the stimuli. Our combined findings suggest that the abilities of autistic and typical children for ensemble perception of emotions are comparable on average.

  18. Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter

    Energy Technology Data Exchange (ETDEWEB)

    Ham, Yoo-Geun; Kang, In-Sik [Seoul National University, School of Earth and Environment Sciences, Seoul (Korea); Kug, Jong-Seong [Korea Ocean Research and Development Institute, Ansan (Korea)


    A method for selecting optimal initial perturbations is developed within the framework of an ensemble Kalman filter (EnKF). Among the initial conditions generated by EnKF, ensemble members with fast growing perturbations are selected to optimize the ENSO seasonal forecast skills. Seasonal forecast experiments show that the forecast skills with the selected ensemble members are significantly improved compared with other ensemble members for up to 1-year lead forecasts. In addition, it is found that there is a strong relationship between the forecast skill improvements and flow-dependent instability. That is, correlation skills are significantly improved over the region where the predictable signal is relatively small (i.e. an inverse relationship). It is also shown that forecast skills are significantly improved during ENSO onset and decay phases, which are the most unpredictable periods among the ENSO events. (orig.)

  19. Reliability analysis of production ships with emphasis on load combination and ultimate strength

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Xiaozhi


    This thesis deals with ultimate strength and reliability analysis of offshore production ships, accounting for stochastic load combinations, using a typical North Sea production ship for reference. A review of methods for structural reliability analysis is presented. Probabilistic methods are established for the still water and vertical wave bending moments. Linear stress analysis of a midships transverse frame is carried out, four different finite element models are assessed. Upon verification of the general finite element code ABAQUS with a typical ship transverse girder example, for which test results are available, ultimate strength analysis of the reference transverse frame is made to obtain the ultimate load factors associated with the specified pressure loads in Det norske Veritas Classification rules for ships and rules for production vessels. Reliability analysis is performed to develop appropriate design criteria for the transverse structure. It is found that the transverse frame failure mode does not seem to contribute to the system collapse. Ultimate strength analysis of the longitudinally stiffened panels is performed, accounting for the combined biaxial and lateral loading. Reliability based design of the longitudinally stiffened bottom and deck panels is accomplished regarding the collapse mode under combined biaxial and lateral loads. 107 refs., 76 refs., 37 tabs.

  20. Quantum canonical ensemble: A projection operator approach (United States)

    Magnus, Wim; Lemmens, Lucien; Brosens, Fons


    Knowing the exact number of particles N, and taking this knowledge into account, the quantum canonical ensemble imposes a constraint on the occupation number operators. The constraint particularly hampers the systematic calculation of the partition function and any relevant thermodynamic expectation value for arbitrary but fixed N. On the other hand, fixing only the average number of particles, one may remove the above constraint and simply factorize the traces in Fock space into traces over single-particle states. As is well known, that would be the strategy of the grand-canonical ensemble which, however, comes with an additional Lagrange multiplier to impose the average number of particles. The appearance of this multiplier can be avoided by invoking a projection operator that enables a constraint-free computation of the partition function and its derived quantities in the canonical ensemble, at the price of an angular or contour integration. Introduced in the recent past to handle various issues related to particle-number projected statistics, the projection operator approach proves beneficial to a wide variety of problems in condensed matter physics for which the canonical ensemble offers a natural and appropriate environment. In this light, we present a systematic treatment of the canonical ensemble that embeds the projection operator into the formalism of second quantization while explicitly fixing N, the very number of particles rather than the average. Being applicable to both bosonic and fermionic systems in arbitrary dimensions, transparent integral representations are provided for the partition function ZN and the Helmholtz free energy FN as well as for two- and four-point correlation functions. The chemical potential is not a Lagrange multiplier regulating the average particle number but can be extracted from FN+1 -FN, as illustrated for a two-dimensional fermion gas.

  1. Combined EUV reflectance and X-ray reflectivity data analysis of periodic multilayer structures

    NARCIS (Netherlands)

    Yakunin, S.N.; Makhotkin, Igor Alexandrovich; Nikolaev, K.V.; van de Kruijs, Robbert Wilhelmus Elisabeth; Chuev, M.A.; Bijkerk, Frederik


    We present a way to analyze the chemical composition of periodical multilayer structures using the simultaneous analysis of grazing incidence hard X-Ray reflectivity (GIXR) and normal incidence extreme ultraviolet reflectance (EUVR). This allows to combine the high sensitivity of GIXR data to layer

  2. Combined calibration and sensitivity analysis for a water quality model of the Biebrza River, Poland

    NARCIS (Netherlands)

    Perk, van der M.; Bierkens, M.F.P.


    A study was performed to quantify the error in results of a water quality model of the Biebrza River, Poland, due to uncertainties in calibrated model parameters. The procedure used in this study combines calibration and sensitivity analysis. Finally,the model was validated to test the model

  3. Thermodynamic analysis of a new combined cooling and power system using ammonia–water mixture

    International Nuclear Information System (INIS)

    Wang, Jiangfeng; Wang, Jianyong; Zhao, Pan; Dai, Yiping


    Highlights: • A new combined cooling and power system is proposed. • Exergy destruction analysis is used to identify irreversibility of components in system. • Thermodynamic parameter analysis is performed for system. - Abstract: In order to achieve both power and cooling supply for users, a new combined cooling and power system using ammonia–water mixture is proposed to utilizing low grade heat sources, such as industrial waste heat, solar energy and geothermal energy. The proposed system combines a Kalina cycle and an ammonia–water absorption refrigeration cycle, in which the ammonia–water turbine exhaust is delivered to a separator to extract purer ammonia vapor. The purer ammonia vapor enters an evaporator to generate refrigeration output after being condensed and throttled. Mathematical models are established to simulate the combined system under steady-state conditions. Exergy destruction analysis is conducted to display the exergy destruction distribution in the system qualitatively and the results show that the major exergy destruction occurs in the heat exchangers. Finally a thermodynamic sensitivity analysis is performed and reveals that with an increase in the pressure of separator I or the ammonia mass fraction of basic solution, thermal efficiency and exergy efficiency of the system increase, whereas with an increase in the temperature of separator I, the ammonia–water turbine back pressure or the condenser II pressure, thermal efficiency and exergy efficiency of the system drop.

  4. Sample preparation for combined chemical analysis and bioassay application in water quality assessment

    NARCIS (Netherlands)

    Kolkman, A.; Schriks, M.; Brand, W; Bäuerlein, P.S.; van der Kooi, M.M.E.; van Doorn, R.H.; Emke, E.; Reus, A.; van der Linden, S.; de Voogt, P.; Heringa, M.B.


    The combination of in vitro bioassays and chemical screening can provide a powerful toolbox to determine biologically relevant compounds in water extracts. In this study, a sample preparation method is evaluated for the suitability for both chemical analysis and in vitro bioassays. A set of 39

  5. Combined sequence-based and genetic mapping analysis of complex traits in outbred rats

    NARCIS (Netherlands)

    Baud, Amelie; Hermsen, Roel; Guryev, Victor; Stridh, Pernilla; Graham, Delyth; McBride, Martin W.; Foroud, Tatiana; Calderari, Sophie; Diez, Margarita; Ockinger, Johan; Beyeen, Amennai D.; Gillett, Alan; Abdelmagid, Nada; Guerreiro-Cacais, Andre Ortlieb; Jagodic, Maja; Tuncel, Jonatan; Norin, Ulrika; Beattie, Elisabeth; Huynh, Ngan; Miller, William H.; Koller, Daniel L.; Alam, Imranul; Falak, Samreen; Osborne-Pellegrin, Mary; Martinez-Membrives, Esther; Canete, Toni; Blazquez, Gloria; Vicens-Costa, Elia; Mont-Cardona, Carme; Diaz-Moran, Sira; Tobena, Adolf; Hummel, Oliver; Zelenika, Diana; Saar, Kathrin; Patone, Giannino; Bauerfeind, Anja; Bihoreau, Marie-Therese; Heinig, Matthias; Lee, Young-Ae; Rintisch, Carola; Schulz, Herbert; Wheeler, David A.; Worley, Kim C.; Muzny, Donna M.; Gibbs, Richard A.; Lathrop, Mark; Lansu, Nico; Toonen, Pim; Ruzius, Frans Paul; de Bruijn, Ewart; Hauser, Heidi; Adams, David J.; Keane, Thomas; Atanur, Santosh S.; Aitman, Tim J.; Flicek, Paul; Malinauskas, Tomas; Jones, E. Yvonne; Ekman, Diana; Lopez-Aumatell, Regina; Dominiczak, Anna F.; Johannesson, Martina; Holmdahl, Rikard; Olsson, Tomas; Gauguier, Dominique; Hubner, Norbert; Fernandez-Teruel, Alberto; Cuppen, Edwin; Mott, Richard; Flint, Jonathan

    Genetic mapping on fully sequenced individuals is transforming understanding of the relationship between molecular variation and variation in complex traits. Here we report a combined sequence and genetic mapping analysis in outbred rats that maps 355 quantitative trait loci for 122 phenotypes. We

  6. Combining Reading Quizzes and Error Analysis to Motivate Students to Grow (United States)

    Wang, Jiawen; Selby, Karen L.


    In the spirit of scholarship in teaching and learning at the college level, we suggested and experimented with reading quizzes in combination with error analysis as one way not only to get students better prepared for class but also to provide opportunities for reflection under frameworks of mastery learning and mind growth. Our mixed-method…

  7. Genetic analysis to identify good combiners for ToLCV resistance ...

    Indian Academy of Sciences (India)


    Nov 10, 2014 ... RESEARCH ARTICLE. Genetic analysis to identify good combiners for ToLCV resistance and yield components in tomato using interspecific hybridization. RAMESH K. SINGH1,2,3, N. RAI1∗, MAJOR SINGH1, S. N. SINGH2 and K. SRIVASTAVA4. 1Crop Improvement Division, Indian Institute of Vegetable ...

  8. Urban Saturated Power Load Analysis Based on a Novel Combined Forecasting Model

    Directory of Open Access Journals (Sweden)

    Huiru Zhao


    Full Text Available Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.

  9. Combining data-driven methods with finite element analysis for flood early warning systems

    NARCIS (Netherlands)

    Pyayt, A.L.; Shevchenko, D.V.; Kozionov, A.P.; Mokhov, I.I.; Lang, B.; Krzhizhanovskaya, V.V.; Sloot, P.M.A.


    We developed a robust approach for real-time levee condition monitoring based on combination of data-driven methods (one-side classification) and finite element analysis. It was implemented within a flood early warning system and validated on a series of full-scale levee failure experiments

  10. Combining soft system methodology and pareto analysis in safety management performance assessment : an aviation case

    NARCIS (Netherlands)

    Karanikas, Nektarios


    Although reengineering is strategically advantageous for organisations in order to keep functional and sustainable, safety must remain a priority and respective efforts need to be maintained. This paper suggests the combination of soft system methodology (SSM) and Pareto analysis on the scope of

  11. Statistical assessment on a combined analysis of GRYN-ROMN-UCBN upland vegetation vital signs (United States)

    Irvine, Kathryn M.; Rodhouse, Thomas J.


    As of 2013, Rocky Mountain and Upper Columbia Basin Inventory and Monitoring Networks have multiple years of vegetation data and Greater Yellowstone Network has three years of vegetation data and monitoring is ongoing in all three networks. Our primary objective is to assess whether a combined analysis of these data aimed at exploring correlations with climate and weather data is feasible. We summarize the core survey design elements across protocols and point out the major statistical challenges for a combined analysis at present. The dissimilarity in response designs between ROMN and UCBN-GRYN network protocols presents a statistical challenge that has not been resolved yet. However, the UCBN and GRYN data are compatible as they implement a similar response design; therefore, a combined analysis is feasible and will be pursued in future. When data collected by different networks are combined, the survey design describing the merged dataset is (likely) a complex survey design. A complex survey design is the result of combining datasets from different sampling designs. A complex survey design is characterized by unequal probability sampling, varying stratification, and clustering (see Lohr 2010 Chapter 7 for general overview). Statistical analysis of complex survey data requires modifications to standard methods, one of which is to include survey design weights within a statistical model. We focus on this issue for a combined analysis of upland vegetation from these networks, leaving other topics for future research. We conduct a simulation study on the possible effects of equal versus unequal probability selection of points on parameter estimates of temporal trend using available packages within the R statistical computing package. We find that, as written, using lmer or lm for trend detection in a continuous response and clm and clmm for visually estimated cover classes with “raw” GRTS design weights specified for the weight argument leads to substantially

  12. On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Webb, M.J.; Senior, C.A.; Sexton, D.M.H.; Ingram, W.J.; Williams, K.D.; Ringer, M.A. [Hadley Centre for Climate Prediction and Research, Met Office, Exeter (United Kingdom); McAvaney, B.J.; Colman, R. [Bureau of Meteorology Research Centre (BMRC), Melbourne (Australia); Soden, B.J. [University of Miami, Rosenstiel School for Marine and Atmospheric Science, Miami, FL (United States); Gudgel, R.; Knutson, T. [Geophysical Fluid Dynamics Laboratory (GFDL), Princeton, NJ (United States); Emori, S.; Ogura, T. [National Institute for Environmental Studies (NIES), Tsukuba (Japan); Tsushima, Y. [Japan Agency for Marine-Earth Science and Technology, Frontier Research Center for Global Change (FRCGC), Kanagawa (Japan); Andronova, N. [University of Michigan, Department of Atmospheric, Oceanic and Space Sciences, Ann Arbor, MI (United States); Li, B. [University of Illinois at Urbana-Champaign (UIUC), Department of Atmospheric Sciences, Urbana, IL (United States); Musat, I.; Bony, S. [Institut Pierre Simon Laplace (IPSL), Paris (France); Taylor, K.E. [Program for Climate Model Diagnosis and Intercomparison (PCMDI), Livermore, CA (United States)


    Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO{sub 2} doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level

  13. Visualization of Time-Varying Weather Ensembles across Multiple Resolutions. (United States)

    Biswas, Ayan; Lin, Guang; Liu, Xiaotong; Shen, Han-Wei


    Uncertainty quantification in climate ensembles is an important topic for the domain scientists, especially for decision making in the real-world scenarios. With powerful computers, simulations now produce time-varying and multi-resolution ensemble data sets. It is of extreme importance to understand the model sensitivity given the input parameters such that more computation power can be allocated to the parameters with higher influence on the output. Also, when ensemble data is produced at different resolutions, understanding the accuracy of different resolutions helps the total time required to produce a desired quality solution with improved storage and computation cost. In this work, we propose to tackle these non-trivial problems on the Weather Research and Forecasting (WRF) model output. We employ a moment independent sensitivity measure to quantify and analyze parameter sensitivity across spatial regions and time domain. A comparison of clustering structures across three resolutions enables the users to investigate the sensitivity variation over the spatial regions of the five input parameters. The temporal trend in the sensitivity values is explored via an MDS view linked with a line chart for interactive brushing. The spatial and temporal views are connected to provide a full exploration system for complete spatio-temporal sensitivity analysis. To analyze the accuracy across varying resolutions, we formulate a Bayesian approach to identify which regions are better predicted at which resolutions compared to the observed precipitation. This information is aggregated over the time domain and finally encoded in an output image through a custom color map that guides the domain experts towards an adaptive grid implementation given a cost model. Users can select and further analyze the spatial and temporal error patterns for multi-resolution accuracy analysis via brushing and linking on the produced image. In this work, we collaborate with a domain expert whose

  14. The classicality and quantumness of a quantum ensemble

    Energy Technology Data Exchange (ETDEWEB)

    Zhu Xuanmin [School for Theoretical Physics and Department of Applied Physics, Hunan University, Changsha 410082 (China); Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230026 (China); Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Pang Shengshi [Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230026 (China); Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Wu Shengjun, E-mail: [Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230026 (China); Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Liu Quanhui, E-mail: qhliu@hnu.c [School for Theoretical Physics and Department of Applied Physics, Hunan University, Changsha 410082 (China)


    In this Letter, we investigate the classicality and quantumness of a quantum ensemble. We define a quantity called ensemble classicality based on classical cloning strategy (ECCC) to characterize how classical a quantum ensemble is. An ensemble of commuting states has a unit ECCC, while a general ensemble can have a ECCC less than 1. We also study how quantum an ensemble is by defining a related quantity called quantumness. We find that the classicality of an ensemble is closely related to how perfectly the ensemble can be cloned, and that the quantumness of the ensemble used in a quantum key distribution (QKD) protocol is exactly the attainable lower bound of the error rate in the sifted key. - Highlights: A quantity is defined to characterize how classical a quantum ensemble is. The classicality of an ensemble is closely related to the cloning performance. Another quantity is also defined to investigate how quantum an ensemble is. This quantity gives the lower bound of the error rate in a QKD protocol.

  15. Exploring and Listening to Chinese Classical Ensembles in General Music (United States)

    Zhang, Wenzhuo


    Music diversity is valued in theory, but the extent to which it is efficiently presented in music class remains limited. Within this article, I aim to bridge this gap by introducing four genres of Chinese classical ensembles--Qin and Xiao duets, Jiang Nan bamboo and silk ensembles, Cantonese ensembles, and contemporary Chinese orchestras--into the…

  16. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

    DEFF Research Database (Denmark)

    Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh


    Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...

  17. Ensemble hydromoeteorological forecasting in Denmark

    DEFF Research Database (Denmark)

    Lucatero Villasenor, Diana

    of the main sources of uncertainty in hydrological forecasts. This is the reason why substantiated efforts to include information from Numerical Weather Predictors (NWP) or General Circulation Models (GCM) have been made over the last couple of decades. The present thesis expects to advance the field...... forecasts only about 15% and ET0 being the lowest at 15% for some months. The lowest skill of ET0 can be attributable to the combination of both T and incoming shortwave radiation (ISWR) bias from the GCM in addition to the added uncertainty for the model of the ET0 chosen (Makkink formula). Attempts...... steps. First, GCM-based streamflow forecasts exhibit biases that increase with lead time and, although these forecasts are sharper than the ESP forecasts, these biases lead to lower accuracy relative to ESP forecasts, especially at lead times larger than two months. Corrected GCM-based streamflow...

  18. The COSMO-LEPS mesoscale ensemble system: validation of the methodology and verification

    Directory of Open Access Journals (Sweden)

    C. Marsigli


    Full Text Available The limited-area ensemble prediction system COSMO-LEPS has been running every day at ECMWF since November 2002. A number of runs of the non-hydrostatic limited-area model Lokal Modell (LM are available every day, nested on members of the ECMWF global ensemble. The limited-area ensemble forecasts range up to 120h and LM-based probabilistic products are disseminated to several national and regional weather services. Some changes of the operational suite have recently been made, on the basis of the results of a statistical analysis of the methodology. The analysis is presented in this paper, showing the benefit of increasing the number of ensemble members. The system has been designed to have a probabilistic support at the mesoscale, focusing the attention on extreme precipitation events. In this paper, the performance of COSMO-LEPS in forecasting precipitation is presented. An objective verification in terms of probabilistic indices is made, using a dense network of observations covering a part of the COSMO domain. The system is compared with ECMWF EPS, showing an improvement of the limited-area high-resolution system with respect to the global ensemble system in the forecast of high precipitation values. The impact of the use of different schemes for the parametrisation of the convection in the limited-area model is also assessed, showing that this have a minor impact with respect to run the model with different initial and boundary condition.

  19. Meta-analysis of individual and combined effects of mycotoxins on growing pigs

    Directory of Open Access Journals (Sweden)

    Ines Andretta


    Full Text Available ABSTRACT Little is known about the toxicity of concomitantly occurring mycotoxins in pig diets. This study was conducted to evaluate, through meta-analysis, the individual and the combined effects of mycotoxins on pig performance. The meta-analysis followed three sequential analyses (graphical, correlation, and variance-covariance based on a database composed of 85 published papers, 1,012 treatments and 13,196 animals. Contamination of diets with individual mycotoxins reduced (p < 0.05 feed intake by 14 % and weight gain by 17 %, while combined mycotoxins reduced the same responses by 42 % and 45 %, respectively, in comparison with the non-challenged group. The correlation (p < 0.05 between reduction in weight gain (ΔG and reduction in feed intake (ΔFI was 0.67 in individual challenges and 0.93 in combined challenges. The estimated ΔG was –6 % in individual challenges and –7 % in combined challenges when ΔFI was zero, suggesting an increase in the maintenance requirements of challenged animals. Most of ΔG (58 % in individual challenges and 84 % in combined challenges was attributed to the changes in feed efficiency. The association of mycotoxins enhances individual toxic effects and the ΔFI is important in explaining the deleterious effects on the growth of challenged pigs.

  20. Probability problems in seismic risk analysis and load combinations for nuclear power plants

    International Nuclear Information System (INIS)

    George, L.L.


    This paper describes seismic risk, load combination, and probabilistic risk problems in power plant reliability, and it suggests applications of extreme value theory. Seismic risk analysis computes the probability of power plant failure in an earthquake and the resulting risk. Components fail if their peak responses to an earthquake exceed their strengths. Dependent stochastic processes represent responses, and peak responses are maxima. A Boolean function of component failures and survivals represents plant failure. Load combinations analysis computes the cdf of the peak of the superposition of stochastic processes that represent earthquake and operating loads. It also computes the probability of pipe fracture due to crack growth, a Markov process, caused by loads. Pipe fracture is an absorbing state. Probabilistic risk analysis computes the cdf's of probabilities which represent uncertainty. These Cdf's are induced by randomizing parameters of cdf's and by randomizing properties of stochastic processes such as initial crack size distributions, marginal cdf's, and failure criteria

  1. An ensemble self-training protein interaction article classifier. (United States)

    Chen, Yifei; Hou, Ping; Manderick, Bernard


    Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.

  2. An Extended-range Hydrometeorological Ensemble Prediction System for Alpine Catchments in Switzerland (United States)

    Monhart, Samuel; Bogner, Konrad; Spirig, Christoph; Bhend, Jonas; Liniger, Mark A.; Zappa, Massimiliano; Schär, Christoph


    In recent years meteorological ensemble prediction systems have increasingly be used to feed hydrological models in order to provide probabilistic streamflow forecasts. Such hydrological ensemble prediction systems (HEPS) have been analyzed for different lead times from short-term to seasonal predictions and are used for different applications. Especially at longer lead times both such forecasts exhibit systematic biases which can be removed by applying bias correction techniques to both the meteorological and/or the hydrological output. However, it is still an open question if pre- or post-processing techniques or both should be applied. We will present first results of the analysis of pre- and post-processed extended-range hydrometeorological forecasts. In a first step the performance of bias corrected and downscaled (using quantile mapping) extended-range meteorological forecasts provided by the ECMWF is assessed for approximately 1000 ground observation sites across Europe. Generally, bias corrected meteorological forecasts show positive skill in terms of CRPSS up to three (two) weeks for weekly mean temperature (precipitation) compared to climatological forecasts. For the Alpine region the absolute skill is generally lower but the relative gain in skill resulting from the bias correction is larger. These pre-processed meteorological forecasts of one year of ECMWF extended-range forecasts and corresponding hindcasts are used to feed a hydrological model for a selected catchment in the Alpine area in Switzerland. Furthermore, different post-processing techniques are tested to correct the resulting streamflow forecasts. This will allow to determine the relative effect of pre- and post-processing of extended-range hydrometeorological predictions in Alpine catchments. Future work will include the combination of these corrected streamflow forecasts with electricity price forecasts to optimize the operations and revenues of hydropower systems in the Alps.

  3. Assessment of Surface Air Temperature over China Using Multi-criterion Model Ensemble Framework (United States)

    Li, J.; Zhu, Q.; Su, L.; He, X.; Zhang, X.


    The General Circulation Models (GCMs) are designed to simulate the present climate and project future trends. It has been noticed that the performances of GCMs are not always in agreement with each other over different regions. Model ensemble techniques have been developed to post-process the GCMs' outputs and improve their prediction reliabilities. To evaluate the performances of GCMs, root-mean-square error, correlation coefficient, and uncertainty are commonly used statistical measures. However, the simultaneous achievements of these satisfactory statistics cannot be guaranteed when using many model ensemble techniques. Meanwhile, uncertainties and future scenarios are critical for Water-Energy management and operation. In this study, a new multi-model ensemble framework was proposed. It uses a state-of-art evolutionary multi-objective optimization algorithm, termed Multi-Objective Complex Evolution Global Optimization with Principle Component Analysis and Crowding Distance (MOSPD), to derive optimal GCM ensembles and demonstrate the trade-offs among various solutions. Such trade-off information was further analyzed with a robust Pareto front with respect to different statistical measures. A case study was conducted to optimize the surface air temperature (SAT) ensemble solutions over seven geographical regions of China for the historical period (1900-2005) and future projection (2006-2100). The results showed that the ensemble solutions derived with MOSPD algorithm are superior over the simple model average and any single model output during the historical simulation period. For the future prediction, the proposed ensemble framework identified that the largest SAT change would occur in the South Central China under RCP 2.6 scenario, North Eastern China under RCP 4.5 scenario, and North Western China under RCP 8.5 scenario, while the smallest SAT change would occur in the Inner Mongolia under RCP 2.6 scenario, South Central China under RCP 4.5 scenario, and

  4. Long term Combination of Tide Gauge Benchmark Monitoring (TIGA) Analysis Center Products (United States)

    Teferle, F. N.; Hunegnaw, A.


    The International GNSS Service (IGS) Tide Gauge Benchmark Monitoring (TIGA) Working Group (WG) has recently finallized their reprocessing campaign, using all relevant Global Positioning System (GPS) observations from 1995 to 2014. This re-processed dataset will provide high quality estimates of land motions, enabling regional and global high-precision geophysical/geodeticstudies. Several of the individual TIGA Analysis Centers (TACs) have completed processing the full history of GPS observations recorded by the IGS global network, as well as, many other GPS stationsat or close to tide gauges, which are available from the TIGA data centre at the University of La Rochelle ( The TAC solutions contain a total of over 700 stations. Following the recentimprovements in processing models and strategies, this is the first complete reprocessing attempt by the TIGA WG to provide homogeneous position time series. The TIGA Combination Centre (TCC) atthe University of Luxembourg (UL) has computed a first multi-year weekly combined solution using two independent combination software packages: CATREF and GLOBK. These combinations allow anevaluation of any effects from the combination software and of the individual TAC contributions and their influences on the combined solution. In this study we will present the first UL TIGA multi-yearcombination results and discuss these in terms of geocentric sea level changes.

  5. Fock-state view of weak-value measurements and implementation with photons and atomic ensembles

    International Nuclear Information System (INIS)

    Simon, Christoph; Polzik, Eugene S.


    Weak measurements in combination with postselection can give rise to a striking amplification effect (related to a large ''weak value''). We show that this effect can be understood by viewing the initial state of the pointer as the ground state of a fictional harmonic oscillator. This perspective clarifies the relationship between the weak-value regime and other measurement techniques and inspires a proposal to implement fully quantum weak-value measurements combining photons and atomic ensembles.

  6. Glycosaminoglycan Analysis by Cryogenic Messenger-Tagging IR Spectroscopy Combined with IMS-MS. (United States)

    Khanal, Neelam; Masellis, Chiara; Kamrath, Michael Z; Clemmer, David E; Rizzo, Thomas R


    We combine ion mobility spectrometry with cryogenic, messenger-tagging, infrared spectroscopy and mass spectrometry to identify different isomeric disaccharides of chondroitin sulfate (CS) and heparan sulfate (HS), which are representatives of two major subclasses of glycosaminoglycans. Our analysis shows that while CS and HS disaccharide isomers have similar drift times, they can be uniquely distinguished by their vibrational spectrum between ∼3200 and 3700 cm -1 due to their different OH hydrogen-bonding patterns. We suggest that this combination of techniques is well suited to identify and characterize glycan isomers directly, which presents tremendous challenges for existing methods.

  7. Analysis of simultaneous measurement of temperature and strain using different combinations of FBG (United States)

    Ashik T., J.; Kachare, Nitin; Kalyani bai, K.; Kumar, D. Sriram


    The Fiber Bragg Grating (FBG) can be used for measuring temperature and or strain. In this paper analysis of different combinations of FBG is made. Certain parameters of FBG are considered such as Bandwidth, Side lobes, Peak power, and Sensitivity. Simultaneous measurement of temperature and strain is made using two combinations of FBG. The setup is simulated using two software. Optigrating 4.2.2 is used for designing different types of gratings such as Uniform, Apodized, Tilted and Superstructure. After designing, these files are exported to Optisystem 12 to simulate the spectrum and to observe the parameters.

  8. Improving Climate Projections Using "Intelligent" Ensembles (United States)

    Baker, Noel C.; Taylor, Patrick C.


    Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and

  9. Demonstrating the value of larger ensembles in forecasting physical systems

    Directory of Open Access Journals (Sweden)

    Reason L. Machete


    Full Text Available Ensemble simulation propagates a collection of initial states forward in time in a Monte Carlo fashion. Depending on the fidelity of the model and the properties of the initial ensemble, the goal of ensemble simulation can range from merely quantifying variations in the sensitivity of the model all the way to providing actionable probability forecasts of the future. Whatever the goal is, success depends on the properties of the ensemble, and there is a longstanding discussion in meteorology as to the size of initial condition ensemble most appropriate for Numerical Weather Prediction. In terms of resource allocation: how is one to divide finite computing resources between model complexity, ensemble size, data assimilation and other components of the forecast system. One wishes to avoid undersampling information available from the model's dynamics, yet one also wishes to use the highest fidelity model available. Arguably, a higher fidelity model can better exploit a larger ensemble; nevertheless it is often suggested that a relatively small ensemble, say ~16 members, is sufficient and that larger ensembles are not an effective investment of resources. This claim is shown to be dubious when the goal is probabilistic forecasting, even in settings where the forecast model is informative but imperfect. Probability forecasts for a ‘simple’ physical system are evaluated at different lead times; ensembles of up to 256 members are considered. The pure density estimation context (where ensemble members are drawn from the same underlying distribution as the target differs from the forecasting context, where one is given a high fidelity (but imperfect model. In the forecasting context, the information provided by additional members depends also on the fidelity of the model, the ensemble formation scheme (data assimilation, the ensemble interpretation and the nature of the observational noise. The effect of increasing the ensemble size is quantified by

  10. Concept of Combining Cost-Effectiveness Analysis and Budget Impact Analysis in Health Care Decision-Making. (United States)

    Yagudina, Roza Ismailovna; Kulikov, Andrey Urievich; Serpik, Vjacheslav Gennadievich; Ugrekhelidze, Dzhumber Tengizovich


    The objective of this study is to cover the ways of solving the problem of understanding the results of two key methods of pharmacoeconomic analysis - budget impact and cost-effectiveness. It is important to note that pharmacoeconomic assessment based on this evidence often has controversial character. The results of one type of analysis can characterize assessed health technology favorably, and the results of other critically. Pharmacoeconomic evidence is often a crucial part of decision-making in healthcare, that's why clear understanding of combination of this two types of analysis is highly in demand. Authors propose methodological solution of the stated problem. This model is a useful tool in making unified pharmacoeconomic report based on cost-effectiveness analysis and budget impact analysis results. Use of this model preserves the meaning and significance of each type of pharmacoeconomic analysis. Three-dimensional pharmacoeconomic model proposes full account of both types of pharmacoeconomic analyses during conclusion preparation, the formation of a single consistent pharmacoeconomic conclusion. Though further validation of a tool is needed, presented model can be interesting for the professional community. The proposed model of combining budget impact and cost-effectiveness analysis can be used by healthcare decision-makers for obtaining reliable and transparent pharmacoeconomic data. Copyright © 2017. Published by Elsevier Inc.

  11. Automated ensemble assembly and validation of microbial genomes (United States)


    Background The continued democratization of DNA sequencing has sparked a new wave of development of genome assembly and assembly validation methods. As individual research labs, rather than centralized centers, begin to sequence the majority of new genomes, it is important to establish best practices for genome assembly. However, recent evaluations such as GAGE and the Assemblathon have concluded that there is no single best approach to genome assembly. Instead, it is preferable to generate multiple assemblies and validate them to determine which is most useful for the desired analysis; this is a labor-intensive process that is often impossible or unfeasible. Results To encourage best practices supported by the community, we present iMetAMOS, an automated ensemble assembly pipeline; iMetAMOS encapsulates the process of running, validating, and selecting a single assembly from multiple assemblies. iMetAMOS packages several leading open-source tools into a single binary that automates parameter selection and execution of multiple assemblers, scores the resulting assemblies based on multiple validation metrics, and annotates the assemblies for genes and contaminants. We demonstrate the utility of the ensemble process on 225 previously unassembled Mycobacterium tuberculosis genomes as well as a Rhodobacter sphaeroides benchmark dataset. On these real data, iMetAMOS reliably produces validated assemblies and identifies potential contamination without user intervention. In addition, intelligent parameter selection produces assemblies of R. sphaeroides comparable to or exceeding the quality of those from the GAGE-B evaluation, affecting the relative ranking of some assemblers. Conclusions Ensemble assembly with iMetAMOS provides users with multiple, validated assemblies for each genome. Although computationally limited to small or mid-sized genomes, this approach is the most effective and reproducible means for generating high-quality assemblies and enables users to

  12. Attributing varying ENSO amplitudes in climate model ensembles (United States)

    Watanabe, M.; Kug, J.-S.; Jin, F.-F.; Collins, M.; Ohba, M.; Wittenberg, A.


    Realistic simulation of the El Niño-Southern Oscillation (ENSO) phenomenon, which has a great impact on the global weather and climate, is of primary importance in the coupled atmosphere-ocean modeling. Nevertheless, the ENSO amplitude is known to vary considerably in a multi-model ensemble (MME) archived in the coupled model inter-comparison project phase 3 (CMIP3). Given a large uncertainty in the atmospheric processes having a substantial influence to the models' ENSO intensity, we constructed physics parameter ensembles (PPEs) based on four climate models (two of them are included in the CMIP5 archive) in which parameters in the atmospheric parameterization schemes have been perturbed. Analysis to the 33-member PPEs reveals a positive relationship between the ENSO amplitude and the mean precipitation over the eastern equatorial Pacific in each model. This relationship is explained by the mean state difference controling the ENSO activity but not by the ENSO rectification of the mean state. The wetter mean state in the eastern equatorial Pacific favors an eastward shift in the equatorial zonal wind stress response to El Niño/La Niña, which acts to increase the ENSO amplitude due to enhanced coupled instability. Such a relationship, however, cannot be seen in both CMIP3 and CMIP5 MMEs, indicating that the above mechanism does not explain the diversity in ENSO amplitude across the models. Yet, ensemble historical runs available for some of the CMIP5 models show the positive relationship between the ENSO amplitude and the mean precipitation, providing a useful insight into the ENSO changes under the global warming in individual models.

  13. Application of photonuclear activation-measuring combined installations for analysis of mineral raw material

    International Nuclear Information System (INIS)

    Burmistenko, Yu.N.


    The problems of application of photonuclear activation- measuring combined installations for the analysis of rocks and ores are considered. As applied to analytical problems of mining and dressing works realized is to the greatest extent one of the most important advantages of photonuclear methods of analysis (PhMA)-rapidity due to which high economic benefit is reached. PhMA rapidity and possibility of the analysis of great mass samples ensure the possibility of creation of new installation on principle for activation-radiometric sorting of ores loaded into dump trucks of mass up to 40 t. For solution of analytical problems in geological prospecting it is advisable to use the photonuclear installation of multielement analysis. At the analysis in the course of prospecting and exploration for mineral resources all PhMA advantages are used: high selectivity, representativeness, productivity and rapidity

  14. Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble

    Directory of Open Access Journals (Sweden)

    Rong Shan


    Full Text Available Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their cross effects in biological systems. In this paper, the Markov property and Neural Network Ensemble (NNE are utilized to construct an estimated matrix that combines the interaction of the different local factors. With such an estimation matrix, we could obtain estimated variables that could reflect the global influence. The ensemble weights are trained by multiple population algorithms. Our prediction could fit the real trend of the two predicted measures, namely Morbidity Rate and Gross Domestic Product (GDP. It could be an effective method of reflecting the relationship between input factors and predicted measures of the health of ecosystems. The method can perform a sensitivity analysis, which could help determine the critical factors that could be adjusted to move the ecosystem in a sustainable direction.

  15. Bioconductor's EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis. (United States)

    Geistlinger, Ludwig; Csaba, Gergely; Zimmer, Ralf


    Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Numerous methods have been published that either ignore (set-based) or incorporate (network-based) known interactions between genes. However, the often subtle benefits and disadvantages of the individual methods are confusing for most biological end users and there is currently no convenient way to combine methods for an enhanced result interpretation. We present the EnrichmentBrowser package as an easily applicable software that enables (1) the application of the most frequently used set-based and network-based enrichment methods, (2) their straightforward combination, and (3) a detailed and interactive visualization and exploration of the results. The package is available from the Bioconductor repository and implements additional support for standardized expression data p