WorldWideScience

Sample records for dynamic data-driven event

  1. Dynamic Data-Driven Event Reconstruction for Atmospheric Releases

    Energy Technology Data Exchange (ETDEWEB)

    Kosovic, B; Belles, R; Chow, F K; Monache, L D; Dyer, K; Glascoe, L; Hanley, W; Johannesson, G; Larsen, S; Loosmore, G; Lundquist, J K; Mirin, A; Neuman, S; Nitao, J; Serban, R; Sugiyama, G; Aines, R

    2007-02-22

    Accidental or terrorist releases of hazardous materials into the atmosphere can impact large populations and cause significant loss of life or property damage. Plume predictions have been shown to be extremely valuable in guiding an effective and timely response. The two greatest sources of uncertainty in the prediction of the consequences of hazardous atmospheric releases result from poorly characterized source terms and lack of knowledge about the state of the atmosphere as reflected in the available meteorological data. In this report, we discuss the development of a new event reconstruction methodology that provides probabilistic source term estimates from field measurement data for both accidental and clandestine releases. Accurate plume dispersion prediction requires the following questions to be answered: What was released? When was it released? How much material was released? Where was it released? We have developed a dynamic data-driven event reconstruction capability which couples data and predictive models through Bayesian inference to obtain a solution to this inverse problem. The solution consists of a probability distribution of unknown source term parameters. For consequence assessment, we then use this probability distribution to construct a ''''composite'' forward plume prediction which accounts for the uncertainties in the source term. Since in most cases of practical significance it is impossible to find a closed form solution, Bayesian inference is accomplished by utilizing stochastic sampling methods. This approach takes into consideration both measurement and forward model errors and thus incorporates all the sources of uncertainty in the solution to the inverse problem. Stochastic sampling methods have the additional advantage of being suitable for problems characterized by a non-Gaussian distribution of source term parameters and for cases in which the underlying dynamical system is non-linear. We initially

  2. Dynamic Data Driven Applications Systems (DDDAS)

    Science.gov (United States)

    2012-05-03

    ultrasonic sensor arrays and infrared thermographic imaging and a full-scale wind turbine blade with in-build structural defects  ability to dynamically...Multiphase Flow Weather and Climate Structural Mechanics Seismic Processing Aerodynamics Geophysical Fluids Quantum Chemistry Actinide Chemistry

  3. Dynamic Data-Driven UAV Network for Plume Characterization

    Science.gov (United States)

    2016-05-23

    management and response. Data driven operation of a mobile sensor network enables asset allocation to regions with highest impact on the mission success. We...operation of a mobile sensor network enables asset allocation to regions with highest impact on the mission success. We studied a dynamic data driven...investigated a two-dimensional Gaussian puff evolving within a uniform background flow. The standard Kalman filter handles the data assimila- tion; an SPH

  4. Dynamic Data Driven Methods for Self-aware Aerospace Vehicles

    Science.gov (United States)

    2015-04-08

    AFRL-OSR-VA-TR-2015-0127 Dynamic Data Driven Methods for Self-aware Aerospace Vehicles Karen Willcox MASSACHUSETTS INSTITUTE OF TECHNOLOGY Final...Methods for Self-aware Aerospace Vehicles 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-11-1-0339 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Karen E...Back (Rev. 8/98) Dynamic Data Driven Methods for Self-aware Aerospace Vehicles Grant # FA9550-11-1-0339 Final Report Participating Institutions

  5. A data driven nonlinear stochastic model for blood glucose dynamics.

    Science.gov (United States)

    Zhang, Yan; Holt, Tim A; Khovanova, Natalia

    2016-03-01

    The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles.

  6. Using Shape Memory Alloys: A Dynamic Data Driven Approach

    KAUST Repository

    Douglas, Craig C.

    2013-06-01

    Shape Memory Alloys (SMAs) are capable of changing their crystallographic structure due to changes of either stress or temperature. SMAs are used in a number of aerospace devices and are required in some devices in exotic environments. We are developing dynamic data driven application system (DDDAS) tools to monitor and change SMAs in real time for delivering payloads by aerospace vehicles. We must be able to turn on and off the sensors and heating units, change the stress on the SMA, monitor on-line data streams, change scales based on incoming data, and control what type of data is generated. The application must have the capability to be run and steered remotely as an unmanned feedback control loop.

  7. Econophysics and Data Driven Modelling of Market Dynamics

    CERN Document Server

    Aoyama, Hideaki; Chakrabarti, Bikas; Chakraborti, Anirban; Ghosh, Asim; Econophysics and Data Driven Modelling of Market Dynamics

    2015-01-01

    This book presents the works and research findings of physicists, economists, mathematicians, statisticians, and financial engineers who have undertaken data-driven modelling of market dynamics and other empirical studies in the field of Econophysics. During recent decades, the financial market landscape has changed dramatically with the deregulation of markets and the growing complexity of products. The ever-increasing speed and decreasing costs of computational power and networks have led to the emergence of huge databases. The availability of these data should permit the development of models that are better founded empirically, and econophysicists have accordingly been advocating that one should rely primarily on the empirical observations in order to construct models and validate them. The recent turmoil in financial markets and the 2008 crash appear to offer a strong rationale for new models and approaches. The Econophysics community accordingly has an important future role to play in market modelling....

  8. Data-driven optimization of dynamic reconfigurable systems of systems.

    Energy Technology Data Exchange (ETDEWEB)

    Tucker, Conrad S.; Eddy, John P.

    2010-11-01

    This report documents the results of a Strategic Partnership (aka University Collaboration) LDRD program between Sandia National Laboratories and the University of Illinois at Urbana-Champagne. The project is titled 'Data-Driven Optimization of Dynamic Reconfigurable Systems of Systems' and was conducted during FY 2009 and FY 2010. The purpose of this study was to determine and implement ways to incorporate real-time data mining and information discovery into existing Systems of Systems (SoS) modeling capabilities. Current SoS modeling is typically conducted in an iterative manner in which replications are carried out in order to quantify variation in the simulation results. The expense of many replications for large simulations, especially when considering the need for optimization, sensitivity analysis, and uncertainty quantification, can be prohibitive. In addition, extracting useful information from the resulting large datasets is a challenging task. This work demonstrates methods of identifying trends and other forms of information in datasets that can be used on a wide range of applications such as quantifying the strength of various inputs on outputs, identifying the sources of variation in the simulation, and potentially steering an optimization process for improved efficiency.

  9. Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution

    Science.gov (United States)

    Ren, Silin; Jin, Xiao; Chan, Chung; Jian, Yiqiang; Mulnix, Tim; Liu, Chi; E Carson, Richard

    2017-06-01

    Data-driven respiratory gating techniques were developed to correct for respiratory motion in PET studies, without the help of external motion tracking systems. Due to the greatly increased image noise in gated reconstructions, it is desirable to develop a data-driven event-by-event respiratory motion correction method. In this study, using the Centroid-of-distribution (COD) algorithm, we established a data-driven event-by-event respiratory motion correction technique using TOF PET list-mode data, and investigated its performance by comparing with an external system-based correction method. Ten human scans with the pancreatic β-cell tracer 18F-FP-(+)-DTBZ were employed. Data-driven respiratory motions in superior-inferior (SI) and anterior-posterior (AP) directions were first determined by computing the centroid of all radioactive events during each short time frame with further processing. The Anzai belt system was employed to record respiratory motion in all studies. COD traces in both SI and AP directions were first compared with Anzai traces by computing the Pearson correlation coefficients. Then, respiratory gated reconstructions based on either COD or Anzai traces were performed to evaluate their relative performance in capturing respiratory motion. Finally, based on correlations of displacements of organ locations in all directions and COD information, continuous 3D internal organ motion in SI and AP directions was calculated based on COD traces to guide event-by-event respiratory motion correction in the MOLAR reconstruction framework. Continuous respiratory correction results based on COD were compared with that based on Anzai, and without motion correction. Data-driven COD traces showed a good correlation with Anzai in both SI and AP directions for the majority of studies, with correlation coefficients ranging from 63% to 89%. Based on the determined respiratory displacements of pancreas between end-expiration and end-inspiration from gated

  10. Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution.

    Science.gov (United States)

    Ren, Silin; Jin, Xiao; Chan, Chung; Jian, Yiqiang; Mulnix, Tim; Liu, Chi; Carson, Richard E

    2017-06-21

    Data-driven respiratory gating techniques were developed to correct for respiratory motion in PET studies, without the help of external motion tracking systems. Due to the greatly increased image noise in gated reconstructions, it is desirable to develop a data-driven event-by-event respiratory motion correction method. In this study, using the Centroid-of-distribution (COD) algorithm, we established a data-driven event-by-event respiratory motion correction technique using TOF PET list-mode data, and investigated its performance by comparing with an external system-based correction method. Ten human scans with the pancreatic β-cell tracer (18)F-FP-(+)-DTBZ were employed. Data-driven respiratory motions in superior-inferior (SI) and anterior-posterior (AP) directions were first determined by computing the centroid of all radioactive events during each short time frame with further processing. The Anzai belt system was employed to record respiratory motion in all studies. COD traces in both SI and AP directions were first compared with Anzai traces by computing the Pearson correlation coefficients. Then, respiratory gated reconstructions based on either COD or Anzai traces were performed to evaluate their relative performance in capturing respiratory motion. Finally, based on correlations of displacements of organ locations in all directions and COD information, continuous 3D internal organ motion in SI and AP directions was calculated based on COD traces to guide event-by-event respiratory motion correction in the MOLAR reconstruction framework. Continuous respiratory correction results based on COD were compared with that based on Anzai, and without motion correction. Data-driven COD traces showed a good correlation with Anzai in both SI and AP directions for the majority of studies, with correlation coefficients ranging from 63% to 89%. Based on the determined respiratory displacements of pancreas between end-expiration and end-inspiration from gated

  11. A Data Driven Framework for Real Time Power System Event Detection and Visualization

    CERN Document Server

    McCamish, Ben; Landford, Jordan; Bass, Robert; Cotilla-Sanchez, Eduardo; Chiu, David

    2015-01-01

    Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide real-time insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system in real-time. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With th...

  12. Dynamic and data-driven classification for polarimetric SAR images

    Science.gov (United States)

    Uhlmann, S.; Kiranyaz, S.; Ince, T.; Gabbouj, M.

    2011-11-01

    In this paper, we introduce dynamic and scalable Synthetic Aperture Radar (SAR) terrain classification based on the Collective Network of Binary Classifiers (CNBC). The CNBC framework is primarily adapted to maximize the SAR classification accuracy on dynamically varying databases where variations do occur in any time in terms of (new) images, classes, features and users' relevance feedback. Whenever a "change" occurs, the CNBC dynamically and "optimally" adapts itself to the change by means of its topology and the underlying evolutionary method MD PSO. Thanks to its "Divide and Conquer" type approach, the CNBC can also support varying and large set of (PolSAR) features among which it optimally selects, weighs and fuses the most discriminative ones for a particular class. Each SAR terrain class is discriminated by a dedicated Network of Binary Classifiers (NBC), which encapsulates a set of evolutionary Binary Classifiers (BCs) discriminating the class with a distinctive feature set. Moreover, with each incremental evolution session, new classes/features can be introduced which signals the CNBC to create new corresponding NBCs and BCs within to adapt and scale dynamically to the change. This can in turn be a significant advantage when the current CNBC is used to classify multiple SAR images with similar terrain classes since no or only minimal (incremental) evolution sessions are needed to adapt it to a new classification problem while using the previously acquired knowledge. We demonstrate our proposed classification approach over several medium and highresolution NASA/JPL AIRSAR images applying various polarimetric decompositions. We evaluate and compare the computational complexity and classification accuracy against static Neural Network classifiers. As CNBC classification accuracy can compete and even surpass them, the computational complexity of CNBC is significantly lower as the CNBC body supports high parallelization making it applicable to grid

  13. WIFIRE: A Scalable Data-Driven Monitoring, Dynamic Prediction and Resilience Cyberinfrastructure for Wildfires

    Science.gov (United States)

    Altintas, I.; Block, J.; Braun, H.; de Callafon, R. A.; Gollner, M. J.; Smarr, L.; Trouve, A.

    2013-12-01

    Recent studies confirm that climate change will cause wildfires to increase in frequency and severity in the coming decades especially for California and in much of the North American West. The most critical sustainability issue in the midst of these ever-changing dynamics is how to achieve a new social-ecological equilibrium of this fire ecology. Wildfire wind speeds and directions change in an instant, and first responders can only be effective when they take action as quickly as the conditions change. To deliver information needed for sustainable policy and management in this dynamically changing fire regime, we must capture these details to understand the environmental processes. We are building an end-to-end cyberinfrastructure (CI), called WIFIRE, for real-time and data-driven simulation, prediction and visualization of wildfire behavior. The WIFIRE integrated CI system supports social-ecological resilience to the changing fire ecology regime in the face of urban dynamics and climate change. Networked observations, e.g., heterogeneous satellite data and real-time remote sensor data is integrated with computational techniques in signal processing, visualization, modeling and data assimilation to provide a scalable, technological, and educational solution to monitor weather patterns to predict a wildfire's Rate of Spread. Our collaborative WIFIRE team of scientists, engineers, technologists, government policy managers, private industry, and firefighters architects implement CI pathways that enable joint innovation for wildfire management. Scientific workflows are used as an integrative distributed programming model and simplify the implementation of engineering modules for data-driven simulation, prediction and visualization while allowing integration with large-scale computing facilities. WIFIRE will be scalable to users with different skill-levels via specialized web interfaces and user-specified alerts for environmental events broadcasted to receivers before

  14. Dynamic Data Driven Applications Systems (DDDAS) modeling for automatic target recognition

    Science.gov (United States)

    Blasch, Erik; Seetharaman, Guna; Darema, Frederica

    2013-05-01

    The Dynamic Data Driven Applications System (DDDAS) concept uses applications modeling, mathematical algorithms, and measurement systems to work with dynamic systems. A dynamic systems such as Automatic Target Recognition (ATR) is subject to sensor, target, and the environment variations over space and time. We use the DDDAS concept to develop an ATR methodology for multiscale-multimodal analysis that seeks to integrated sensing, processing, and exploitation. In the analysis, we use computer vision techniques to explore the capabilities and analogies that DDDAS has with information fusion. The key attribute of coordination is the use of sensor management as a data driven techniques to improve performance. In addition, DDDAS supports the need for modeling from which uncertainty and variations are used within the dynamic models for advanced performance. As an example, we use a Wide-Area Motion Imagery (WAMI) application to draw parallels and contrasts between ATR and DDDAS systems that warrants an integrated perspective. This elementary work is aimed at triggering a sequence of deeper insightful research towards exploiting sparsely sampled piecewise dense WAMI measurements - an application where the challenges of big-data with regards to mathematical fusion relationships and high-performance computations remain significant and will persist. Dynamic data-driven adaptive computations are required to effectively handle the challenges with exponentially increasing data volume for advanced information fusion systems solutions such as simultaneous target tracking and ATR.

  15. Data-driven prediction and prevention of extreme events in a spatially extended excitable system.

    Science.gov (United States)

    Bialonski, Stephan; Ansmann, Gerrit; Kantz, Holger

    2015-10-01

    Extreme events occur in many spatially extended dynamical systems, often devastatingly affecting human life, which makes their reliable prediction and efficient prevention highly desirable. We study the prediction and prevention of extreme events in a spatially extended system, a system of coupled FitzHugh-Nagumo units, in which extreme events occur in a spatially and temporally irregular way. Mimicking typical constraints faced in field studies, we assume not to know the governing equations of motion and to be able to observe only a subset of all phase-space variables for a limited period of time. Based on reconstructing the local dynamics from data and despite being challenged by the rareness of events, we are able to predict extreme events remarkably well. With small, rare, and spatiotemporally localized perturbations which are guided by our predictions, we are able to completely suppress extreme events in this system.

  16. Data driven analysis of rain events: feature extraction, clustering, microphysical /macro physical relationship

    Science.gov (United States)

    Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

    2017-04-01

    that a rain time series can be considered by an alternation of independent rain event and no rain period. The five selected feature are used to perform a hierarchical clustering of the events. The well-known division between stratiform and convective events appears clearly. This classification into two classes is then refined in 5 fairly homogeneous subclasses. The data driven analysis performed on whole rain events instead of fixed length samples allows identifying strong relationships between macrophysics (based on rain rate) and microphysics (based on raindrops) features. We show that among the 5 identified subclasses some of them have specific microphysics characteristics. Obtaining information on microphysical characteristics of rainfall events from rain gauges measurement suggests many implications in development of the quantitative precipitation estimation (QPE), for the improvement of rain rate retrieval algorithm in remote sensing context.

  17. Loss Modeling with a Data-Driven Approach in Event-Based Rainfall-Runoff Analysis

    Science.gov (United States)

    Chua, L. H. C.

    2012-04-01

    Mathematical models require the estimation of rainfall abstractions for accurate predictions of runoff. Although loss models such as the constant loss and exponential loss models are commonly used, these methods are based on simplified assumptions of the physical process. A new approach based on the data driven paradigm to estimate rainfall abstractions is proposed in this paper. The proposed data driven model, based on the artificial neural network (ANN) does not make any assumptions on the loss behavior. The estimated discharge from a physically-based model, obtained from the kinematic wave (KW) model assuming zero losses, was used as the only input to the ANN. The output is the measured discharge. Thus, the ANN functions as a black-box loss model. Two sets of data were analyzed for this study. The first dataset consists of rainfall and runoff data, measured from an artificial catchment (area = 25 m2) comprising two overland planes (slope = 11%), 25m long, transversely inclined towards a rectangular channel (slope = 2%) which conveyed the flow, recorded using calibrated weigh tanks, to the outlet. Two rain gauges, each placed 6.25 m from either ends of the channel, were used to record rainfall. Data for six storm events over the period between October 2002 and December 2002 were analyzed. The second dataset was obtained from the Upper Bukit Timah catchment (area = 6.4 km2) instrumented with two rain gauges and a flow measuring station. A total of six events recorded between November 1987 and July 1988 were selected for this study. The runoff predicted by the ANN was compared with the measured runoff. In addition, results from KW models developed for both the catchments were used as a benchmark. The KW models were calibrated assuming the loss rate for an average event for each of the datasets. The results from both the ANN and KW models agreed well with the runoff measured from the artificial catchment. The KW model is expected to perform well since the catchment

  18. An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development

    KAUST Repository

    Douglas, Craig C.

    2014-06-06

    In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. A DDDAS is an application that has data assimilation that can change the models and/or scales of the computation and that the application controls the data collection based on the computational results. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

  19. Data Driven Modelling of the Dynamic Wake Between Two Wind Turbines

    DEFF Research Database (Denmark)

    Knudsen, Torben; Bak, Thomas

    2012-01-01

    . This paper is the first where modern commercial mega watt turbines are used for data driven modelling including the upwind turbine loading by changing power reference. Obtaining the necessary data is difficult and data is therefore limited. A simple dynamic extension to the Jensen wake model is tested...... without much success. The best model turns out to be non linear with upwind turbine loading and wind speed as inputs. Using a transformation of these inputs it is possible to obtain a linear model and use well proven system identification methods. Finally it is shown that including the upwind wind...... turbine. This paper establishes flow models relating the wind speeds at turbines in a farm. So far, research in this area has been mainly based on first principles static models and the data driven modelling done has not included the loading of the upwind turbine and its impact on the wind speed downwind...

  20. Prop erties and Data-driven Design of Perceptual Reasoning Metho d Based Linguistic Dynamic Systems

    Institute of Scientific and Technical Information of China (English)

    LI Cheng-Dong; ZHANG Gui-Qing; WANG Hui-Dong; REN Wei-Na

    2014-01-01

    The linguistic dynamic systems (LDSs) based on type-1 fuzzy sets can provide a powerful tool for modeling, analysis, evaluation and control of complex systems. However, as pointed out in earlier studies, it is much more reasonable to take type-2 fuzzy sets to model the existing uncertainties of linguistic words. In this paper, the LDS based on type-2 fuzzy sets is studied, and its reasoning process is realized through the perceptual reasoning method. The properties of the perceptual reasoning method based LDS (PR-LDS) are explored. These properties demonstrated that the output of PR-LDS is intuitive and the computation complexity can be reduced when the consequent type-2 fuzzy numbers in the rule base satisfy some conditions. Further, a data driven method for the design of the PR-LDS is provided. At last, the effectiveness and rationality of the proposed data-driven method are verified by an example.

  1. Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

    Directory of Open Access Journals (Sweden)

    Huan-Kai Peng

    Full Text Available In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.

  2. Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

    Science.gov (United States)

    Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

    2016-01-01

    In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.

  3. A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea

    Directory of Open Access Journals (Sweden)

    Jining Yan

    2015-05-01

    Full Text Available In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. Based on the DDDAS, combing a remote sensor system that detects oil spills with a numerical simulation, an integrated data processing, analysis, forecasting and emergency response system was established. Once an oil spill accident occurs, the DDDAS-based oil spill model receives information about the oil slick extracted from the dynamic remote sensor data in the simulation. Through comparison, information fusion and feedback updates, continuous and more precise oil spill simulation results can be obtained. Then, the simulation results can provide help for disaster control and clean-up. The Penglai, Xingang and Suizhong oil spill results showed our simulation model could increase the prediction accuracy and reduce the error caused by empirical parameters in existing simulation systems. Therefore, the DDDAS-based detection and simulation system can effectively improve oil spill simulation and diffusion forecasting, as well as provide decision-making information and technical support for emergency responses to oil spills.

  4. Dynamic model reduction using data-driven Loewner-framework applied to thermally morphing structures

    Science.gov (United States)

    Phoenix, Austin A.; Tarazaga, Pablo A.

    2017-05-01

    The work herein proposes the use of the data-driven Loewner-framework for reduced order modeling as applied to dynamic Finite Element Models (FEM) of thermally morphing structures. The Loewner-based modeling approach is computationally efficient and accurately constructs reduced models using analytical output data from a FEM. This paper details the two-step process proposed in the Loewner approach. First, a random vibration FEM simulation is used as the input for the development of a Single Input Single Output (SISO) data-based dynamic Loewner state space model. Second, an SVD-based truncation is used on the Loewner state space model, such that the minimal, dynamically representative, state space model is achieved. For this second part, varying levels of reduction are generated and compared. The work herein can be extended to model generation using experimental measurements by replacing the FEM output data in the first step and following the same procedure. This method will be demonstrated on two thermally morphing structures, a rigidly fixed hexapod in multiple geometric configurations and a low mass anisotropic morphing boom. This paper is working to detail the method and identify the benefits of the reduced model methodology.

  5. Data driven clustering of rain events: microphysics information derived from macro scale observations

    OpenAIRE

    Dilmi, Mohamed Djallel; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

    2016-01-01

    The study of rain time series records is mainly carried out using rainfall rate or rain accumulation parameters estimated on a fixed duration (typically 1 min, 1 hour or 1 day). In this paper we used the concept of rain event. Among the numerous existing variables dedicated to the characterisation of rain events, the first part of this paper aims to obtain a parsimonious characterisation of these events using a minimal set of variables. In this context an algorithm based on Genetic Algorithm ...

  6. Probing the dynamics of identified neurons with a data-driven modeling approach.

    Directory of Open Access Journals (Sweden)

    Thomas Nowotny

    Full Text Available In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach.

  7. Simulating Flying Insects Using Dynamics and Data-Driven Noise Modeling to Generate Diverse Collective Behaviors.

    Science.gov (United States)

    Ren, Jiaping; Wang, Xinjie; Jin, Xiaogang; Manocha, Dinesh

    2016-01-01

    We present a biologically plausible dynamics model to simulate swarms of flying insects. Our formulation, which is based on biological conclusions and experimental observations, is designed to simulate large insect swarms of varying densities. We use a force-based model that captures different interactions between the insects and the environment and computes collision-free trajectories for each individual insect. Furthermore, we model the noise as a constructive force at the collective level and present a technique to generate noise-induced insect movements in a large swarm that are similar to those observed in real-world trajectories. We use a data-driven formulation that is based on pre-recorded insect trajectories. We also present a novel evaluation metric and a statistical validation approach that takes into account various characteristics of insect motions. In practice, the combination of Curl noise function with our dynamics model is used to generate realistic swarm simulations and emergent behaviors. We highlight its performance for simulating large flying swarms of midges, fruit fly, locusts and moths and demonstrate many collective behaviors, including aggregation, migration, phase transition, and escape responses.

  8. Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

    CERN Document Server

    Abler, Daniel; Davies, Jim; Dosanjh, Manjit; Jena, Raj; Kirkby, Norman; Peach, Ken

    2013-01-01

    Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of ‘general Markov models’, providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy ...

  9. Data-driven diagnostics of terrestrial carbon dynamics over North America

    Science.gov (United States)

    Jingfeng Xiao; Scott V. Ollinger; Steve Frolking; George C. Hurtt; David Y. Hollinger; Kenneth J. Davis; Yude Pan; Xiaoyang Zhang; Feng Deng; Jiquan Chen; Dennis D. Baldocchi; Bevery E. Law; M. Altaf Arain; Ankur R. Desai; Andrew D. Richardson; Ge Sun; Brian Amiro; Hank Margolis; Lianhong Gu; Russell L. Scott; Peter D. Blanken; Andrew E. Suyker

    2014-01-01

    The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale...

  10. Data Driven Approach for High Resolution Population Distribution and Dynamics Models

    Energy Technology Data Exchange (ETDEWEB)

    Bhaduri, Budhendra L [ORNL; Bright, Eddie A [ORNL; Rose, Amy N [ORNL; Liu, Cheng [ORNL; Urban, Marie L [ORNL; Stewart, Robert N [ORNL

    2014-01-01

    High resolution population distribution data are vital for successfully addressing critical issues ranging from energy and socio-environmental research to public health to human security. Commonly available population data from Census is constrained both in space and time and does not capture population dynamics as functions of space and time. This imposes a significant limitation on the fidelity of event-based simulation models with sensitive space-time resolution. This paper describes ongoing development of high-resolution population distribution and dynamics models, at Oak Ridge National Laboratory, through spatial data integration and modeling with behavioral or activity-based mobility datasets for representing temporal dynamics of population. The model is resolved at 1 km resolution globally and describes the U.S. population for nighttime and daytime at 90m. Integration of such population data provides the opportunity to develop simulations and applications in critical infrastructure management from local to global scales.

  11. FireMap: A Web Tool for Dynamic Data-Driven Predictive Wildfire Modeling Powered by the WIFIRE Cyberinfrastructure

    Science.gov (United States)

    Block, J.; Crawl, D.; Artes, T.; Cowart, C.; de Callafon, R.; DeFanti, T.; Graham, J.; Smarr, L.; Srivas, T.; Altintas, I.

    2016-12-01

    The NSF-funded WIFIRE project has designed a web-based wildfire modeling simulation and visualization tool called FireMap. The tool executes FARSITE to model fire propagation using dynamic weather and fire data, configuration settings provided by the user, and static topography and fuel datasets already built-in. Using GIS capabilities combined with scalable big data integration and processing, FireMap enables simple execution of the model with options for running ensembles by taking the information uncertainty into account. The results are easily viewable, sharable, repeatable, and can be animated as a time series. From these capabilities, users can model real-time fire behavior, analyze what-if scenarios, and keep a history of model runs over time for sharing with collaborators. Firemap runs FARSITE with national and local sensor networks for real-time weather data ingestion and High-Resolution Rapid Refresh (HRRR) weather for forecasted weather. The HRRR is a NOAA/NCEP operational weather prediction system comprised of a numerical forecast model and an analysis/assimilation system to initialize the model. It is run with a horizontal resolution of 3 km, has 50 vertical levels, and has a temporal resolution of 15 minutes. The HRRR requires an Environmental Data Exchange (EDEX) server to receive the feed and generate secondary products out of it for the modeling. UCSD's EDEX server, funded by NSF, makes high-resolution weather data available to researchers worldwide and enables visualization of weather systems and weather events lasting months or even years. The high-speed server aggregates weather data from the University Consortium for Atmospheric Research by way of a subscription service from the Consortium called the Internet Data Distribution system. These features are part of WIFIRE's long term goals to build an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. Although Firemap is a

  12. Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

    Science.gov (United States)

    Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

    2011-12-01

    In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

  13. Data-driven modelling of vertical dynamic excitation of bridges induced by people running

    Science.gov (United States)

    Racic, Vitomir; Morin, Jean Benoit

    2014-02-01

    With increasingly popular marathon events in urban environments, structural designers face a great deal of uncertainty when assessing dynamic performance of bridges occupied and dynamically excited by people running. While the dynamic loads induced by pedestrians walking have been intensively studied since the infamous lateral sway of the London Millennium Bridge in 2000, reliable and practical descriptions of running excitation are still very rare and limited. This interdisciplinary study has addressed the issue by bringing together a database of individual running force signals recorded by two state-of-the-art instrumented treadmills and two attempts to mathematically describe the measurements. The first modelling strategy is adopted from the available design guidelines for human walking excitation of structures, featuring perfectly periodic and deterministic characterisation of pedestrian forces presentable via Fourier series. This modelling approach proved to be inadequate for running loads due to the inherent near-periodic nature of the measured signals, a great inter-personal randomness of the dominant Fourier amplitudes and the lack of strong correlation between the amplitudes and running footfall rate. Hence, utilising the database established and motivated by the existing models of wind and earthquake loading, speech recognition techniques and a method of replicating electrocardiogram signals, this paper finally presents a numerical generator of random near-periodic running force signals which can reliably simulate the measurements. Such a model is an essential prerequisite for future quality models of dynamic loading induced by individuals, groups and crowds running under a wide range of conditions, such as perceptibly vibrating bridges and different combinations of visual, auditory and tactile cues.

  14. Real-Time Monitoring of TP Load in a Mississippi Delta Stream Using a Dynamic Data Driven Application System

    Science.gov (United States)

    Ouyang, Y.; Leininger, T.; Hatten, J. A.

    2012-12-01

    Elevated phosphorus (P) in surface waters can cause eutrophication of aquatic ecosystems and can impair water for drinking, industry, agriculture, and recreation. Currently, little effort has been devoted to monitoring real-time variation and load of total P (TP) in surface waters due to the lack of suitable and/or cost-effective wireless sensors. However, when considering human health, drinking water supply, and rapidly developing events such as algal blooms, the availability of timely P information is very critical. In this study, we developed a new approach in the form of a dynamic data driven application system (DDDAS) for monitoring the real-time variation and load of TP in surface water. This DDDAS consisted of the following three major components: (1) a User Control that interacts with Schedule Run to implement the DDDAS with starting and ending times; (2) a Schedule Run that activates the Hydstra model; and (3) a Hydstra model that downloads the real-time data from a US Geological Survey (USGS) website that is updated every 15 minutes with data from USGS monitoring stations, predicts real-time variation and load of TP, graphs the variables in real-time on a computer screen, and sends email alerts when the TP exceeds a certain value. The DDDAS was applied to monitor real-time variation and load of TP for 30 days in Deer Creek, a stream located east of Leland, Mississippi, USA. Results showed that the TP contents in the stream ranged from 0.24 to 0.48 mg L-1 with an average of 0.30 mg L-1 for a 30-day monitoring period, whereas the cumulative load of TP from the stream was about 2.8kg for the same monitoring period. Our study suggests that the DDDAS developed in this study was useful for estimating the real-time variation and load of TP in surface water ecosystems.

  15. Data-driven spectral decomposition and forecasting of ergodic dynamical systems

    CERN Document Server

    Giannakis, Dimitrios

    2015-01-01

    We develop a framework for dimension reduction, mode decomposition, and nonparametric forecasting of data generated by ergodic dynamical systems. This framework is based on a representation of the Koopman group of unitary operators in a smooth orthonormal basis of the L2 space of the dynamical system, acquired from time-ordered data through the diffusion maps algorithm. Using this representation, we compute Koopman eigenfunctions through a regularized advection-diffusion operator, and employ these eigenfunctions in dimension reduction maps with projectible dynamics and high smoothness for the given observation modality. In systems with pure point spectra, we construct a decomposition of the generator of the Koopman group into mutually commuting vector fields, which we reconstruct in data space through a representation of the pushforward map in the Koopman eigenfunction basis. We also use a special property of this basis, namely that the basis elements evolve as simple harmonic oscillators, to build nonparamet...

  16. Reduced-space Gaussian Process Regression for Data-Driven Probabilistic Forecast of Chaotic Dynamical Systems

    CERN Document Server

    Wan, Zhong Yi

    2016-01-01

    We formulate a reduced-order strategy for efficiently forecasting complex high-dimensional dynamical systems entirely based on data streams. The first step of our method involves reconstructing the dynamics in a reduced-order subspace of choice using Gaussian Process Regression (GPR). GPR simultaneously allows for reconstruction of the vector field and more importantly, estimation of local uncertainty. The latter is due to i) local interpolation error and ii) truncation of the high-dimensional phase space. This uncertainty component can be analytically quantified in terms of the GPR hyperparameters. In the second step we formulate stochastic models that explicitly take into account the reconstructed dynamics and their uncertainty. For regions of the attractor which are not sufficiently sampled for our GPR framework to be effective, an adaptive blended scheme is formulated to enforce correct statistical steady state properties, matching those of the real data. We examine the effectiveness of the proposed metho...

  17. Optimized Routing of Intelligent, Mobile Sensors for Dynamic, Data-Driven Sampling

    Science.gov (United States)

    2016-09-27

    notable dates Multi-Hole Probe System for Small UAS Flow Measurements (UMD invention disclosure #PS-2016-015) Do you plan to pursue a claim for personal or...estimation and control, to design coordinated sampling trajectories that yield the most informative measure - ments of estimated dynamical and stochastic...the DDDAS concept in which measurement data is used to update the model description and the updated model is used to guide subsequent measurements . 1

  18. Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics.

    Directory of Open Access Journals (Sweden)

    Gustavo Deco

    2009-12-01

    Full Text Available We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation, either experimentally or using simulations of biophysically detailed models. The dimensionality of the data is first reduced to the first principal component, and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation, which describes the motion of a Brownian particle in a potential. The advantage of such description is that the stationary probability density of the dynamical variable can be easily derived. We applied this method to the analysis of cortical network dynamics during up and down states in an anesthetized animal. During deep anesthesia, intracellularly recorded up and down states transitions occurred with high regularity and could not be adequately described by a one-dimensional diffusion equation. Under lighter anesthesia, however, the distributions of the times spent in the up and down states were better fitted by such a model, suggesting a role for noise in determining the time spent in a particular state.

  19. Data driven, predictive molecular dynamics for nanoscale flow simulations under uncertainty.

    Science.gov (United States)

    Angelikopoulos, Panagiotis; Papadimitriou, Costas; Koumoutsakos, Petros

    2013-11-27

    For over five decades, molecular dynamics (MD) simulations have helped to elucidate critical mechanisms in a broad range of physiological systems and technological innovations. MD simulations are synergetic with experiments, relying on measurements to calibrate their parameters and probing "what if scenarios" for systems that are difficult to investigate experimentally. However, in certain systems, such as nanofluidics, the results of experiments and MD simulations differ by several orders of magnitude. This discrepancy may be attributed to the spatiotemporal scales and structural information accessible by experiments and simulations. Furthermore, MD simulations rely on parameters that are often calibrated semiempirically, while the effects of their computational implementation on their predictive capabilities have only been sporadically probed. In this work, we show that experimental and MD investigations can be consolidated through a rigorous uncertainty quantification framework. We employ a Bayesian probabilistic framework for large scale MD simulations of graphitic nanostructures in aqueous environments. We assess the uncertainties in the MD predictions for quantities of interest regarding wetting behavior and hydrophobicity. We focus on three representative systems: water wetting of graphene, the aggregation of fullerenes in aqueous solution, and the water transport across carbon nanotubes. We demonstrate that the dominant mode of calibrating MD potentials in nanoscale fluid mechanics, through single values of water contact angle on graphene, leads to large uncertainties and fallible quantitative predictions. We demonstrate that the use of additional experimental data reduces uncertainty, improves the predictive accuracy of MD models, and consolidates the results of experiments and simulations.

  20. A new data-driven model for post-transplant antibody dynamics in high risk kidney transplantation.

    Science.gov (United States)

    Zhang, Yan; Briggs, David; Lowe, David; Mitchell, Daniel; Daga, Sunil; Krishnan, Nithya; Higgins, Robert; Khovanova, Natasha

    2017-02-01

    The dynamics of donor specific human leukocyte antigen antibodies during early stage after kidney transplantation are of great clinical interest as these antibodies are considered to be associated with short and long term clinical outcomes. The limited number of antibody time series and their diverse patterns have made the task of modelling difficult. Focusing on one typical post-transplant dynamic pattern with rapid falls and stable settling levels, a novel data-driven model has been developed for the first time. A variational Bayesian inference method has been applied to select the best model and learn its parameters for 39 time series from two groups of graft recipients, i.e. patients with and without acute antibody-mediated rejection (AMR) episodes. Linear and nonlinear dynamic models of different order were attempted to fit the time series, and the third order linear model provided the best description of the common features in both groups. Both deterministic and stochastic parameters are found to be significantly different in the AMR and no-AMR groups showing that the time series in the AMR group have significantly higher frequency of oscillations and faster dissipation rates. This research may potentially lead to better understanding of the immunological mechanisms involved in kidney transplantation.

  1. Data-Driven Tracking Control With Adaptive Dynamic Programming for a Class of Continuous-Time Nonlinear Systems.

    Science.gov (United States)

    Mu, Chaoxu; Ni, Zhen; Sun, Changyin; He, Haibo

    2016-04-22

    A data-driven adaptive tracking control approach is proposed for a class of continuous-time nonlinear systems using a recent developed goal representation heuristic dynamic programming (GrHDP) architecture. The major focus of this paper is on designing a multivariable tracking scheme, including the filter-based action network (FAN) architecture, and the stability analysis in continuous-time fashion. In this design, the FAN is used to observe the system function, and then generates the corresponding control action together with the reference signals. The goal network will provide an internal reward signal adaptively based on the current system states and the control action. This internal reward signal is assigned as the input for the critic network, which approximates the cost function over time. We demonstrate its improved tracking performance in comparison with the existing heuristic dynamic programming (HDP) approach under the same parameter and environment settings. The simulation results of the multivariable tracking control on two examples have been presented to show that the proposed scheme can achieve better control in terms of learning speed and overall performance.

  2. Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

    Directory of Open Access Journals (Sweden)

    Eli eShlizerman

    2014-08-01

    Full Text Available The antennal lobe (AL, olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units, and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (i design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii characterize scent recognition, i.e., decision-making based on olfactory signals and (iii infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.

  3. Data-Driven Techniques for Detecting Dynamical State Changes in Noisily Measured 3D Single-Molecule Trajectories

    Directory of Open Access Journals (Sweden)

    Christopher P. Calderon

    2014-11-01

    Full Text Available Optical microscopes and nanoscale probes (AFM, optical tweezers, etc. afford researchers tools capable of quantitatively exploring how molecules interact with one another in live cells. The analysis of in vivo single-molecule experimental data faces numerous challenges due to the complex, crowded, and time changing environments associated with live cells. Fluctuations and spatially varying systematic forces experienced by molecules change over time; these changes are obscured by “measurement noise” introduced by the experimental probe monitoring the system. In this article, we demonstrate how the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS of Fox et al. [IEEE Transactions on Signal Processing 59] can be used to detect both subtle and abrupt state changes in time series containing “thermal” and “measurement” noise. The approach accounts for temporal dependencies induced by random and “systematic overdamped” forces. The technique does not require one to subjectively select the number of “hidden states” underlying a trajectory in an a priori fashion. The number of hidden states is simultaneously inferred along with change points and parameters characterizing molecular motion in a data-driven fashion. We use large scale simulations to study and compare the new approach to state-of-the-art Hidden Markov Modeling techniques. Simulations mimicking single particle tracking (SPT experiments are the focus of this study.

  4. A New Application of Dynamic Data Driven System in the Talbot-Ogden Model for Groundwater Infiltration

    KAUST Repository

    Yu, Han

    2012-06-02

    The TalbotOgden model is a mass conservative method to simulate flow of a wetting liquid in variably-saturated porous media. The principal feature of this model is the discretization of the moisture content domain into bins. This paper gives an analysis of the relationship between the number of bins and the computed flux. Under the circumstances of discrete bins and discontinuous wetting fronts, we show that fluxes increase with the number of bins. We then apply this analysis to the continuous case and get an upper bound of the difference of infiltration rates when the number of bins tends to infinity. We also extend this model by creating a two dimensional moisture content domain so that there exists a probability distribution of the moisture content for different soil systems. With these theoretical and experimental results and using a Dynamic Data Driven Application System (DDDAS), sensors can be put in soils to detect the infiltration fluxes, which are important to compute the proper number of bins for a specific soil system and predict fluxes. Using this feedback control loop, the extended TalbotOgden model can be made more efficient for estimating infiltration into soils.

  5. Designing a Dynamic Data Driven Application System for Estimating Real-Time Load of DOC in a River

    Science.gov (United States)

    Ouyang, Y.; None

    2011-12-01

    Understanding the dynamics of naturally occurring dissolved organic carbon (DOC) in a river is central to estimating surface water quality, aquatic carbon cycling, and climate change. Currently, determination of DOC in surface water is primarily accomplished by manually collecting samples for laboratory analysis, which requires at least 24 hours. In other words, no effort has been devoted to monitoring real-time variations of DOC in a river due to the lack of suitable and/or cost-effective wireless sensors. However, when considering human health, carbon footprints, and effects of urbanization, industry, and agriculture on water resource supply, timely DOC information may be critical. We have developed here a new paradigm, a dynamic data driven application system (DDDAS), for estimating the real-time load of DOC into a river. This DDDAS consisted of the following four components: (1) a Visual Basic (VB) program for downloading US Geological Survey real-time chlorophyll and discharge data; (2) a STELLA model for evaluating real-time DOC load based on the relationship between chlorophyll a, DOC, and river discharge; (3) a batch file for linking the VB program and STELLA model; and (4) a Microsoft Windows Scheduled Tasks wizard for executing the model and displaying output on a computer screen at selected times. Results show that the real-time load of DOC into the St. Johns River basin near Satsuma, Putnam County, Florida, USA varied over a range from -13,143 to 29,248 kg/h at the selected site in Florida, USA. The negative loads occurred because of the back flow in the estuarine reach of the river. The cumulative load of DOC in the river for the selected site at the end of the simulation (178 hours) was about 1.2 tons. Our results support the utility of the DDDAS developed in this study for estimating the real-time variations of DOC in river ecosystems.

  6. Development of a data-driven algorithm to determine the W+jets background in t anti t events in ATLAS

    Energy Technology Data Exchange (ETDEWEB)

    Mehlhase, Sascha

    2010-07-12

    The physics of the top quark is one of the key components in the physics programme of the ATLAS experiment at the Large Hadron Collider at CERN. In this thesis, general studies of the jet trigger performance for top quark events using fully simulated Monte Carlo samples are presented and two data-driven techniques to estimate the multi-jet trigger efficiency and the W+Jets background in top pair events are introduced to the ATLAS experiment. In a tag-and-probe based method, using a simple and common event selection and a high transverse momentum lepton as tag object, the possibility to estimate the multijet trigger efficiency from data in ATLAS is investigated and it is shown that the method is capable of estimating the efficiency without introducing any significant bias by the given tag selection. In the second data-driven analysis a new method to estimate the W+Jets background in a top-pair event selection is introduced to ATLAS. By defining signal and background dominated regions by means of the jet multiplicity and the pseudo-rapidity distribution of the lepton in the event, the W+Jets contribution is extrapolated from the background dominated into the signal dominated region. The method is found to estimate the given background contribution as a function of the jet multiplicity with an accuracy of about 25% for most of the top dominated region with an integrated luminosity of above 100 pb{sup -1} at {radical}(s) = 10 TeV. This thesis also covers a study summarising the thermal behaviour and expected performance of the Pixel Detector of ATLAS. All measurements performed during the commissioning phase of 2008/09 yield results within the specification of the system and the performance is expected to stay within those even after several years of running under LHC conditions. (orig.)

  7. Selection of independent components representing event-related brain potentials: A data-driven approach for greater objectivity

    NARCIS (Netherlands)

    Wessel, J.R.; Ullsperger, M.

    2011-01-01

    Following the development of increasingly precise measurement instruments and fine-grain analysis tools for electroencephalographic (EEG) data, analysis of single-trial event-related EEG has considerably widened the utility of this non-invasive method to investigate brain activity. Recently, indepen

  8. Data-driven storytelling

    CERN Document Server

    Henry Riche, Nathalie

    2017-01-01

    This book is an accessible introduction to data-driven storytelling, resulting from discussions between data visualization researchers and data journalists. This book will be the first to define the topic, present compelling examples and existing resources, as well as identify challenges and new opportunities for research.

  9. Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques

    Science.gov (United States)

    Chang, Tak Kwin; Talei, Amin; Alaghmand, Sina; Ooi, Melanie Po-Leen

    2017-02-01

    Input selection for data-driven rainfall-runoff models is an important task as these models find the relationship between rainfall and runoff by direct mapping of inputs to output. In this study, two different input selection methods were used: cross-correlation analysis (CCA), and a combination of mutual information and cross-correlation analyses (MICCA). Selected inputs were used to develop adaptive network-based fuzzy inference system (ANFIS) in Sungai Kayu Ara basin, Selangor, Malaysia. The study catchment has 10 rainfall stations and one discharge station located at the outlet of the catchment. A total of 24 rainfall-runoff events (10-min interval) from 1996 to 2004 were selected from which 18 events were used for training and the remaining 6 were reserved for validating (testing) the models. The results of ANFIS models then were compared against the ones obtained by conceptual model HEC-HMS. The CCA and MICCA methods selected the rainfall inputs only from 2 (stations 1 and 5) and 3 (stations 1, 3, and 5) rainfall stations, respectively. ANFIS model developed based on MICCA inputs (ANFIS-MICCA) performed slightly better than the one developed based on CCA inputs (ANFIS-CCA). ANFIS-CCA and ANFIS-MICCA were able to perform comparably to HEC-HMS model where rainfall data of all 10 stations had been used; however, in peak estimation, ANFIS-MICCA was the best model. The sensitivity analysis on HEC-HMS was conducted by recalibrating the model by using the same selected rainfall stations for ANFIS. It was concluded that HEC-HMS model performance deteriorates if the number of rainfall stations reduces. In general, ANFIS was found to be a reliable alternative for HEC-HMS in cases whereby not all rainfall stations are functioning. This study showed that the selected stations have received the highest total rain and rainfall intensity (stations 3 and 5). Moreover, the contributing rainfall stations selected by CCA and MICCA were found to be located near the outlet of

  10. Designing a dynamic data driven application system for estimating real-time load of dissolved organic carbon in a river

    Science.gov (United States)

    Ying. Ouyang

    2012-01-01

    Understanding the dynamics of naturally occurring dissolved organic carbon (DOC) in a river is central to estimating surface water quality, aquatic carbon cycling, and global climate change. Currently, determination of the DOC in surface water is primarily accomplished by manually collecting samples for laboratory analysis, which requires at least 24 h. In other words...

  11. Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

    Science.gov (United States)

    2013-09-30

    Dmitri Kondrashov Dept. of Atmospheric & Oceanic Sciences and Institute of Geophysics and Planetary Physics, University of California, Los Angeles, CA...chaotic and dissipative dynamical systems, Chekroun et al. (2013c) have shown that the recurrences observed in planetary flows can play a key role...are the most energetic and correlations decay slowly. Parameterizing manifolds for stochastic partial differential equations Chekroun et al. (2013a,b

  12. Application of direct virtual coil to dynamic contrast-enhanced MRI and MR angiography with data-driven parallel imaging.

    Science.gov (United States)

    Wang, Kang; Beatty, Philip J; Nagle, Scott K; Reeder, Scott B; Holmes, James H; Rahimi, Mahdi S; Bell, Laura C; Korosec, Frank R; Brittain, Jean H

    2014-02-01

    To demonstrate the feasibility of direct virtual coil (DVC) in the setting of 4D dynamic imaging used in multiple clinical applications. Three dynamic imaging applications were chosen: pulmonary perfusion, liver perfusion, and peripheral MR angiography (MRA), with 18, 11, and 10 subjects, respectively. After view-sharing, the k-space data were reconstructed twice: once with channel-by-channel (CBC) followed by sum-of-squares coil combination and once with DVC. Images reconstructed using CBC and DVC were compared and scored based on overall image quality by two experienced radiologists using a five-point scale. The CBC and DVC showed similar image quality in image domain. Time course measurements also showed good agreement in the temporal domain. CBC and DVC images were scored as equivalent for all pulmonary perfusion cases, all liver perfusion cases, and four of the 10 peripheral MRA cases. For the remaining six peripheral MRA cases, DVC were scored as slightly better (not clinically significant) than the CBC images by Radiologist A and as equivalent by Radiologist B. For dynamic contrast-enhanced MR applications, it is clinically feasible to reduce image reconstruction time while maintaining image quality and time course measurement using the DVC technique. Copyright © 2013 Wiley Periodicals, Inc.

  13. The evolution of agricultural intensification and environmental degradation in the UK: a data-driven systems dynamics approach

    Science.gov (United States)

    Armstrong McKay, David I.; Dearing, John A.; Dyke, James G.; Poppy, Guy; Firbank, Les

    2016-04-01

    The world's population continues to grow rapidly, yet the current demand for food is already resulting in environmental degradation in many regions. As a result, an emerging challenge of the 21st century is how agriculture can simultaneously undergo sustainable intensification and be made more resilient to accelerating climate change. Key to this challenge is: a) finding the "safe and just operating space" for the global agri-environment system that both provides sufficient food for humanity and avoids crossing dangerous planetary boundaries, and b) downscaling this framework from a planetary to a regional scale in order to better inform decision making and incorporate regional dynamics within the planetary boundaries framework. Regional safe operating spaces can be defined and explored using a combination of metrics that indicate the changing status of ecosystem services (both provisioning and regulating), statistical techniques that reveal early warning signals and breakpoints, and dynamical system models of the regional agri-environment system. Initial attempts to apply this methodology have been made in developing countries (e.g. China [Dearing et al., 2012, 2014; Zhang et al., 2015]), but have not yet been attempted in more developed countries, for example the UK. In this study we assess the changes in ecosystem services in two contrasting agricultural regions in the UK, arable-dominated East England and pastoral-dominated South-West England, since the middle of the 20th Century. We identify and establish proxies and indices of various provisioning and regulating services in these two regions and analyse how these have changed over this time. We find that significant degradation of regulating services occurred in Eastern England in the early 1980s, reflecting a period of rapid intensification and escalating fertiliser usage, but that regulating services have begun to recover since 2000 mainly as a result of fertiliser usage decoupling from increasing wheat

  14. Data-driven computational mechanics

    CERN Document Server

    Kirchdoerfer, Trenton

    2015-01-01

    We develop a new computing paradigm, which we refer to as data-driven computing, according to which calculations are carried out directly from experimental material data and pertinent constraints and conservation laws, such as compatibility and equilibrium, thus bypassing the empirical material modeling step of conventional computing altogether. Data-driven solvers seek to assign to each material point the state from a prespecified data set that is closest to satisfying the conservation laws. Equivalently, data-driven solvers aim to find the state satisfying the conservation laws that is closest to the data set. The resulting data-driven problem thus consists of the minimization of a distance function to the data set in phase space subject to constraints introduced by the conservation laws. We motivate the data-driven paradigm and investigate the performance of data-driven solvers by means of two examples of application, namely, the static equilibrium of nonlinear three-dimensional trusses and linear elastici...

  15. The dynamic chain event graph

    OpenAIRE

    2015-01-01

    In this paper we develop a formal dynamic version of Chain Event Graphs (CEGs), a particularly expressive family of discrete graphical models. We demonstrate how this class links to semi-Markov models and provides a convenient generalization of the Dynamic Bayesian Network (DBN). In particular we develop a repeating time-slice Dynamic CEG providing a useful and simpler model in this family. We demonstrate how the Dynamic CEG’s graphical formulation exhibits asymmetric conditional independence...

  16. Data Driven Dark Ages

    Science.gov (United States)

    Thurner, Stefan

    If physics is the experimental science of matter that interacts through the four basic interactions, the science of complex systems is its natural extension, where the concepts of matter and interactions are generalized. Matter can be anything that is capable of interacting, interactions can be anything that is able to change states of the constituents of a system. Complex systems are made from many constituents (parts) that interact through interaction networks. These parts are characterized by states that change over time. At the same time the interaction networks may change over time. What makes a system complex is that the states of the parts change as a function F of the interaction network (and the states), and, simultaneously, the interaction networks change as another function G of the states of the nodes (and the networks). Physics is about the predictive understanding of the dynamics and changes of states once the interactions and initial and boundary conditions are specified. In complex systems interactions also change over time, and to make things really complicated, these changes are coupled to the dynamics of the state-changes. States co-evolve with the interaction networks. In this sense complex systems often are chicken-egg problems. They are evolutionary, show emergent behavior, can be self-organized critical, show power laws, etc...

  17. NEBULAS A High Performance Data-Driven Event-Building Architecture based on an Asynchronous Self-Routing Packet-Switching Network

    CERN Multimedia

    Costa, M; Letheren, M; Djidi, K; Gustafsson, L; Lazraq, T; Minerskjold, M; Tenhunen, H; Manabe, A; Nomachi, M; Watase, Y

    2002-01-01

    RD31 : The project is evaluating a new approach to event building for level-two and level-three processor farms at high rate experiments. It is based on the use of commercial switching fabrics to replace the traditional bus-based architectures used in most previous data acquisition sytems. Switching fabrics permit the construction of parallel, expandable, hardware-driven event builders that can deliver higher aggregate throughput than the bus-based architectures. A standard industrial switching fabric technology is being evaluated. It is based on Asynchronous Transfer Mode (ATM) packet-switching network technology. Commercial, expandable ATM switching fabrics and processor interfaces, now being developed for the future Broadband ISDN infrastructure, could form the basis of an implementation. The goals of the project are to demonstrate the viability of this approach, to evaluate the trade-offs involved in make versus buy options, to study the interfacing of the physics frontend data buffers to such a fabric, a...

  18. Data-driven batch schuduling

    Energy Technology Data Exchange (ETDEWEB)

    Bent, John [Los Alamos National Laboratory; Denehy, Tim [GOOGLE; Arpaci - Dusseau, Remzi [UNIV OF WISCONSIN; Livny, Miron [UNIV OF WISCONSIN; Arpaci - Dusseau, Andrea C [NON LANL

    2009-01-01

    In this paper, we develop data-driven strategies for batch computing schedulers. Current CPU-centric batch schedulers ignore the data needs within workloads and execute them by linking them transparently and directly to their needed data. When scheduled on remote computational resources, this elegant solution of direct data access can incur an order of magnitude performance penalty for data-intensive workloads. Adding data-awareness to batch schedulers allows a careful coordination of data and CPU allocation thereby reducing the cost of remote execution. We offer here new techniques by which batch schedulers can become data-driven. Such systems can use our analytical predictive models to select one of the four data-driven scheduling policies that we have created. Through simulation, we demonstrate the accuracy of our predictive models and show how they can reduce time to completion for some workloads by as much as 80%.

  19. Combining engineering and data-driven approaches

    DEFF Research Database (Denmark)

    Fischer, Katharina; De Sanctis, Gianluca; Kohler, Jochen

    2015-01-01

    assumptions that may result in a biased risk assessment. In two related papers we show how engineering and data-driven modelling can be combined by developing generic risk models that are calibrated to statistical data on observed fire events. The focus of the present paper is on the calibration procedure....... A framework is developed that is able to deal with data collection in non-homogeneous portfolios of buildings. Also incomplete data sets containing only little information on each fire event can be used for model calibration. To illustrate the capabilities of the proposed framework, it is applied...

  20. Data-Driven Proficiency Profiling

    Science.gov (United States)

    Mostafavi, Behrooz; Liu, Zhongxiu; Barnes, Tiffany

    2015-01-01

    Deep Thought is a logic tutor where students practice constructing deductive logic proofs. Within Deep Thought is a data-driven mastery learning system (DDML), which calculates student proficiency based on rule scores weighted by expert-decided weights in order to assign problem sets of appropriate difficulty. In this study, we designed and tested…

  1. Data Driven Constraints for the SVM

    DEFF Research Database (Denmark)

    Darkner, Sune; Clemmensen, Line Katrine Harder

    2012-01-01

    We propose a generalized data driven constraint for support vector machines exemplified by classification of paired observations in general and specifically on the human ear canal. This is particularly interesting in dynamic cases such as tissue movement or pathologies developing over time....... Assuming that two observations of the same subject in different states span a vector, we hypothesise that such structure of the data contains implicit information which can aid the classification, thus the name data driven constraints. We derive a constraint based on the data which allow for the use...... of the ℓ1-norm on the constraint while still allowing for the application of kernels. We specialize the proposed constraint to orthogonality of the vectors between paired observations and the estimated hyperplane. We show that imposing the constraint of orthogonality on the paired data yields a more robust...

  2. Dynamical studies of the Mersa Matruh Gyre: intense meander and ring formation events

    Science.gov (United States)

    Golnaraghi, Maryam

    A study of the dynamics of the Mersa Matruh Gyre and the Mid-Mediterranean Jet flow system in the southwestern Levantine basin is presented. Data-driven simulations in the Levantine basin, using an eddy-resolving quasigeostrophic model initialized with two quasi-synoptic hydrographic data sets, reveal intense mesoscale meander and ring formation events involving the Mid-Mediterranean Jet, the Mersa Matruh Gyre and the Rhodes Gyre. The dynamics of these events are quantified via local energy and vorticity budget analyses. The dominant processes are investigated and compared with previously studied events in the Gulf Stream Ring and Meander region.

  3. Data driven marketing for dummies

    CERN Document Server

    Semmelroth, David

    2013-01-01

    Embrace data and use it to sell and market your products Data is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products. Successful data analysis can help marketing professionals spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Data Driven Marketing For Dummies helps companies use all the data at their disposal to make current customers more satisfied, reach new customers, and sell to their most important customer segments more efficiently. Identifyi

  4. Dynamical freeze-out in event-by-event hydrodynamics

    CERN Document Server

    Holopainen, Hannu

    2012-01-01

    In hydrodynamical modeling of the ultrarelativistic heavy-ion collisions the freeze-out is typically performed at a constant temperature or density. In this work we apply a dynamical freeze-out criterion, which compares the hydrodynamical expansion rate with the pion scattering rate. Recently many calculations have been done using event-by-event hydrodynamics where the initial density profile fluctuates from event to event. In these event-by-event calculations the expansion rate fluctuates strongly as well, and thus it is interesting to check how the dynamical freeze-out changes hadron distributions with respect to the constant temperature freeze-out. We present hadron spectra and elliptic flow calculated using (2+1)-dimensional ideal hydrodynamics, and show the differences between constant temperature and dynamical freeze-out criteria. We find that the differences caused by different freeze-out criteria are small in all studied cases.

  5. Data-driven architectural production and operation

    NARCIS (Netherlands)

    Bier, H.H.; Mostafavi, S.

    2014-01-01

    Data-driven architectural production and operation as explored within Hyperbody rely heavily on system thinking implying that all parts of a system are to be understood in relation to each other. These relations are increasingly established bi-directionally so that data-driven architecture is not

  6. Data Driven Device Failure Prediction

    Science.gov (United States)

    2016-09-15

    Cyber Security and Control System D-PLG Distributed PowerShell Load Generator DC Domain Controller DNS Domain Name System DOD Department of Defense...manpower burden. Specifically, in the Air Force, it could most effectively be implemented and used by the Cyber Security and Control System (CSCS...MSWinEventLog 5 Security 3 Sun May 08 14:31:50 2016 4672 Microsoft- Windows- Security -Auditing N/A Audit Success dc.afnet.com 12548 Special privileges

  7. Dynamic Data Driven Applications Systems (DDDAS)

    Science.gov (United States)

    2013-03-06

    Experiments in Aerodynamics (“Integration”), Tokyo-Japan, Oct 3-5, 2012  (Keynote) New Frontiers through Computer and Information Science...Chemical pollution transport (atmosphere, aquatic, subsurface), ecological systems, molecular bionetworks, protein folding.. • Medical and

  8. The Structural Consequences of Big Data-Driven Education.

    Science.gov (United States)

    Zeide, Elana

    2017-06-01

    Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education

  9. 基于动态数据驱动的多UAV实时任务规划%Multi-UAV Real-time Mission Planning Based on Dynamic Data Driven Application System

    Institute of Scientific and Technical Information of China (English)

    朱林; 方胜良; 吴付祥; 吴志建

    2014-01-01

    针对在实时动态条件下多UAV任务规划问题,提出了基于动态数据驱动的多UAV实时任务规划仿真平台,主要包括基于MultiUAV2的真实UAV群仿真平台和基于Multi-Agent的预测仿真平台两个部分。采用了A*算法对真实系统工作流进行探索,在此基础之上构建了多UAV合成工作流模型,然后针对动态数据注入运行仿真的问题,研究了传感器任务重置及传感器的预处理方法。最后,通过一个仿真实例验证了提出方法的可行性和有效性。%In order to achieve mission planning of Multi-UAV in the real-time and dynamic conditions,a Multi-UAV mission planning simulation system based on dynamic data driven application system(DDDAS)is proposed,mainly include real UAV swarm simulation platform based on MultiUAV2 and predictive simulation platform based on Multi-Agent. As a breakthrough point of the key technologies that addressed in the simulation system,the workflows within real-life system is discovered by using A* algorithm. Based on these analyses,a Multi-UAV workflows model is constructed,then the method of sensor re-tasking and sensor-based pre-processing are studied for injecting streaming sensor data into a running simulation. Finally,through a simulation example,the proposed method is feasibility and validity.

  10. Characterization of Rare Events in Molecular Dynamics

    Directory of Open Access Journals (Sweden)

    Carsten Hartmann

    2013-12-01

    Full Text Available A good deal of molecular dynamics simulations aims at predicting and quantifying rare events, such as the folding of a protein or a phase transition. Simulating rare events is often prohibitive, especially if the equations of motion are high-dimensional, as is the case in molecular dynamics. Various algorithms have been proposed for efficiently computing mean first passage times, transition rates or reaction pathways. This article surveys and discusses recent developments in the field of rare event simulation and outlines a new approach that combines ideas from optimal control and statistical mechanics. The optimal control approach described in detail resembles the use of Jarzynski’s equality for free energy calculations, but with an optimized protocol that speeds up the sampling, while (theoretically giving variance-free estimators of the rare events statistics. We illustrate the new approach with two numerical examples and discuss its relation to existing methods.

  11. Dynamic Event Tree Analysis Through RAVEN

    Energy Technology Data Exchange (ETDEWEB)

    A. Alfonsi; C. Rabiti; D. Mandelli; J. Cogliati; R. A. Kinoshita; A. Naviglio

    2013-09-01

    Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (D-PRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other application including the ones based on the MOOSE framework, developed by INL as well. RAVEN performs two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, RAVEN also models stochastic events, such as components failures, and performs uncertainty quantification. Such stochastic modeling is employed by using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This paper focuses on the first task and shows how it is possible to perform the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, the Dynamic PRA analysis, using Dynamic Event Tree, of a simplified pressurized water reactor for a Station Black-Out scenario is presented.

  12. Event Normalization Through Dynamic Log Format Detection

    Institute of Scientific and Technical Information of China (English)

    Christoph Meinel

    2014-01-01

    The analytical and monitoring capabilities of central event re-positories, such as log servers and intrusion detection sys-tems, are limited by the amount of structured information ex-tracted from the events they receive. Diverse networks and ap-plications log their events in many different formats, and this makes it difficult to identify the type of logs being received by the central repository. The way events are logged by IT systems is problematic for developers of host-based intrusion-detection systems (specifically, host-based systems), develop-ers of security-information systems, and developers of event-management systems. These problems preclude the develop-ment of more accurate, intrusive security solutions that obtain results from data included in the logs being processed. We propose a new method for dynamically normalizing events into a unified super-event that is loosely based on the Common Event Expression standard developed by Mitre Corporation. We explain how our solution can normalize seemingly unrelat-ed events into a single, unified format.

  13. Fault diagnosis approach of dynamic system based on data driven of nonlinear spectrum%基于非线性频谱数据驱动的动态系统故障诊断方法

    Institute of Scientific and Technical Information of China (English)

    张家良; 曹建福; 高峰; 韩海涛

    2014-01-01

    基于非线性频谱数据驱动方法,研究了动态系统的故障诊断问题。利用一维非线性输出频率响应函数提出一种非线性频谱特征提取方法,为了提高实时性,采用变步长自适应辨识算法进行求解;根据估计偏差实时地改变步长,兼顾了收敛速度与稳态误差;获取了非线性频谱特征之后,利用最小二乘支持向量机分类器进行故障识别。通过对提升设备的故障诊断问题进行实验研究,所得结果表明,所提出的算法识别率高,能满足在线诊断要求。%The problem of fault diagnosis for the dynamic system is studied based on the data driven method of nonlinear spectrum. An extraction method of nonlinear frequency spectrum feature is proposed by using one dimensional nonlinear output frequency response function. In order to improve timeliness, the variable step size adaptive identification algorithm is used to solve the nonlinear output frequency response function. The step size is changed according to estimating error so that convergence rate and steady state error are both considered. After obtained nonlinear frequency spectrum feature, the least square support vector machine classifier is used to fault identification. The fault diagnosis of hoisting equipment is researched, and experiments show that the proposed algorithm has the good high recognition rate that can fulfill the demand of online diagnosis.

  14. Temporal dynamics of hydrological threshold events

    Directory of Open Access Journals (Sweden)

    G. S. McGrath

    2006-09-01

    Full Text Available The episodic nature of hydrological flows such as surface runoff and preferential flow is a result of the nonlinearity of their triggering and the intermittency of rainfall. In this paper we examine the temporal dynamics of threshold processes that are triggered by either an infiltration excess (IE mechanism when rainfall intensity exceeds a specified threshold value, or a saturation excess (SE mechanism governed by a storage threshold. We analytically derive probabilistic measures of the time between successive events in each case, and in the case of the SE triggering, we relate the statistics of the time between events to the statistics of storage and the underlying water balance. In the case of the IE mechanism, the temporal dynamics of flow events is shown to be simply scaled statistics of rainfall timing. In the case of the SE mechanism the time between events becomes structured. With increasing climate aridity the mean and the variance of the time between SE events increases but temporal clustering, as measured by the coefficient of variation (CV of the inter-event time, reaches a maximum in deep stores when the climatic aridity index equals 1. In very humid and also very arid climates, the temporal clustering disappears, and the pattern of triggering is similar to that seen for the IE mechanism. In addition we show that the mean and variance of the magnitude of SE events decreases but the CV increases with increasing aridity. The CV of inter-event times is found to be approximately equal to the CV of the magnitude of SE events per storm only in very humid climates with the CV of event magnitude tending to be much larger than the CV of inter-event times in arid climates. In comparison to storage the maximum temporal clustering was found to be associated with a maximum in the variance of soil moisture. The CV of the time till the first saturation excess event was found to be greatest when the initial storage was at the threshold.

  15. Data-driven regionalization of housing markets

    NARCIS (Netherlands)

    Helbich, M.; Brunauer, W.; Hagenauer, J.; Leitner, M.

    2013-01-01

    This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The

  16. On the data-driven COS method

    NARCIS (Netherlands)

    A. Leitao Rodriguez (Álvaro); C.W. Oosterlee (Cornelis); L. Ortiz Gracia (Luis); S.M. Bohte (Sander)

    2018-01-01

    textabstractIn this paper, we present the data-driven COS method, ddCOS. It is a Fourier-based finan- cial option valuation method which assumes the availability of asset data samples: a char- acteristic function of the underlying asset probability density function is not required. As such, the

  17. Data-driven regionalization of housing markets

    NARCIS (Netherlands)

    Helbich, M.|info:eu-repo/dai/nl/370530349; Brunauer, W.; Hagenauer, J.; Leitner, M.

    2013-01-01

    This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The

  18. Temporal dynamics of hydrological threshold events

    Directory of Open Access Journals (Sweden)

    G. S. McGrath

    2007-01-01

    Full Text Available The episodic nature of hydrological flows such as surface runoff and preferential flow is a result of the nonlinearity of their triggering and the intermittency of rainfall. In this paper we examine the temporal dynamics of threshold processes that are triggered by either an infiltration excess (IE mechanism when rainfall intensity exceeds a specified threshold value, or a saturation excess (SE mechanism governed by a storage threshold. We use existing and newly derived analytical results to describe probabilistic measures of the time between successive events in each case, and in the case of the SE triggering, we relate the statistics of the time between events (the inter-event time, denoted IET to the statistics of storage and the underlying water balance. In the case of the IE mechanism, the temporal dynamics of flow events is found to be simply scaled statistics of rainfall timing. In the case of the SE mechanism the time between events becomes structured. With increasing climate aridity the mean and the variance of the time between SE events increases but temporal clustering, as measured by the coefficient of variation (CV of the IET, reaches a maximum in deep stores when the climatic aridity index equals 1. In very humid and also very arid climates, the temporal clustering disappears, and the pattern of triggering is similar to that seen for the IE mechanism. In addition we show that the mean and variance of the magnitude of SE events decreases but the CV increases with increasing aridity. The CV of IETs is found to be approximately equal to the CV of the magnitude of SE events per storm only in very humid climates with the CV of event magnitude tending to be much larger than the CV of IETs in arid climates. In comparison to storage the maximum temporal clustering was found to be associated with a maximum in the variance of soil moisture. The CV of the time till the first saturation excess event was found to be greatest when the initial

  19. Data Driven Tuning of Inventory Controllers

    DEFF Research Database (Denmark)

    Huusom, Jakob Kjøbsted; Santacoloma, Paloma Andrade; Poulsen, Niels Kjølstad

    2007-01-01

    A systematic method for criterion based tuning of inventory controllers based on data-driven iterative feedback tuning is presented. This tuning method circumvent problems with modeling bias. The process model used for the design of the inventory control is utilized in the tuning...... as an approximation to reduce time required on experiments. The method is illustrated in an application with a multivariable inventory control implementation on a four tank system....

  20. Data driven CAN node reliability assessment for manufacturing system

    Science.gov (United States)

    Zhang, Leiming; Yuan, Yong; Lei, Yong

    2017-01-01

    The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

  1. Data-Driven Information Extraction from Chinese Electronic Medical Records.

    Directory of Open Access Journals (Sweden)

    Dong Xu

    Full Text Available This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event.Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM algorithm that innovatively utilizes Normalized Google Distance (NGD to estimate the correlation between medical events and their descriptions.The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846.In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886. In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838.The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica.

  2. Data-driven parameterization of the generalized Langevin equation

    Energy Technology Data Exchange (ETDEWEB)

    Lei, Huan; Baker, Nathan A.; Li, Xiantao

    2016-11-29

    We present a data-driven approach to determine the memory kernel and random noise of the generalized Langevin equation. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. Further, we show that such an approximation can be constructed to arbitrarily high order. Within these approximations, the generalized Langevin dynamics can be embedded in an extended stochastic model without memory. We demonstrate how to introduce the stochastic noise so that the fluctuation-dissipation theorem is exactly satisfied.

  3. Challenges of Data-driven Healthcare Management

    DEFF Research Database (Denmark)

    Bossen, Claus; Danholt, Peter; Ubbesen, Morten Bonde

    activity and financing and relies of extensive data entry, reporting and calculations. This has required the development of new skills, work and work roles. The second case concerns a New Governance project aimed at developing new performance indicators for healthcare delivery as an alternative to DRG....... Here, a core challenge is select indicators and actually being able to acquire data upon them. The two cases point out that data-driven healthcare requires more and new kinds of work for which new skills, functions and work roles have to be developed....

  4. Data-driven modeling of systemic delay propagation under severe meteorological conditions

    CERN Document Server

    Fleurquin, Pablo; Eguiluz, Victor M

    2013-01-01

    The upsetting consequences of weather conditions are well known to any person involved in air transportation. Still the quantification of how these disturbances affect delay propagation and the effectiveness of managers and pilots interventions to prevent possible large-scale system failures needs further attention. In this work, we employ an agent-based data-driven model developed using real flight performance registers for the entire US airport network and focus on the events occurring on October 27 2010 in the United States. A major storm complex that was later called the 2010 Superstorm took place that day. Our model correctly reproduces the evolution of the delay-spreading dynamics. By considering different intervention measures, we can even improve the model predictions getting closer to the real delay data. Our model can thus be of help to managers as a tool to assess different intervention measures in order to diminish the impact of disruptive conditions in the air transport system.

  5. Data-Driven Forecasting Schemes: Evaluation and Applications

    Directory of Open Access Journals (Sweden)

    J. V. Jr

    2007-01-01

    Full Text Available A reliable multi-step predictor is very useful to a wide array of applications to forecast the behavior of dynamic systems. The objective of this paper is to develop a more robust data-driven predictor for time series forecasting. Based on simulation analysis, it is found that multi-step-ahead forecasting schemes based on step inputs perform better than those based on sequential inputs. It is also realized that recurrent neural fuzzy predictor is superior to both recurrent neural networks and feedforward networks. In order to enhance the forecasting convergence, a hybrid training technique is proposed base on the real-time recurrent training and weighted least squares estimate. The developed predictor is also implemented for real-time applications in material property testing. The investigation results show that the developed adaptive predictor is a reliable forecasting tool. It can capture the system’s dynamic behavior quickly and track the system’s characteristics accurately. Its performance is superior to other classical data-driven forecasting schemes.

  6. Data-Driven Control of Refrigeration System

    DEFF Research Database (Denmark)

    Vinther, Kasper

    facilities without using a pressure sensor. A single-sensor solution is thus provided, which either reduces the variable costs or increases the robustness of the system by not relying on pressure measurements. MSS is an example of data-driven control and can be applied to a broad class of nonlinear control......Refrigeration is used in a wide range of applications, e.g., for storage of food at low temperatures to prolong shelf life and in air conditioning for occupancy comfort. The main focus of this thesis is control of supermarket refrigeration systems. This market is very competitive...... and it is important to keep the variable costs at a minimum and, if possible, offer products which have higher robustness, performance, and functionality than similar products from competitors. However, the multitude of different system configurations, system complexity, component wear, and changing operating...

  7. Data-driven workflows for microservices

    DEFF Research Database (Denmark)

    Safina, Larisa; Mazzara, Manuel; Montesi, Fabrizio

    2016-01-01

    Microservices is an architectural style inspired by service-oriented computing that has recently started gainingpopularity. Jolie is a programming language based on the microservices paradigm: the main building block of Jolie systems are services, in contrast to, e.g., functions or objects....... The primitives offered by the Jolie language elicit many of the recurring patterns found in microservices, like load balancers and structured processes. However, Jolie still lacks some useful constructs for dealing with message types and data manipulation that are present in service-oriented computing......). We show the impact of our implementation on some of the typical scenarios found in microservice systems. This shows how computation can move from a process-driven to a data-driven approach, and leads to the preliminary identification of recurring communication patterns that can be shaped as design...

  8. Data-Driven Security-Constrained OPF

    DEFF Research Database (Denmark)

    Thams, Florian; Halilbasic, Lejla; Pinson, Pierre

    2017-01-01

    In this paper we unify electricity market operations with power system security considerations. Using data-driven techniques, we address both small signal stability and steady-state security, derive tractable decision rules in the form of line flow limits, and incorporate the resulting constraints......, both from measurements and simulations, in order to determine stable and unstable operating regions. With the help of decision trees, we transform this information to linear decision rules for line flow constraints. We propose conditional line transfer limits, which can accurately capture security...... considerations, while being less conservative than current approaches. Our approach can be scalable for large systems, accounts explicitly for power system security, and enables the electricity market to identify a cost-efficient dispatch avoiding redispatching actions. We demonstrate the performance of our...

  9. Data driven innovations in structural health monitoring

    Science.gov (United States)

    Rosales, M. J.; Liyanapathirana, R.

    2017-05-01

    At present, substantial investments are being allocated to civil infrastructures also considered as valuable assets at a national or global scale. Structural Health Monitoring (SHM) is an indispensable tool required to ensure the performance and safety of these structures based on measured response parameters. The research to date on damage assessment has tended to focus on the utilization of wireless sensor networks (WSN) as it proves to be the best alternative over the traditional visual inspections and tethered or wired counterparts. Over the last decade, the structural health and behaviour of innumerable infrastructure has been measured and evaluated owing to several successful ventures of implementing these sensor networks. Various monitoring systems have the capability to rapidly transmit, measure, and store large capacities of data. The amount of data collected from these networks have eventually been unmanageable which paved the way to other relevant issues such as data quality, relevance, re-use, and decision support. There is an increasing need to integrate new technologies in order to automate the evaluation processes as well as to enhance the objectivity of data assessment routines. This paper aims to identify feasible methodologies towards the application of time-series analysis techniques to judiciously exploit the vast amount of readily available as well as the upcoming data resources. It continues the momentum of a greater effort to collect and archive SHM approaches that will serve as data-driven innovations for the assessment of damage through efficient algorithms and data analytics.

  10. Data-driven parameterization of the generalized Langevin equation.

    Science.gov (United States)

    Lei, Huan; Baker, Nathan A; Li, Xiantao

    2016-12-13

    We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method.

  11. Data-driven parameterization of the generalized Langevin equation

    CERN Document Server

    Lei, Huan; Li, Xiantao

    2016-01-01

    We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method.

  12. Asynchronous networks and event driven dynamics

    Science.gov (United States)

    Bick, Christian; Field, Michael

    2017-02-01

    Real-world networks in technology, engineering and biology often exhibit dynamics that cannot be adequately reproduced using network models given by smooth dynamical systems and a fixed network topology. Asynchronous networks give a theoretical and conceptual framework for the study of network dynamics where nodes can evolve independently of one another, be constrained, stop, and later restart, and where the interaction between different components of the network may depend on time, state, and stochastic effects. This framework is sufficiently general to encompass a wide range of applications ranging from engineering to neuroscience. Typically, dynamics is piecewise smooth and there are relationships with Filippov systems. In this paper, we give examples of asynchronous networks, and describe the basic formalism and structure. In the following companion paper, we make the notion of a functional asynchronous network rigorous, discuss the phenomenon of dynamical locks, and present a foundational result on the spatiotemporal factorization of the dynamics for a large class of functional asynchronous networks.

  13. High dimensional data driven statistical mechanics.

    Science.gov (United States)

    Adachi, Yoshitaka; Sadamatsu, Sunao

    2014-11-01

    In "3D4D materials science", there are five categories such as (a) Image acquisition, (b) Processing, (c) Analysis, (d) Modelling, and (e) Data sharing. This presentation highlights the core of these categories [1]. Analysis and modellingA three-dimensional (3D) microstructure image contains topological features such as connectivity in addition to metric features. Such more microstructural information seems to be useful for more precise property prediction. There are two ways for microstructure-based property prediction (Fig. 1A). One is 3D image data based modelling such as micromechanics or crystal plasticity finite element method. The other one is a numerical microstructural features driven machine learning approach such as artificial neural network or Bayesian estimation method. It is the key to convert the 3D image data into numerals in order to apply the dataset to property prediction. As a numerical feature of microstructures, grain size, number of density, of particles, connectivity of particles, grain boundary connectivity, stacking degree, clustering etc. should be taken into consideration. These microstructural features are so-called "materials genome". Among those materials genome, we have to find out dominant factors to determine a focused property. The dominant factorzs are defined as "descriptor(s)" in high dimensional data driven statistical mechanics.jmicro;63/suppl_1/i4/DFU086F1F1DFU086F1Fig. 1.(a) A concept of 3D4D materials science. (b) Fully-automated serial sectioning 3D microscope "Genus_3D". (c) Materials Genome Archive (JSPS). Image acquisitionIt is important for researchers to choice a 3D microscope from various microscopes depending on a length-scale of a focused microstructure. There is a long term request to acquire a 3D microstructure image more conveniently. Therefore a fully automated serial sectioning 3D optical microscope "Genus_3D" (Fig. 1B) has been developed and nowadays it is commercially available. A user can get a good

  14. Data-Driven Techniques for Regional Groundwater Level Forecasts

    Science.gov (United States)

    Chang, F. J.; Chang, L. C.; Tsai, F. H.; Shen, H. Y.

    2015-12-01

    Data-Driven Techniques for Regional Groundwater Level Forecasts Fi-John Changa, Li-Chiu Changb, Fong He Tsaia, Hung-Yu Shenba Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC. b Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan, ROC..Correspondence to: Fi-John Chang (email: changfj@ntu.edu.tw)The alluvial fan of the Zhuoshui River in Taiwan is a good natural recharge area of groundwater. However, the over extraction of groundwater occurs in the coastland results in serious land subsidence. Groundwater systems are heterogeneous with diverse temporal-spatial patterns, and it is very difficult to quantify their complex processes. Data-driven methods can effectively capture the spatial-temporal characteristics of input-output patterns at different scales for accurately imitating dynamic complex systems with less computational requirements. In this study, we implement various data-driven methods to suitably predict the regional groundwater level variations for making countermeasures in response to the land subsidence issue in the study area. We first establish the relationship between regional rainfall, streamflow as well as groundwater levels and then construct intelligent groundwater level prediction models for the basin based on the long-term (2000-2013) regional monthly data sets collected from the Zhuoshui River basin. We analyze the interaction between hydrological factors and groundwater level variations; apply the self-organizing map (SOM) to obtain the clustering results of the spatial-temporal groundwater level variations; and then apply the recurrent configuration of nonlinear autoregressive with exogenous inputs (R-NARX) to predicting the monthly groundwater levels. As a consequence, a regional intelligent groundwater level prediction model can be constructed based on the adaptive results of the SOM. Results demonstrate that the development

  15. Safety analysis of proposed data-driven physiologic alarm parameters for hospitalized children.

    Science.gov (United States)

    Goel, Veena V; Poole, Sarah F; Longhurst, Christopher A; Platchek, Terry S; Pageler, Natalie M; Sharek, Paul J; Palma, Jonathan P

    2016-12-01

    Modification of alarm limits is one approach to mitigating alarm fatigue. We aimed to create and validate heart rate (HR) and respiratory rate (RR) percentiles for hospitalized children, and analyze the safety of replacing current vital sign reference ranges with proposed data-driven, age-stratified 5th and 95th percentile values. In this retrospective cross-sectional study, nurse-charted HR and RR data from a training set of 7202 hospitalized children were used to develop percentile tables. We compared 5th and 95th percentile values with currently accepted reference ranges in a validation set of 2287 patients. We analyzed 148 rapid response team (RRT) and cardiorespiratory arrest (CRA) events over a 12-month period, using HR and RR values in the 12 hours prior to the event, to determine the proportion of patients with out-of-range vitals based upon reference versus data-driven limits. There were 24,045 (55.6%) fewer out-of-range measurements using data-driven vital sign limits. Overall, 144/148 RRT and CRA patients had out-of-range HR or RR values preceding the event using current limits, and 138/148 were abnormal using data-driven limits. Chart review of RRT and CRA patients with abnormal HR and RR per current limits considered normal by data-driven limits revealed that clinical status change was identified by other vital sign abnormalities or clinical context. A large proportion of vital signs in hospitalized children are outside presently used norms. Safety evaluation of data-driven limits suggests they are as safe as those currently used. Implementation of these parameters in physiologic monitors may mitigate alarm fatigue. Journal of Hospital Medicine 2015;11:817-823. © 2015 Society of Hospital Medicine. © 2016 Society of Hospital Medicine.

  16. Dynamic Boundaries of Event Horizon Magnetospheres

    OpenAIRE

    Punsly, Brian

    2007-01-01

    This Letter analyzes 3-dimensional simulations of Kerr black hole magnetospheres that obey the general relativistic equations of perfect magnetohydrodynamics (MHD). Particular emphasis is on the event horizon magnetosphere (EHM) which is defined as the the large scale poloidal magnetic flux that threads the event horizon of a black hole (This is distinct from the poloidal magnetic flux that threads the equatorial plane of the ergosphere, which forms the ergospheric disk magnetosphere). Standa...

  17. Data-Driven Hint Generation from Peer Debugging Solutions

    Science.gov (United States)

    Liu, Zhongxiu

    2015-01-01

    Data-driven methods have been a successful approach to generating hints for programming problems. However, the majority of previous studies are focused on procedural hints that aim at moving students to the next closest state to the solution. In this paper, I propose a data-driven method to generate remedy hints for BOTS, a game that teaches…

  18. Data-driven design of fault diagnosis and fault-tolerant control systems

    CERN Document Server

    Ding, Steven X

    2014-01-01

    Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods, and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and...

  19. A Data-Driven Air Transportation Delay Propagation Model Using Epidemic Process Models

    Directory of Open Access Journals (Sweden)

    B. Baspinar

    2016-01-01

    Full Text Available In air transport network management, in addition to defining the performance behavior of the system’s components, identification of their interaction dynamics is a delicate issue in both strategic and tactical decision-making process so as to decide which elements of the system are “controlled” and how. This paper introduces a novel delay propagation model utilizing epidemic spreading process, which enables the definition of novel performance indicators and interaction rates of the elements of the air transportation network. In order to understand the behavior of the delay propagation over the network at different levels, we have constructed two different data-driven epidemic models approximating the dynamics of the system: (a flight-based epidemic model and (b airport-based epidemic model. The flight-based epidemic model utilizing SIS epidemic model focuses on the individual flights where each flight can be in susceptible or infected states. The airport-centric epidemic model, in addition to the flight-to-flight interactions, allows us to define the collective behavior of the airports, which are modeled as metapopulations. In network model construction, we have utilized historical flight-track data of Europe and performed analysis for certain days involving certain disturbances. Through this effort, we have validated the proposed delay propagation models under disruptive events.

  20. Data-driven discovery of partial differential equations

    Science.gov (United States)

    Rudy, Samuel; Brunton, Steven; Proctor, Joshua; Kutz, J. Nathan

    2016-11-01

    Fluid dynamics is inherently governed by spatial-temporal interactions which can be characterized by partial differential equations (PDEs). Emerging sensor and measurement technologies allowing for rich, time-series data collection motivate new data-driven methods for discovering governing equations. We present a novel computational technique for discovering governing PDEs from time series measurements. A library of candidate terms for the PDE including nonlinearities and partial derivatives is computed and sparse regression is then used to identify a subset which accurately reflects the measured dynamics. Measurements may be taken either in a Eulerian framework to discover field equations or in a Lagrangian framework to study a single stochastic trajectory. The method is shown to be robust, efficient, and to work on a variety of canonical equations. Data collected from a simulation of a flow field around a cylinder is used to accurately identify the Navier-Stokes vorticity equation and the Reynolds number to within 1%. A single trace of Brownian motion is also used to identify the diffusion equation. Our method provides a novel approach towards data enabled science where spatial-temporal information bolsters classical machine learning techniques to identify physical laws.

  1. Dynamic boundaries of event horizon magnetospheres

    Science.gov (United States)

    Punsly, Brian

    2007-10-01

    This Letter analyses three-dimensional (3D) simulations of Kerr black hole magnetospheres that obey the general relativistic equations of perfect magnetohydrodynamics (MHD). Particular emphasis is on the event horizon magnetosphere (EHM) which is defined as the the large-scale poloidal magnetic flux that threads the event horizon of a black hole. (This is distinct from the poloidal magnetic flux that threads the equatorial plane of the ergosphere, which forms the ergospheric disc magnetosphere.) Standard MHD theoretical treatments of Poynting jets in the EHM are predicated on the assumption that the plasma comprising the boundaries of the EHM plays no role in producing the Poynting flux. The energy flux is electrodynamic in origin and it is essentially conserved from the horizon to infinity; this is known as the Blandford-Znajek (B-Z) mechanism. In contrast, within the 3D simulations, the lateral boundaries are strong pistons for MHD waves and actually inject prodigious quantities of Poynting flux into the EHM. At high black hole spin rates, strong sources of Poynting flux adjacent to the EHM from the ergospheric disc will actually diffuse to higher latitudes and swamp any putative B-Z effects. This is in contrast to lower spin rates, which are characterized by much lower output powers, and where modest amounts of Poynting flux are injected into the EHM from the accretion disc corona.

  2. Alternating event processes during lifetimes: population dynamics and statistical inference.

    Science.gov (United States)

    Shinohara, Russell T; Sun, Yifei; Wang, Mei-Cheng

    2017-08-07

    In the literature studying recurrent event data, a large amount of work has been focused on univariate recurrent event processes where the occurrence of each event is treated as a single point in time. There are many applications, however, in which univariate recurrent events are insufficient to characterize the feature of the process because patients experience nontrivial durations associated with each event. This results in an alternating event process where the disease status of a patient alternates between exacerbations and remissions. In this paper, we consider the dynamics of a chronic disease and its associated exacerbation-remission process over two time scales: calendar time and time-since-onset. In particular, over calendar time, we explore population dynamics and the relationship between incidence, prevalence and duration for such alternating event processes. We provide nonparametric estimation techniques for characteristic quantities of the process. In some settings, exacerbation processes are observed from an onset time until death; to account for the relationship between the survival and alternating event processes, nonparametric approaches are developed for estimating exacerbation process over lifetime. By understanding the population dynamics and within-process structure, the paper provide a new and general way to study alternating event processes.

  3. Realistic Data-Driven Traffic Flow Animation Using Texture Synthesis.

    Science.gov (United States)

    Chao, Qianwen; Deng, Zhigang; Ren, Jiaping; Ye, Qianqian; Jin, Xiaogang

    2017-01-11

    We present a novel data-driven approach to populate virtual road networks with realistic traffic flows. Specifically, given a limited set of vehicle trajectories as the input samples, our approach first synthesizes a large set of vehicle trajectories. By taking the spatio-temporal information of traffic flows as a 2D texture, the generation of new traffic flows can be formulated as a texture synthesis process, which is solved by minimizing a newly developed traffic texture energy. The synthesized output captures the spatio-temporal dynamics of the input traffic flows, and the vehicle interactions in it strictly follow traffic rules. After that, we position the synthesized vehicle trajectory data to virtual road networks using a cage-based registration scheme, where a few traffic-specific constraints are enforced to maintain each vehicle's original spatial location and synchronize its motion in concert with its neighboring vehicles. Our approach is intuitive to control and scalable to the complexity of virtual road networks. We validated our approach through many experiments and paired comparison user studies.

  4. Mobile assessment in schizophrenia: a data-driven momentary approach.

    Science.gov (United States)

    Oorschot, Margreet; Lataster, Tineke; Thewissen, Viviane; Wichers, Marieke; Myin-Germeys, Inez

    2012-05-01

    In this article, a data-driven approach was adopted to demonstrate how real-life diary techniques [ie, the experience sampling method (ESM)] could be deployed for assessment purposes in patients with psychotic disorder, delivering individualized and clinically relevant information. The dataset included patients in an acute phase of psychosis and the focus was on paranoia as one of the main psychotic symptoms (30 patients with high levels of paranoia and 34 with low levels of paranoia). Based on individual cases, it was demonstrated how (1) symptom and mood patterns, (2) patterns of social interactions or activities, (3) contextual risk profiles (eg, is being among strangers, as opposed to family, associated with higher paranoia severity?), and (4) temporal dynamics between mood states and paranoia (eg, does anxiety precipitate or follow the onset of increased paranoia severity?) substantially differ within individual patients and across the high vs low paranoid patient group. Most striking, it was shown that individual findings are different from what is found on overall group levels. Some people stay anxious after a paranoid thought came to mind. For others, paranoia is followed by a state of relaxation. It is discussed how ESM, surfacing the patient's implicit knowledge about symptom patterns, may provide an excellent starting point for person-tailored psychoeducation and for choosing the most applicable therapeutic intervention.

  5. Human body segmentation via data-driven graph cut.

    Science.gov (United States)

    Li, Shifeng; Lu, Huchuan; Shao, Xingqing

    2014-11-01

    Human body segmentation is a challenging and important problem in computer vision. Existing methods usually entail a time-consuming training phase for prior knowledge learning with complex shape matching for body segmentation. In this paper, we propose a data-driven method that integrates top-down body pose information and bottom-up low-level visual cues for segmenting humans in static images within the graph cut framework. The key idea of our approach is first to exploit human kinematics to search for body part candidates via dynamic programming for high-level evidence. Then, by using the body parts classifiers, obtaining bottom-up cues of human body distribution for low-level evidence. All the evidence collected from top-down and bottom-up procedures are integrated in a graph cut framework for human body segmentation. Qualitative and quantitative experiment results demonstrate the merits of the proposed method in segmenting human bodies with arbitrary poses from cluttered backgrounds.

  6. Data-driven non-Markovian closure models

    Science.gov (United States)

    Kondrashov, Dmitri; Chekroun, Mickaël D.; Ghil, Michael

    2015-03-01

    This paper has two interrelated foci: (i) obtaining stable and efficient data-driven closure models by using a multivariate time series of partial observations from a large-dimensional system; and (ii) comparing these closure models with the optimal closures predicted by the Mori-Zwanzig (MZ) formalism of statistical physics. Multilayer stochastic models (MSMs) are introduced as both a generalization and a time-continuous limit of existing multilevel, regression-based approaches to closure in a data-driven setting; these approaches include empirical model reduction (EMR), as well as more recent multi-layer modeling. It is shown that the multilayer structure of MSMs can provide a natural Markov approximation to the generalized Langevin equation (GLE) of the MZ formalism. A simple correlation-based stopping criterion for an EMR-MSM model is derived to assess how well it approximates the GLE solution. Sufficient conditions are derived on the structure of the nonlinear cross-interactions between the constitutive layers of a given MSM to guarantee the existence of a global random attractor. This existence ensures that no blow-up can occur for a broad class of MSM applications, a class that includes non-polynomial predictors and nonlinearities that do not necessarily preserve quadratic energy invariants. The EMR-MSM methodology is first applied to a conceptual, nonlinear, stochastic climate model of coupled slow and fast variables, in which only slow variables are observed. It is shown that the resulting closure model with energy-conserving nonlinearities efficiently captures the main statistical features of the slow variables, even when there is no formal scale separation and the fast variables are quite energetic. Second, an MSM is shown to successfully reproduce the statistics of a partially observed, generalized Lotka-Volterra model of population dynamics in its chaotic regime. The challenges here include the rarity of strange attractors in the model's parameter

  7. Construction, Analysis, and Data-Driven Augmentation of Supersaturated Designs

    Science.gov (United States)

    2013-09-01

    CONSTRUCTION, ANALYSIS, AND DATA-DRIVEN AUGMENTATION OF SUPERSATURATED DESIGNS DISSERTATION Alex J. Gutman, AFIT-ENC-DS-13-S-02 DEPARTMENT OF THE AIR...DRIVEN AUGMENTATION OF SUPERSATURATED DESIGNS DISSERTATION Presented to the Faculty Graduate School of Engineering and Management Air Force Institute...13-S-02 CONSTRUCTION, ANALYSIS, AND DATA-DRIVEN AUGMENTATION OF SUPERSATURATED DESIGNS Alex J. Gutman, BS, MS Approved: //signed// September 2013

  8. Dynamics of three anomalous SST events in the Coral Sea

    Science.gov (United States)

    Schiller, A.; Ridgway, K. R.; Steinberg, C. R.; Oke, P. R.

    2009-03-01

    Variability of the circulation in the Coral Sea, accompanied by large heat transport anomalies, has the potential to have detrimental impacts on underlying ecosystems, including the Great Barrier Reef. In this study we analyze the dynamics of three events, characterized by extremes in sea-surface temperature, as simulated in an eddy-resolving ocean reanalysis. We show that a cooling in April 1997 results from strong wind anomalies and is supported by vertical and horizontal advective heat losses. A warm event in October 1998 is attributable to a heat gain by horizontal advection. A heat budget of the mixed-layer within a closed box shows that warm anomalies in January 2002 involve a quasi-balance between horizontal advection and vertical entrainment with a large local heat gain through the ocean surface near-shore that apparently caused a coral bleaching event. The dynamics of these extreme events are all quite different, with both local and remote influences.

  9. Demo Abstract: Toward Data-driven Demand-Response Optimization in a Campus Microgrid

    Energy Technology Data Exchange (ETDEWEB)

    Amam, Saima; Natarajan, Sreedhar; Yin, Wei; Zhou, Qunzhi; Simmhan, Yogesh; Prasanna, Viktor

    2011-11-01

    We describe and demonstrate a prototype software architecture to support data-driven demand response optimization (DR) in the USC campus microgrid, as part of the Los Angeles Smart Grid Demonstration Project. The architecture includes a semantic information repository that integrates diverse data sources to support DR, demand forecasting using scalable machine-learned models, and detection of load curtailment opportunities by matching complex event patterns.

  10. Dynamic Event Tree advancements and control logic improvements

    Energy Technology Data Exchange (ETDEWEB)

    Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Sen, Ramazan Sonat [Idaho National Lab. (INL), Idaho Falls, ID (United States); Cogliati, Joshua Joseph [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2015-09-01

    The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees). The main subject of this document is to report the activities that have been done in order to: start the migration of the RAVEN/RELAP-7 control logic system into MOOSE, and develop advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year an initial migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework. In this document, a brief explanation of what has been done is going to be reported. The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named “Hybrid Dynamic Event Tree” (HDET) and its Adaptive variant “Adaptive Hybrid Dynamic Event Tree” (AHDET). As other authors have already reported, among the different types of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event Tree is in charge of treating the first class (aleatory) uncertainties; the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the

  11. Design Tools for Dynamic, Data-Driven, Stream Mining Systems

    Science.gov (United States)

    2015-01-01

    vertexes (actors) represent computa- tional functions of arbitrary complexity, and edges represent first-in-first-out ( FIFO ) buffers that store data values...different kinds of FIFO implementations, basic signal processing actors, and actors for interfac- ing with input and output files. Using LIDE...for our SVM actor implementation. The first four function parameters, input_data, input_svs, input_alphas, and output_class, correspond to the FIFOs

  12. Dynamic Data Driven Operator Error Early Warning System

    Science.gov (United States)

    2015-08-13

    integrating data from skin conductance and heart rate to monitor emotion and behavior for clinical use. Cognitive Behavioral Therapy could be given...system. Journal of Cognitive Engineering and Decision Making, page 1555343412441001, 2012. [10] Paul Ekman , Robert W. Levenson, and Wallace V. Friesen...Autonomic Nervous System Activity Distinguishes among Emotions . Science, 221(4616):1208–1210, 1983. [11] Richard Ribón Fletcher, Sharon Tam, Olufemi

  13. Stable Event-Driven Particle Dynamics: Spherically Symmetric Potentials

    CERN Document Server

    Bannerman, Marcus N

    2012-01-01

    Event-Driven Particle Dynamics is a fast and precise method to simulate particulate systems of all scales. These advantages arise from the analytical solution of the dynamics required by the discrete-potential models used. Despite the high precision solution, the finite calculation-precision of computers will still cause the simulation to enter invalid states which, if left unchecked, can lead to unresolvable errors. In this work, the treatment of these marginal invalid-states is discussed and a general event-detection algorithm is proposed which stably handles these situations. This requires a definition of the dynamics of invalid states and leads to improved algorithms for event-detection in spherically symmetric systems, including the well-established hard-sphere and square-well models. Finally, the Event-Driven Particle Dynamics technique is extended to allow the study of systems with complex spherical-mesh boundary conditions and distance constraints as a demonstration of the generality of the proposed a...

  14. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

    Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  15. Severe accident analysis using dynamic accident progression event trees

    Science.gov (United States)

    Hakobyan, Aram P.

    In present, the development and analysis of Accident Progression Event Trees (APETs) are performed in a manner that is computationally time consuming, difficult to reproduce and also can be phenomenologically inconsistent. One of the principal deficiencies lies in the static nature of conventional APETs. In the conventional event tree techniques, the sequence of events is pre-determined in a fixed order based on the expert judgments. The main objective of this PhD dissertation was to develop a software tool (ADAPT) for automated APET generation using the concept of dynamic event trees. As implied by the name, in dynamic event trees the order and timing of events are determined by the progression of the accident. The tool determines the branching times from a severe accident analysis code based on user specified criteria for branching. It assigns user specified probabilities to every branch, tracks the total branch probability, and truncates branches based on the given pruning/truncation rules to avoid an unmanageable number of scenarios. The function of a dynamic APET developed includes prediction of the conditions, timing, and location of containment failure or bypass leading to the release of radioactive material, and calculation of probabilities of those failures. Thus, scenarios that can potentially lead to early containment failure or bypass, such as through accident induced failure of steam generator tubes, are of particular interest. Also, the work is focused on treatment of uncertainties in severe accident phenomena such as creep rupture of major RCS components, hydrogen burn, containment failure, timing of power recovery, etc. Although the ADAPT methodology (Analysis of Dynamic Accident Progression Trees) could be applied to any severe accident analysis code, in this dissertation the approach is demonstrated by applying it to the MELCOR code [1]. A case study is presented involving station blackout with the loss of auxiliary feedwater system for a

  16. Data-Driven Learning: Reasonable Fears and Rational Reassurance

    Science.gov (United States)

    Boulton, Alex

    2009-01-01

    Computer corpora have many potential applications in teaching and learning languages, the most direct of which--when the learners explore a corpus themselves--has become known as data-driven learning (DDL). Despite considerable enthusiasm in the research community and interest in higher education, the approach has not made major inroads to…

  17. Data-driven importance distributions for articulated tracking

    DEFF Research Database (Denmark)

    Hauberg, Søren; Pedersen, Kim Steenstrup

    2011-01-01

    We present two data-driven importance distributions for particle filterbased articulated tracking; one based on background subtraction, another on depth information. In order to keep the algorithms efficient, we represent human poses in terms of spatial joint positions. To ensure constant bone...

  18. Social Capital in Data-Driven Community College Reform

    Science.gov (United States)

    Kerrigan, Monica Reid

    2015-01-01

    The current rhetoric around using data to improve community college student outcomes with only limited research on data-driven decision-making (DDDM) within postsecondary education compels a more comprehensive understanding of colleges' capacity for using data to inform decisions. Based on an analysis of faculty and administrators' perceptions and…

  19. A Statistical Quality Model for Data-Driven Speech Animation.

    Science.gov (United States)

    Ma, Xiaohan; Deng, Zhigang

    2012-11-01

    In recent years, data-driven speech animation approaches have achieved significant successes in terms of animation quality. However, how to automatically evaluate the realism of novel synthesized speech animations has been an important yet unsolved research problem. In this paper, we propose a novel statistical model (called SAQP) to automatically predict the quality of on-the-fly synthesized speech animations by various data-driven techniques. Its essential idea is to construct a phoneme-based, Speech Animation Trajectory Fitting (SATF) metric to describe speech animation synthesis errors and then build a statistical regression model to learn the association between the obtained SATF metric and the objective speech animation synthesis quality. Through delicately designed user studies, we evaluate the effectiveness and robustness of the proposed SAQP model. To the best of our knowledge, this work is the first-of-its-kind, quantitative quality model for data-driven speech animation. We believe it is the important first step to remove a critical technical barrier for applying data-driven speech animation techniques to numerous online or interactive talking avatar applications.

  20. Data Driven Decision Making in the Social Studies

    Science.gov (United States)

    Ediger, Marlow

    2010-01-01

    Data driven decision making emphasizes the importance of the teacher using objective sources of information in developing the social studies curriculum. Too frequently, decisions of teachers have been made based on routine and outdated methods of teaching. Valid and reliable tests used to secure results from pupil learning make for better…

  1. Data-driven services marketing in a connected world

    NARCIS (Netherlands)

    Kumar, V.; Chattaraman, Veena; Neghina, Carmen; Skiera, Bernd; Aksoy, Lerzan; Buoye, Alexander; Henseler, Joerg

    2013-01-01

    Purpose – The purpose of this paper is to provide insights into the benefits of data-driven services marketing and provide a conceptual framework for how to link traditional and new sources of customer data and their metrics. Linking data and metrics to strategic and tactical business insights and i

  2. Data mining, knowledge discovery and data-driven modelling

    NARCIS (Netherlands)

    Solomatine, D.P.; Velickov, S.; Bhattacharya, B.; Van der Wal, B.

    2003-01-01

    The project was aimed at exploring the possibilities of a new paradigm in modelling - data-driven modelling, often referred as "data mining". Several application areas were considered: sedimentation problems in the Port of Rotterdam, automatic soil classification on the basis of cone penetration tes

  3. Social Capital in Data-Driven Community College Reform

    Science.gov (United States)

    Kerrigan, Monica Reid

    2015-01-01

    The current rhetoric around using data to improve community college student outcomes with only limited research on data-driven decision-making (DDDM) within postsecondary education compels a more comprehensive understanding of colleges' capacity for using data to inform decisions. Based on an analysis of faculty and administrators' perceptions and…

  4. Data-Driven Learning of Q-Matrix

    Science.gov (United States)

    Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

    2012-01-01

    The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

  5. Data-Driven Planning: Using Assessment in Strategic Planning

    Science.gov (United States)

    Bresciani, Marilee J.

    2010-01-01

    Data-driven planning or evidence-based decision making represents nothing new in its concept. For years, business leaders have claimed they have implemented planning informed by data that have been strategically and systematically gathered. Within higher education and student affairs, there may be less evidence of the actual practice of…

  6. Data-Driven Decision Making in Auction Markets

    NARCIS (Netherlands)

    Y. Lu (Yixin)

    2014-01-01

    markdownabstract__Abstract__ This dissertation consists of three essays that examine the promises of data-driven decision making in the design and operationalization of complex auction markets. In the first essay, we derive a structural econometric model to understand the effect of auction design

  7. Data-Driven Model Order Reduction for Bayesian Inverse Problems

    KAUST Repository

    Cui, Tiangang

    2014-01-06

    One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.

  8. RAVEN: Dynamic Event Tree Approach Level III Milestone

    Energy Technology Data Exchange (ETDEWEB)

    Andrea Alfonsi; Cristian Rabiti; Diego Mandelli; Joshua Cogliati; Robert Kinoshita

    2013-07-01

    Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented.

  9. RAVEN. Dynamic Event Tree Approach Level III Milestone

    Energy Technology Data Exchange (ETDEWEB)

    Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Cogliati, Joshua [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kinoshita, Robert [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2014-07-01

    Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented.

  10. The glaciology of IRD events: warming and ice dynamics

    Science.gov (United States)

    Hindmarsh, R. C. A.

    2003-04-01

    Heinrich events, the enormous glacial-period ice-rafting episodeshave been posited to be due to large-scale surges of the Laurentide ice-sheet (3). However, more frequent events such as the Bond events are difficult to explain this way. Recently acquired geological evidence (2,4) suggests that climatic perturbations are correlated with some N. Atlantic IRD events. A model (1) which show how climate perturbations can lead to IRD events is reviewed. The model shows how 20-50km retreats induced by ablation rates of 2 m/yr provide sufficient debris flux through the grounding line to produce large sedimentation events. Such ablation would reduce ice-shelf extent markedly, permitting debris to reach the calving front and be transported by icebergs leading to ice-rafted debris (IRD) events. Surges are not necessary conditions for the production of large IRD events. The glacial dynamics of this climate perturbation model is compared with the surge theory, with particular emphasis on the amount of sediment that either method can deliver to the oceans. Consideration of the non-exclusivety and consistency of the two mechanisms is emphasised. (1) R.C.A. Hindmarsh and A. Jenkins, Centurial-millenial ice-rafted debris pulses from ablating marine ice sheets, Geophys Res. Lett 22(12), 2477-2480, 2001; (2) Paul C. Knutz et al. G3 Multidecadal ocean variability and NW European ice sheet surges during the last deglaciation G3 3(12) 17 December 2002 1077, doi:10.1029/2002GC000351; (3) MacAyeal,D.R. Binge/purge oscillations of the Laurentide ice-sheet as a cause of the North-Atlantic's Heinrich events, Paleoceanography, 8(6), p.775-784, (1993); (4) M. Moros, et. al. Were glacial iceberg surges in the North Atlantic triggered by climatic warming?, Marine Geology, 192(4), 2002, p.393-417

  11. CYCLE TIMES ASSIGNMENT OF NONLINEAR DISCRETE EVENT DYNAMIC SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    CHEN Wende

    2000-01-01

    In this paper, nonautonomous models of Discrete Event Dynamic Systems (DEDS) are established by min-max function, reachability and observability are defined,the problem on cycle times assignment of DEDS, which corresponds with the important problem on poles assignment of linear systems, is studied. By Gunawardena et al.'Duality Theorem following results are obtained: Cycle times of system can be assigned under state feedback(or output feedback) if and only if system is reachable (or reachable and obserbable).

  12. The Heliophysics Event Knowledgebase for the Solar Dynamics Observatory

    Science.gov (United States)

    Hurlburt, Neal E.; Cheung, M.; Schrijver, K.; HEK development Team

    2009-05-01

    The Solar Dynamics Observatory will generated over 2 petabytes of imagery in its 5 year mission. In order to improve scientific productivity and to reduce system requirements , we have developed a system for data markup to identify "interesting” datasets and direct scientists to them through an event-based querying system. The SDO Heliophysics Event Knowledgebase (HEK) will enable caching of commonly accessed datasets within the Joint Science Operations Center (JSOC) and reduces the (human) time spent searching for and downloading relevant data. We present an overview of our HEK including the ingestion of images, automated and manual tools for identifying and annotation features within the images, and interfaces and web tools for querying and accessing events and their associated data.

  13. Toward data-driven methods in geophysics: the Analog Data Assimilation

    Science.gov (United States)

    Lguensat, Redouane; Tandeo, Pierre; Ailliot, Pierre; Pulido, Manuel; Fablet, Ronan

    2017-04-01

    The Analog Data Assimilation (AnDA) is a recently introduced data-driven methods for data assimilation where the dynamical model is learned from data, contrary to classical data assimilation where a physical model of the dynamics is needed. AnDA relies on replacing the physical dynamical model by a statistical emulator of the dynamics using analog forecasting methods. Then, the analog dynamical model is incorporated in ensemble-based data assimilation algorithms (Ensemble Kalman Filter and Smoother or Particle Filter). The relevance of the proposed AnDA is demonstrated for Lorenz-63 and Lorenz-96 chaotic dynamics. Applications in meteorology and oceanography as well as potential perspectives that are worthy of investigation are further discussed. We expect that the directions of research we suggest will help in bringing more interest in applied machine learning to geophysical sciences.

  14. Turbulence Model Discovery with Data-Driven Learning and Optimization

    Science.gov (United States)

    King, Ryan; Hamlington, Peter

    2016-11-01

    Data-driven techniques have emerged as a useful tool for model development in applications where first-principles approaches are intractable. In this talk, data-driven multi-task learning techniques are used to discover flow-specific optimal turbulence closure models. We use the recently introduced autonomic closure technique to pose an online supervised learning problem created by test filtering turbulent flows in the self-similar inertial range. The autonomic closure is modified to solve the learning problem for all stress components simultaneously with multi-task learning techniques. The closure is further augmented with a feature extraction step that learns a set of orthogonal modes that are optimal at predicting the turbulent stresses. We demonstrate that these modes can be severely truncated to enable drastic reductions in computational costs without compromising the model accuracy. Furthermore, we discuss the potential universality of the extracted features and implications for reduced order modeling of other turbulent flows.

  15. Data-driven approaches in the investigation of social perception.

    Science.gov (United States)

    Adolphs, Ralph; Nummenmaa, Lauri; Todorov, Alexander; Haxby, James V

    2016-05-05

    The complexity of social perception poses a challenge to traditional approaches to understand its psychological and neurobiological underpinnings. Data-driven methods are particularly well suited to tackling the often high-dimensional nature of stimulus spaces and of neural representations that characterize social perception. Such methods are more exploratory, capitalize on rich and large datasets, and attempt to discover patterns often without strict hypothesis testing. We present four case studies here: behavioural studies on face judgements, two neuroimaging studies of movies, and eyetracking studies in autism. We conclude with suggestions for particular topics that seem ripe for data-driven approaches, as well as caveats and limitations. © 2016 The Author(s).

  16. Undersampled MR Image Reconstruction with Data-Driven Tight Frame

    Directory of Open Access Journals (Sweden)

    Jianbo Liu

    2015-01-01

    Full Text Available Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

  17. Undersampled MR Image Reconstruction with Data-Driven Tight Frame.

    Science.gov (United States)

    Liu, Jianbo; Wang, Shanshan; Peng, Xi; Liang, Dong

    2015-01-01

    Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

  18. Data-Driven Controller Design The H2 Approach

    CERN Document Server

    Sanfelice Bazanella, Alexandre; Eckhard, Diego

    2012-01-01

    Data-driven methodologies have recently emerged as an important paradigm alternative to model-based controller design and several such methodologies are formulated as an H2 performance optimization. This book presents a comprehensive theoretical treatment of the H2 approach to data-driven control design. The fundamental properties implied by the H2 problem formulation are analyzed in detail, so that common features to all solutions are identified. Direct methods (VRFT) and iterative methods (IFT, DFT, CbT) are put under a common theoretical framework. The choice of the reference model, the experimental conditions, the optimization method to be used, and several other designer’s choices are crucial to the quality of the final outcome, and firm guidelines for all these choices are derived from the theoretical analysis presented. The practical application of the concepts in the book is illustrated with a large number of practical designs performed for different classes of processes: thermal, fluid processing a...

  19. A data-driven framework for investigating customer retention

    OpenAIRE

    Mgbemena, Chidozie Simon

    2016-01-01

    This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London. This study presents a data-driven simulation framework in order to understand customer behaviour and therefore improve customer retention. The overarching system design methodology used for this study is aligned with the design science paradigm. The Social Media Domain Analysis (SoMeDoA) approach is adopted and evaluated to build a model on the determinants of customer satisfaction ...

  20. Large-Scale Mode Identification and Data-Driven Sciences

    OpenAIRE

    Mukhopadhyay, Subhadeep

    2015-01-01

    Bump-hunting or mode identification is a fundamental problem that arises in almost every scientific field of data-driven discovery. Surprisingly, very few data modeling tools are available for automatic (not requiring manual case-by-base investigation), objective (not subjective), and nonparametric (not based on restrictive parametric model assumptions) mode discovery, which can scale to large data sets. This article introduces LPMode--an algorithm based on a new theory for detecting multimod...

  1. Large-scale mode identification and data-driven sciences

    OpenAIRE

    Mukhopadhyay, Subhadeep

    2017-01-01

    Bump-hunting or mode identification is a fundamental problem that arises in almost every scientific field of data-driven discovery. Surprisingly, very few data modeling tools are available for automatic (not requiring manual case-by-case investigation), objective (not subjective), and nonparametric (not based on restrictive parametric model assumptions) mode discovery, which can scale to large data sets. This article introduces LPMode–an algorithm based on a new theory for detecting multimoda...

  2. Undersampled MR Image Reconstruction with Data-Driven Tight Frame

    OpenAIRE

    Jianbo Liu; Shanshan Wang; Xi Peng; Dong Liang

    2015-01-01

    Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven ...

  3. Mobile Assessment in Schizophrenia: A Data-Driven Momentary Approach

    OpenAIRE

    Oorschot, Margreet; Lataster, Tineke; Thewissen, Viviane; Wichers, Marieke; Myin-Germeys, Inez

    2011-01-01

    In this article, a data-driven approach was adopted to demonstrate how real-life diary techniques [ie, the experience sampling method (ESM)] could be deployed for assessment purposes in patients with psychotic disorder, delivering individualized and clinically relevant information. The dataset included patients in an acute phase of psychosis and the focus was on paranoia as one of the main psychotic symptoms (30 patients with high levels of paranoia and 34 with low levels of paranoia). Based ...

  4. Data-Driven Adaptive Observer for Fault Diagnosis

    OpenAIRE

    Shen Yin; Xuebo Yang; Hamid Reza Karimi

    2012-01-01

    This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After...

  5. Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

    Science.gov (United States)

    Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

    2015-11-01

    The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

  6. Data-Driven Guides: Supporting Expressive Design for Information Graphics.

    Science.gov (United States)

    Kim, Nam Wook; Schweickart, Eston; Liu, Zhicheng; Dontcheva, Mira; Li, Wilmot; Popovic, Jovan; Pfister, Hanspeter

    2017-01-01

    In recent years, there is a growing need for communicating complex data in an accessible graphical form. Existing visualization creation tools support automatic visual encoding, but lack flexibility for creating custom design; on the other hand, freeform illustration tools require manual visual encoding, making the design process time-consuming and error-prone. In this paper, we present Data-Driven Guides (DDG), a technique for designing expressive information graphics in a graphic design environment. Instead of being confined by predefined templates or marks, designers can generate guides from data and use the guides to draw, place and measure custom shapes. We provide guides to encode data using three fundamental visual encoding channels: length, area, and position. Users can combine more than one guide to construct complex visual structures and map these structures to data. When underlying data is changed, we use a deformation technique to transform custom shapes using the guides as the backbone of the shapes. Our evaluation shows that data-driven guides allow users to create expressive and more accurate custom data-driven graphics.

  7. Development of a data-driven semi-distributed hydrological model for regional scale catchments prone to Mediterranean flash floods

    Science.gov (United States)

    Adamovic, M.; Branger, F.; Braud, I.; Kralisch, S.

    2016-10-01

    Flash floods represent one of the most destructive natural hazards in the Mediterranean region. These floods result from very intense and spatially heterogeneous rainfall events. Distributed hydrological models are valuable tools to study these phenomena and increase our knowledge on the main processes governing the generation and propagation of floods over large spatial scales. They are generally built using a bottom-up approach that generalizes small-physics representations of processes. However, top-down or data-driven approach is increasingly shown to provide also valuable knowledge. A simplified semi-distributed continuous hydrological model, named SIMPLEFLOOD, was developed, based on the simple dynamical system approach (SDSA) proposed by Kirchner (WRR, 2009, 45, W02429), and applied to the Ardèche catchment in France (2388 km2). This data-driven method assumes that discharge at the outlet of a given catchment can be expressed as a function only of catchment storage. It leads to a 3-parameter nonlinear model according to rainfall and runoff observations. This model was distributed over sub-catchments and coupled with a kinematic wave based flow propagation module. The parameters were estimated by discharge recession analyses at several gauged stations. Parameter regionalization was conducted using a Factorial Analysis of Mixed Data (FAMD) and Hierarchical Classification on Principal Component (HCPC) in order to find relationships between the SDSA approach and catchments characteristics. Geology was found to be the main predictor of hydrological response variability and model parameters were regionalized according to the dominant geology. The SIMPLEFLOOD model was applied for a 12-year continuous simulation over the Ardèche catchment. Four flash flood events were also selected for further analysis. The simulated hydrographs were compared with the observations at 11 gauging stations with catchment size ranging from 17 to 2300 km2. The results show a good

  8. Event-by-Event Study of Space-Time Dynamics in Flux-Tube Fragmentation

    CERN Document Server

    Wong, Cheuk-Yin

    2015-01-01

    In the semi-classical description of the flux-tube fragmentation process, the rapidity-space-time ordering and the local conservation laws of charge, flavor, and momentum provide a set of powerful tools that may allow the reconstruction of the space-time dynamics of quarks and mesons in the flux-tube fragmentation in event-by-event exclusive measurements of produced hadrons. Besides testing the contents of the flux tube fragmentation mechanism, additional interesting problems that may be opened up for examination by these measurements include the stochastic and quantum fluctuations in flux-tube fragmentation, the effects of multiple collisions in $pA$ and light $AA$ collisions, the interaction between flux tubes and between produced particles from different flux tubes, the effect of the merging of the flux tubes, and the occurrence of the fragmentation of ropes in $AA$ collisions, if they ever occur.

  9. Markov State Models for Rare Events in Molecular Dynamics

    Directory of Open Access Journals (Sweden)

    Marco Sarich

    2013-12-01

    Full Text Available Rare, but important, transition events between long-lived states are a key feature of many molecular systems. In many cases, the computation of rare event statistics by direct molecular dynamics (MD simulations is infeasible, even on the most powerful computers, because of the immensely long simulation timescales needed. Recently, a technique for spatial discretization of the molecular state space designed to help overcome such problems, so-called Markov State Models (MSMs, has attracted a lot of attention. We review the theoretical background and algorithmic realization of MSMs and illustrate their use by some numerical examples. Furthermore, we introduce a novel approach to using MSMs for the efficient solution of optimal control problems that appear in applications where one desires to optimize molecular properties by means of external controls.

  10. Phosphorus Dynamics along River Continuum during Typhoon Storm Events

    Directory of Open Access Journals (Sweden)

    Ming Fai Chow

    2017-07-01

    Full Text Available Information on riverine phosphorus (P dynamics during typhoon storm events remains scarce in subtropical regions. Thus, this study investigates the spatial and temporal dynamics of riverine phosphorus in a headwater catchment during three typhoon events. Continuous sampling (3 h intervals of stormwater samples and discharge data were conducted at five locations, which represent the upstream, transitional zone, and downstream areas of the main inflow river. The results revealed that the average event mean concentrations (EMCs for total dissolved phosphorus (TDP and particulate phosphorus (PP in the upstream catchment of Fei-Tsui reservoir were 15.66 μg/L and 11.94 μg/L, respectively. There was at least a 1.3-fold increase in flow-weighted concentrations of TDP and PP from the upper to lower reaches of the main stream. PP and TDP were transported either in clockwise or anticlockwise directions, depending on storm intensity and source. The transport of TDP was primarily regulated by the subsurface flow during the storm event. Soluble reactive phosphorus (SRP contributes more than 50% of the TDP load in moderate storms, while extreme storms supply a greater dissolved organic phosphorus (DOP load into the stream. TDP accounted for approximately 50% of TP load during typhoon storms. Mobilization of all P forms was observed from upstream to downstream of the river, except for DOP. A decrease of DOP load on passing downstream may reflect the change in phosphorus form along the river continuum. Peak discharge and antecedent dry days are correlated positively with P fluxes, indicating that river bank erosion and re-suspension of within-channel sediment are the dominant pathways of P during typhoon storm periods.

  11. Data-driven pile-up correction for track-based analyses

    Energy Technology Data Exchange (ETDEWEB)

    Schulz, Holger; Lacker, Heiko; Leyton, Michael [HU Berlin (Germany); Brandt, Gerhard [University of Oxford (United Kingdom)

    2013-07-01

    The impact of pile-up can have considerable effects on observables measured at the LHC, especially those sensitive to the effects of the underlying event. We present a data-driven method that is based on the HBOM (''Hit Backspace Once More'') approach, to correct track-based distributions for tracks coming from pile-up interactions. We demonstrate successful application to a track-based measurement of event-shapes that are sensitive to the Underlying Event with the ATLAS detector. Tests of the method on Monte-Carlo simulation show closure within O(1-2 %) for the majority of bins of most observables studied.

  12. A Dynamic Approach to Modeling Dependence Between Human Failure Events

    Energy Technology Data Exchange (ETDEWEB)

    Boring, Ronald Laurids [Idaho National Laboratory

    2015-09-01

    In practice, most HRA methods use direct dependence from THERP—the notion that error be- gets error, and one human failure event (HFE) may increase the likelihood of subsequent HFEs. In this paper, we approach dependence from a simulation perspective in which the effects of human errors are dynamically modeled. There are three key concepts that play into this modeling: (1) Errors are driven by performance shaping factors (PSFs). In this context, the error propagation is not a result of the presence of an HFE yielding overall increases in subsequent HFEs. Rather, it is shared PSFs that cause dependence. (2) PSFs have qualities of lag and latency. These two qualities are not currently considered in HRA methods that use PSFs. Yet, to model the effects of PSFs, it is not simply a matter of identifying the discrete effects of a particular PSF on performance. The effects of PSFs must be considered temporally, as the PSFs will have a range of effects across the event sequence. (3) Finally, there is the concept of error spilling. When PSFs are activated, they not only have temporal effects but also lateral effects on other PSFs, leading to emergent errors. This paper presents the framework for tying together these dynamic dependence concepts.

  13. Associative nature of event participation dynamics: A network theory approach

    Science.gov (United States)

    Smiljanić, Jelena; Mitrović Dankulov, Marija

    2017-01-01

    The affiliation with various social groups can be a critical factor when it comes to quality of life of each individual, making such groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group’s ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist’s engagement in conference activities of the specific scientific community depends on the balance between the numbers of previous attendances and non-attendances and is directly related to scientist’s association with that community. Here we show that the same holds for leisure groups of the Meetup website and further quantify individual members’ association with the group. We examine how structure of personal social networks is evolving with the event attendance. Our results show that member’s increasing engagement in the group activities is primarily associated with the strengthening of already existing ties and increase in the bonding social capital. We also show that Meetup social networks mostly grow trough big events, while small events contribute to the groups cohesiveness. PMID:28166305

  14. Data-Driven Predictive Direct Load Control of Refrigeration Systems

    DEFF Research Database (Denmark)

    Shafiei, Seyed Ehsan; Knudsen, Torben; Wisniewski, Rafal

    2015-01-01

    A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control...... is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model...

  15. Data-driven algorithm to estimate friction in automobile engine

    DEFF Research Database (Denmark)

    Stotsky, Alexander A.

    2010-01-01

    Algorithms based on the oscillations of the engine angular rotational speed under fuel cutoff and no-load were proposed for estimation of the engine friction torque. The recursive algorithm to restore the periodic signal is used to calculate the amplitude of the engine speed signal at fuel cutoff....... The values of the friction torque in the corresponding table entries are updated at acquiring new measurements of the friction moment. A new, data-driven algorithm for table adaptation on the basis of stepwise regression was developed and verified using the six-cylinder Volvo engine....

  16. Building Energy Modeling: A Data-Driven Approach

    Science.gov (United States)

    Cui, Can

    Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings. Finally, Kalman filter-based data fusion technique is incorporated into the building recommendation system for on-line energy forecasting. Data fusion enables model calibration to update the state estimation in real-time, which filters out the noise and renders more accurate energy forecast. The framework is composed of two modules: off-line model recommendation module and on-line model calibration module. Specifically, the off-line model recommendation module includes 6 widely used data-driven simulation models, which are ranked by meta-learning recommendation system for off-line energy modeling on a given building scenario. Only a selective set of building physical and operational characteristic features is needed to complete the recommendation task. The on-line calibration module effectively addresses system uncertainties, where data fusion on

  17. Controller synthesis for negative imaginary systems: a data driven approach

    KAUST Repository

    Mabrok, Mohamed

    2016-02-17

    The negative imaginary (NI) property occurs in many important applications. For instance, flexible structure systems with collocated force actuators and position sensors can be modelled as negative imaginary systems. In this study, a data-driven controller synthesis methodology for NI systems is presented. In this approach, measured frequency response data of the plant is used to construct the controller frequency response at every frequency by minimising a cost function. Then, this controller response is used to identify the controller transfer function using system identification methods. © The Institution of Engineering and Technology 2016.

  18. Data driven information system for supervision of judicial open

    Directory of Open Access Journals (Sweden)

    Ming LI

    2016-08-01

    Full Text Available Aiming at the four outstanding problems of informationized supervision for judicial publicity, the judicial public data is classified based on data driven to form the finally valuable data. Then, the functional structure, technical structure and business structure of the data processing system are put forward, including data collection module, data reduction module, data analysis module, data application module and data security module, etc. The development of the data processing system based on these structures can effectively reduce work intensity of judicial open iformation management, summarize the work state, find the problems, and promote the level of judicial publicity.

  19. The Under-represented in the Data-Driven Economy

    DEFF Research Database (Denmark)

    Bechmann, Anja

    2017-01-01

    -jewellery). These data traces are increasingly used to inform product and processual decisions by companies that want to ‘listen’ to the user and optimize products and revenue accordingly or governments that want to ‘adjust’ behavior using large data streams and big data methods. What are the democratic implications...... of this data driven economy, in which data-enriched decisions may have profound consequence for the equal representation of individuals in society? How does the democratic society make sure that data traces actually represent the user and that all users are part of the data processing on equal terms...

  20. Data-driven detrending of nonstationary fractal time series with echo state networks

    CERN Document Server

    Maiorino, Enrico; Livi, Lorenzo; Rizzi, Antonello; Sadeghian, Alireza

    2015-01-01

    In this paper, we propose a data-driven approach to the problem of detrending fractal and multifractal time series. We consider a time series as the measurements elaborated from a dynamical process over time. We assume that such a dynamical process is predictable to a certain degree, by means of a class of recurrent networks called echo state networks. Such networks have been shown to be able to predict the outcome of a number of dynamical processes. Here we propose to perform a data-driven detrending of nonstationary, fractal and multifractal time series by using an echo state network operating as a filter. Notably, we predict the trend component of a given input time series, which is superimposed to the (multi)fractal component of interest. Such a (estimated) trend is then removed from the original time series and the residual signal is analyzed with the Multifractal Detrended Fluctuation Analysis for a quantitative verification of the correctness of the proposed detrending procedure. In order to demonstrat...

  1. Data-driven execution of fast multipole methods

    KAUST Repository

    Ltaief, Hatem

    2013-09-17

    Fast multipole methods (FMMs) have O (N) complexity, are compute bound, and require very little synchronization, which makes them a favorable algorithm on next-generation supercomputers. Their most common application is to accelerate N-body problems, but they can also be used to solve boundary integral equations. When the particle distribution is irregular and the tree structure is adaptive, load balancing becomes a non-trivial question. A common strategy for load balancing FMMs is to use the work load from the previous step as weights to statically repartition the next step. The authors discuss in the paper another approach based on data-driven execution to efficiently tackle this challenging load balancing problem. The core idea consists of breaking the most time-consuming stages of the FMMs into smaller tasks. The algorithm can then be represented as a directed acyclic graph where nodes represent tasks and edges represent dependencies among them. The execution of the algorithm is performed by asynchronously scheduling the tasks using the queueing and runtime for kernels runtime environment, in a way such that data dependencies are not violated for numerical correctness purposes. This asynchronous scheduling results in an out-of-order execution. The performance results of the data-driven FMM execution outperform the previous strategy and show linear speedup on a quad-socket quad-core Intel Xeon system.Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.

  2. Locative media and data-driven computing experiments

    Directory of Open Access Journals (Sweden)

    Sung-Yueh Perng

    2016-06-01

    Full Text Available Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insight from them. These projects often start from the data, rather than from a question or theory, and try to imagine and identify their potential utility. In this paper, we explore the desires and mechanics of data-driven computing experiments. We demonstrate how both locative media data and computing experiments are ‘staged’ to create new values and computing techniques, which in turn are used to try and derive possible futures that are ridden with unintended consequences. We argue that using computing experiments to imagine potential urban futures produces effects that often have little to do with creating new urban practices. Instead, these experiments promote Big Data science and the prospect that data produced for one purpose can be recast for another and act as alternative mechanisms of envisioning urban futures.

  3. Data-Driven Adaptive Observer for Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Shen Yin

    2012-01-01

    Full Text Available This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

  4. Product design pattern based on big data-driven scenario

    Directory of Open Access Journals (Sweden)

    Conggang Yu

    2016-07-01

    Full Text Available This article discusses about new product design patterns in the big data era, gives designer a new rational thinking way, and is a new way to understand the design of the product. Based on the key criteria of the product design process, category, element, and product are used to input the data, which comprises concrete data and abstract data as an enlargement of the criteria of product design process for the establishment of a big data-driven product design pattern’s model. Moreover, an experiment and a product design case are conducted to verify the feasibility of the new pattern. Ultimately, we will conclude that the data-driven product design has two patterns: one is the concrete data supporting the product design, namely “product–data–product” pattern, and the second is based on the value of the abstract data for product design, namely “data–product–data” pattern. Through the data, users are involving themselves in the design development process. Data and product form a huge network, and data plays a role of connection or node. So the essence of the design is to find a new connection based on element, and to find a new node based on category.

  5. Data-driven Science in Geochemistry & Petrology: Vision & Reality

    Science.gov (United States)

    Lehnert, K. A.; Ghiorso, M. S.; Spear, F. S.

    2013-12-01

    Science in many fields is increasingly ';data-driven'. Though referred to as a ';new' Fourth Paradigm (Hey, 2009), data-driven science is not new, and examples are cited in the Geochemical Society's data policy, including the compilation of Dziewonski & Anderson (1981) that led to PREM, and Zindler & Hart (1986), who compiled mantle isotope data to present for the first time a comprehensive view of the Earth's mantle. Today, rapidly growing data volumes, ubiquity of data access, and new computational and information management technologies enable data-driven science at a radically advanced scale of speed, extent, flexibility, and inclusiveness, with the ability to seamlessly synthesize observations, experiments, theory, and computation, and to statistically mine data across disciplines, leading to more comprehensive, well informed, and high impact scientific advances. Are geochemists, petrologists, and volcanologists ready to participate in this revolution of the scientific process? In the past year, researchers from the VGP community and related disciplines have come together at several cyberinfrastructure related workshops, in part prompted by the EarthCube initiative of the US NSF, to evaluate the status of cyberinfrastructure in their field, to put forth key scientific challenges, and identify primary data and software needs to address these. Science scenarios developed by workshop participants that range from non-equilibrium experiments focusing on mass transport, chemical reactions, and phase transformations (J. Hammer) to defining the abundance of elements and isotopes in every voxel in the Earth (W. McDonough), demonstrate the potential of cyberinfrastructure enabled science, and define the vision of how data access, visualization, analysis, computation, and cross-domain interoperability can and should support future research in VGP. The primary obstacle for data-driven science in VGP remains the dearth of accessible, integrated data from lab and sensor

  6. Manifold Learning With Contracting Observers for Data-Driven Time-Series Analysis

    Science.gov (United States)

    Shnitzer, Tal; Talmon, Ronen; Slotine, Jean-Jacques

    2017-02-01

    Analyzing signals arising from dynamical systems typically requires many modeling assumptions and parameter estimation. In high dimensions, this modeling is particularly difficult due to the "curse of dimensionality". In this paper, we propose a method for building an intrinsic representation of such signals in a purely data-driven manner. First, we apply a manifold learning technique, diffusion maps, to learn the intrinsic model of the latent variables of the dynamical system, solely from the measurements. Second, we use concepts and tools from control theory and build a linear contracting observer to estimate the latent variables in a sequential manner from new incoming measurements. The effectiveness of the presented framework is demonstrated by applying it to a toy problem and to a music analysis application. In these examples we show that our method reveals the intrinsic variables of the analyzed dynamical systems.

  7. Modeling energy market dynamics using discrete event system simulation

    Energy Technology Data Exchange (ETDEWEB)

    Gutierrez-Alcaraz, G. [Department of Electrical and Electronics Engineering, Instituto Tecnologico de Morelia, Av. Tecnologico 1500, Col. Lomas de Santiaguito 58120, Morelia Michoacan (Mexico); Sheble, G.B. [Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97207-0751 (United States)

    2009-10-15

    This paper proposes the use of Discrete Event System Simulation to study the interactions among fuel and electricity markets and consumers, and the decision-making processes of fuel companies (FUELCOs), generation companies (GENCOs), and consumers in a simple artificial energy market. In reality, since markets can reach a stable equilibrium or fail, it is important to observe how they behave in a dynamic framework. We consider a Nash-Cournot model in which marketers are depicted as Nash-Cournot players that determine supply to meet end-use consumption. Detailed engineering considerations such as transportation network flows are omitted, because the focus is upon the selection and use of appropriate market models to provide answers to policy questions. (author)

  8. Associative nature of event participation dynamics: a network theory approach

    CERN Document Server

    Smiljanić, Jelena

    2016-01-01

    Affiliation with various social groups can be a critical factor when it comes to quality of life of every individual, making these groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group's ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist's engagement in conference activities of the specific scientific community depends on the balance between the number of previous attendance and non-attendance and is directly related to scientist's association with that community. Here we show that the same holds for leisure groups of Meetup website and further quantify member's association with the group. We examine how structure of personal social networks is evolving with event attendance. Our results show that member's increasing engagement in group activities is primarily associated with the strengthening of already existing ties and increase of bonding social capital. We also show tha...

  9. Event scale variability of mixed alluvial-bedrock channel dynamics

    Science.gov (United States)

    Cook, Kristen; Turowski, Jens; Hovius, Niels

    2015-04-01

    The relationship between flood events and fluvial behavior is critical for understanding how rivers may respond to the changing hydrologic forcing that may accompany climate change. In mixed bedrock-alluvial rivers, the response of the system to a flood event can be affected by a large number of factors, including coarse sediment availability in the channel, sediment supply from the hillslopes, bedrock-controlled changes in channel width and planform, and the shape of the hydrograph. We use the Daan River Gorge in western Taiwan as a case study to directly observe the effect of individual flood events on channel evolution. The 1200 m long and up to 20 m deep bedrock gorge formed in response to uplift of the riverbed during the 1999 Chi-Chi earthquake. The extremely rapid pace of change ensures that flood events have measurable and often dramatic effects on the channel. Taiwan is subject to both summer typhoons and a spring monsoon, resulting in numerous channel-altering floods with a range of magnitudes. Discharge is therefore highly variable, ranging from 5 to over 2000 m3/s, and changes in the channel are almost entirely driven by discrete flood events. Since early 2009 we have monitored changes in the gorge with repeated RTK GPS surveys, laser rangefinder measurements, and terrestrial LIDAR surveys. Six rainfall stations and five water level gauges provide hydrological data for the basin. We find a distinct relationship between flood magnitude and the magnitude of geomorphic change; however, we do not find a clear relationship between flood characteristics and the direction of change - whether the channel experienced aggradation or erosion in a particular flood. Upstream coarse sediment supply and the influence of abrupt changes in channel width on bedload flux through the gorge appear to have important influences on the channel response. The better understand these controls, we use the model sedFlow (Heimann et al., 2014) to explore the effects of interactions

  10. Cleavage events and sperm dynamics in chick intrauterine embryos.

    Directory of Open Access Journals (Sweden)

    Hyung Chul Lee

    Full Text Available This study was undertaken to elucidate detailed event of early embryogenesis in chicken embryos using a noninvasive egg retrieval technique before oviposition. White Leghorn intrauterine eggs were retrieved from 95 cyclic hens aged up to 54-56 weeks and morphogenetic observation was made under both bright field and fluorescent image in a time course manner. Differing from mammals, asymmetric cleavage to yield preblastodermal cells was observed throughout early embryogenesis. The first two divisions occurred synchronously and four polarized preblastodermal cells resulted after cruciform cleavage. Then, asynchronous cleavage continued in a radial manner and overall cell size in the initial cleavage region was smaller than that in the distal area. Numerous sperms were visible, regardless of zygotic nuclei formation. Condensed sperm heads were present mainly in the perivitelline space and cytoplasm, and rarely in the yolk region, while decondensed sperm heads were only visible in the yolk. In conclusion, apparent differences in sperm dynamics and early cleavage events compared with mammalian embryos were detected in chick embryo development, which demonstrated polarized cleavage with penetrating supernumerary sperm into multiple regions.

  11. Data-driven MHD simulation of a solar eruption observed in NOAA Active Region 12158

    Science.gov (United States)

    Lee, Hwanhee; Magara, Tetsuya; Kang, Jihye

    2017-08-01

    We present a data-driven magnetohydrodynamic (MHD) simulation of a solar eruption where the dynamics of a background solar wind is incorporated. The background solar wind exists in the real solar atmosphere, which continuously transports magnetized plasma toward the interplanetary space. This suggests that it may play a role in producing a solar eruption. We perform a simulation for NOAA AR 12158 accompanied with X1.6-class flare and CME on 2014 September 10. We construct a magnetohydrostatic state used as the initial state of data-driven simulation, which is composed of a nonlinear force-free field (NLFFF) derived from observation data of photospheric vector magnetic field and a hydrostatic atmosphere with prescribed distributions of temperature and gravity. We then reduce the gas pressure well above the solar surface to drive a solar wind. As a result, a magnetic field gradually evolves during an early phase, and eventually eruption is observed. To figure out what causes the transition from gradual evolution to eruption, we analyze the temporal development of force distribution and geometrical shape of magnetic field lines. The result suggests that the curvature and the scale height of a coronal magnetic field play an important role in determining its dynamic state.

  12. Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development.

    Science.gov (United States)

    Cotter, Christopher R; Schüttler, Heinz-Bernd; Igoshin, Oleg A; Shimkets, Lawrence J

    2017-06-06

    Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell-cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with data-driven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.

  13. submitter Data-driven RBE parameterization for helium ion beams

    CERN Document Server

    Mairani, A; Dokic, I; Valle, S M; Tessonnier, T; Galm, R; Ciocca, M; Parodi, K; Ferrari, A; Jäkel, O; Haberer, T; Pedroni, P; Böhlen, T T

    2016-01-01

    Helium ion beams are expected to be available again in the near future for clinical use. A suitable formalism to obtain relative biological effectiveness (RBE) values for treatment planning (TP) studies is needed. In this work we developed a data-driven RBE parameterization based on published in vitro experimental values. The RBE parameterization has been developed within the framework of the linear-quadratic (LQ) model as a function of the helium linear energy transfer (LET), dose and the tissue specific parameter ${{(\\alpha /\\beta )}_{\\text{ph}}}$ of the LQ model for the reference radiation. Analytic expressions are provided, derived from the collected database, describing the $\\text{RB}{{\\text{E}}_{\\alpha}}={{\\alpha}_{\\text{He}}}/{{\\alpha}_{\\text{ph}}}$ and ${{\\text{R}}_{\\beta}}={{\\beta}_{\\text{He}}}/{{\\beta}_{\\text{ph}}}$ ratios as a function of LET. Calculated RBE values at 2 Gy photon dose and at 10% survival ($\\text{RB}{{\\text{E}}_{10}}$ ) are compared with the experimental ones. Pearson's correlati...

  14. Data-driven system to predict academic grades and dropout.

    Science.gov (United States)

    Rovira, Sergi; Puertas, Eloi; Igual, Laura

    2017-01-01

    Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona.

  15. Integrative systems biology for data-driven knowledge discovery.

    Science.gov (United States)

    Greene, Casey S; Troyanskaya, Olga G

    2010-09-01

    Integrative systems biology is an approach that brings together diverse high-throughput experiments and databases to gain new insights into biological processes or systems at molecular through physiological levels. These approaches rely on diverse high-throughput experimental techniques that generate heterogeneous data by assaying varying aspects of complex biological processes. Computational approaches are necessary to provide an integrative view of these experimental results and enable data-driven knowledge discovery. Hypotheses generated from these approaches can direct definitive molecular experiments in a cost-effective manner. By using integrative systems biology approaches, we can leverage existing biological knowledge and large-scale data to improve our understanding of as yet unknown components of a system of interest and how its malfunction leads to disease.

  16. Data-driven system to predict academic grades and dropout

    Science.gov (United States)

    Rovira, Sergi; Puertas, Eloi

    2017-01-01

    Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona. PMID:28196078

  17. Data-driven forward model inference for EEG brain imaging

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai

    2016-01-01

    Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain......-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models....... Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging...

  18. Data-driven forward model inference for EEG brain imaging

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai

    2016-01-01

    Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain......-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models....... Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging...

  19. Data-driven simulation methodology using DES 4-layer architecture

    Directory of Open Access Journals (Sweden)

    Aida Saez

    2016-05-01

    Full Text Available In this study, we present a methodology to build data-driven simulation models of manufacturing plants. We go further than other research proposals and we suggest focusing simulation model development under a 4-layer architecture (network, logic, database and visual reality. The Network layer includes system infrastructure. The Logic layer covers operations planning and control system, and material handling equipment system. The Database holds all the information needed to perform the simulation, the results used to analyze and the values that the Logic layer is using to manage the Plant. Finally, the Visual Reality displays an augmented reality system including not only the machinery and the movement but also blackboards and other Andon elements. This architecture provides numerous advantages as helps to build a simulation model that consistently considers the internal logistics, in a very flexible way.

  20. A purely data driven method for European option valuation

    Institute of Scientific and Technical Information of China (English)

    HUANG Guang-hui; WAN Jian-ping

    2006-01-01

    An alternative option pricing method is proposed based on a random walk market model.The minimal entropy martingale measure which adopts no arbitrage opportunity in the market,is deduced for this market model and is used as the pricing measure to evaluate European call options by a Monte Carlo simulation method.The proposed method is a purely data driven valuation method without any distributional assumption about the price process of underlying asset.The performance of the proposed method is compared with the canonical valuation method and the historical volatility-based Black-Scholes method in an artificial Black-Scholes world.The simulation results show that the proposed method has merits,and is valuable to financial engineering.

  1. Data-driven identification of potential Zika virus vectors

    Science.gov (United States)

    Evans, Michelle V; Dallas, Tad A; Han, Barbara A; Murdock, Courtney C; Drake, John M

    2017-01-01

    Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States. DOI: http://dx.doi.org/10.7554/eLife.22053.001 PMID:28244371

  2. Facilitating Data Driven Business Model Innovation - A Case study

    DEFF Research Database (Denmark)

    Bjerrum, Torben Cæsar Bisgaard; Andersen, Troels Christian; Aagaard, Annabeth

    2016-01-01

    , that gathers knowledge is of great importance. The SMEs have little, if no experience, within data handling, data analytics, and working with structured Business Model Innovation (BMI), that relates to both new and conventional products, processes and services. This new frontier of data and BMI will have......This paper aims to understand the barriers that businesses meet in understanding their current business models (BM) and in their attempt at innovating new data driven business models (DDBM) using data. The interdisciplinary challenge of knowledge exchange occurring outside and/or inside businesses...... ability to adapt these new DDBM depends on the ability to pick up, share and develop knowledge between customers, partners and the network. This knowledge can be embedded into core BMs and constitutes a strategic opportunity enabling businesses to extract value from data into BMI, resulting in DDBMs...

  3. Discovering Outliers of Potential Drug Toxicities Using a Large-scale Data-driven Approach.

    Science.gov (United States)

    Luo, Jake; Cisler, Ron A

    2016-01-01

    We systematically compared the adverse effects of cancer drugs to detect event outliers across different clinical trials using a data-driven approach. Because many cancer drugs are toxic to patients, better understanding of adverse events of cancer drugs is critical for developing therapies that could minimize the toxic effects. However, due to the large variabilities of adverse events across different cancer drugs, methods to efficiently compare adverse effects across different cancer drugs are lacking. To address this challenge, we present an exploration study that integrates multiple adverse event reports from clinical trials in order to systematically compare adverse events across different cancer drugs. To demonstrate our methods, we first collected data on 186,339 clinical trials from ClinicalTrials.gov and selected 30 common cancer drugs. We identified 1602 cancer trials that studied the selected cancer drugs. Our methods effectively extracted 12,922 distinct adverse events from the clinical trial reports. Using the extracted data, we ranked all 12,922 adverse events based on their prevalence in the clinical trials, such as nausea 82%, fatigue 77%, and vomiting 75.97%. To detect the significant drug outliers that could have a statistically high possibility of causing an event, we used the boxplot method to visualize adverse event outliers across different drugs and applied Grubbs' test to evaluate the significance. Analyses showed that by systematically integrating cross-trial data from multiple clinical trial reports, adverse event outliers associated with cancer drugs can be detected. The method was demonstrated by detecting the following four statistically significant adverse event cases: the association of the drug axitinib with hypertension (Grubbs' test, P < 0.001), the association of the drug imatinib with muscle spasm (P < 0.001), the association of the drug vorinostat with deep vein thrombosis (P < 0.001), and the association of the drug afatinib

  4. A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach.

    Science.gov (United States)

    Bogoevska, Simona; Spiridonakos, Minas; Chatzi, Eleni; Dumova-Jovanoska, Elena; Höffer, Rudiger

    2017-03-30

    The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe's renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool.

  5. Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control.

    Science.gov (United States)

    Bu, Xuhui; Hou, Zhongsheng; Zhang, Hongwei

    2017-03-14

    This paper investigates the data-driven consensus tracking problem for multiagent systems with both fixed communication topology and switching topology by utilizing a distributed model free adaptive control (MFAC) method. Here, agent's dynamics are described by unknown nonlinear systems and only a subset of followers can access the desired trajectory. The dynamical linearization technique is applied to each agent based on the pseudo partial derivative, and then, a distributed MFAC algorithm is proposed to ensure that all agents can track the desired trajectory. It is shown that the consensus error can be reduced for both time invariable and time varying desired trajectories. The main feature of this design is that consensus tracking can be achieved using only input-output data of each agent. The effectiveness of the proposed design is verified by simulation examples.

  6. Conical intersection dynamics of the primary photoisomerization event in vision.

    Science.gov (United States)

    Polli, Dario; Altoè, Piero; Weingart, Oliver; Spillane, Katelyn M; Manzoni, Cristian; Brida, Daniele; Tomasello, Gaia; Orlandi, Giorgio; Kukura, Philipp; Mathies, Richard A; Garavelli, Marco; Cerullo, Giulio

    2010-09-23

    Ever since the conversion of the 11-cis retinal chromophore to its all-trans form in rhodopsin was identified as the primary photochemical event in vision, experimentalists and theoreticians have tried to unravel the molecular details of this process. The high quantum yield of 0.65 (ref. 2), the production of the primary ground-state rhodopsin photoproduct within a mere 200 fs (refs 3-7), and the storage of considerable energy in the first stable bathorhodopsin intermediate all suggest an unusually fast and efficient photoactivated one-way reaction. Rhodopsin's unique reactivity is generally attributed to a conical intersection between the potential energy surfaces of the ground and excited electronic states enabling the efficient and ultrafast conversion of photon energy into chemical energy. But obtaining direct experimental evidence for the involvement of a conical intersection is challenging: the energy gap between the electronic states of the reacting molecule changes significantly over an ultrashort timescale, which calls for observational methods that combine high temporal resolution with a broad spectral observation window. Here we show that ultrafast optical spectroscopy with sub-20-fs time resolution and spectral coverage from the visible to the near-infrared allows us to follow the dynamics leading to the conical intersection in rhodopsin isomerization. We track coherent wave-packet motion from the photoexcited Franck-Condon region to the photoproduct by monitoring the loss of reactant emission and the subsequent appearance of photoproduct absorption, and find excellent agreement between the experimental observations and molecular dynamics calculations that involve a true electronic state crossing. Taken together, these findings constitute the most compelling evidence to date for the existence and importance of conical intersections in visual photochemistry.

  7. DynamO: a free O(N) general event-driven molecular dynamics simulator.

    Science.gov (United States)

    Bannerman, M N; Sargant, R; Lue, L

    2011-11-30

    Molecular dynamics algorithms for systems of particles interacting through discrete or "hard" potentials are fundamentally different to the methods for continuous or "soft" potential systems. Although many software packages have been developed for continuous potential systems, software for discrete potential systems based on event-driven algorithms are relatively scarce and specialized. We present DynamO, a general event-driven simulation package, which displays the optimal O(N) asymptotic scaling of the computational cost with the number of particles N, rather than the O(N) scaling found in most standard algorithms. DynamO provides reference implementations of the best available event-driven algorithms. These techniques allow the rapid simulation of both complex and large (>10(6) particles) systems for long times. The performance of the program is benchmarked for elastic hard sphere systems, homogeneous cooling and sheared inelastic hard spheres, and equilibrium Lennard-Jones fluids. This software and its documentation are distributed under the GNU General Public license and can be freely downloaded from http://marcusbannerman.co.uk/dynamo.

  8. High frame-rate neutron radiography of dynamic events

    Energy Technology Data Exchange (ETDEWEB)

    Bossi, R.H.; Robinson, A.H.; Barton, J.P.

    1981-11-20

    A system has been developed to perform neutron radiographic analysis of dynamic events having a duration of several milliseconds. The system has been operated in the range of 2000 to 10,000 frames/second. Synchronization has provided high-speed-motion neutron radiographs for evaluation of the firing cycle of 7.62 mm munition rounds within a steel rifle barrel. The system has also been used to demonstrate the ability to produce neutron radiographic movies of two-phase flow. The equipment uses the Oregon State University TRIGA reactor capable of pulsing to 3000 MW peak power, a neutron beam collimator, a scintillator neutron conversion screen coupled to an image intensifier, and a 16 mm high speed movie camera. The peak neutron flux incident at the object position is approximately 4 x 10/sup 11/ n/cm/sup 2/s with a pulse, full width at half maximum, of 9 ms. Special studies have been performed on the scintillator conversion screens and on the effects of statistical limitations on the image quality. Modulation transfer function analysis has been used to assist in the evaluation of the system performance.

  9. A data-driven prediction method for fast-slow systems

    Science.gov (United States)

    Groth, Andreas; Chekroun, Mickael; Kondrashov, Dmitri; Ghil, Michael

    2016-04-01

    In this work, we present a prediction method for processes that exhibit a mixture of variability on low and fast scales. The method relies on combining empirical model reduction (EMR) with singular spectrum analysis (SSA). EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity and serial correlation in the estimated noise, while SSA provides a decomposition of the complex dynamics into low-order components that capture spatio-temporal behavior on different time scales. Our study focuses on the data-driven modeling of partial observations from dynamical systems that exhibit power spectra with broad peaks. The main result in this talk is that the combination of SSA pre-filtering with EMR modeling improves, under certain circumstances, the modeling and prediction skill of such a system, as compared to a standard EMR prediction based on raw data. Specifically, it is the separation into "fast" and "slow" temporal scales by the SSA pre-filtering that achieves the improvement. We show, in particular that the resulting EMR-SSA emulators help predict intermittent behavior such as rapid transitions between specific regions of the system's phase space. This capability of the EMR-SSA prediction will be demonstrated on two low-dimensional models: the Rössler system and a Lotka-Volterra model for interspecies competition. In either case, the chaotic dynamics is produced through a Shilnikov-type mechanism and we argue that the latter seems to be an important ingredient for the good prediction skills of EMR-SSA emulators. Shilnikov-type behavior has been shown to arise in various complex geophysical fluid models, such as baroclinic quasi-geostrophic flows in the mid-latitude atmosphere and wind-driven double-gyre ocean circulation models. This pervasiveness of the Shilnikow mechanism of fast-slow transition opens interesting perspectives for the extension of the proposed EMR-SSA approach to more realistic situations.

  10. Data-driven encoding for quantitative genetic trait prediction.

    Science.gov (United States)

    He, Dan; Wang, Zhanyong; Parida, Laxmi

    2015-01-01

    Given a set of biallelic molecular markers, such as SNPs, with genotype values on a collection of plant, animal or human samples, the goal of quantitative genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Quantitative genetic trait prediction is usually represented as linear regression models which require quantitative encodings for the genotypes: the three distinct genotype values, corresponding to one heterozygous and two homozygous alleles, are usually coded as integers, and manipulated algebraically in the model. Further, epistasis between multiple markers is modeled as multiplication between the markers: it is unclear that the regression model continues to be effective under this. In this work we investigate the effects of encodings to the quantitative genetic trait prediction problem. We first showed that different encodings lead to different prediction accuracies, in many test cases. We then proposed a data-driven encoding strategy, where we encode the genotypes according to their distribution in the phenotypes and we allow each marker to have different encodings. We show in our experiments that this encoding strategy is able to improve the performance of the genetic trait prediction method and it is more helpful for the oligogenic traits, whose values rely on a relatively small set of markers. To the best of our knowledge, this is the first paper that discusses the effects of encodings to the genetic trait prediction problem.

  11. A data-driven approach to quality risk management

    Directory of Open Access Journals (Sweden)

    Demissie Alemayehu

    2013-01-01

    Full Text Available Aim: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Materials and Methods: Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. Results: Only a subset of the risk factors had a significant association with quality issues, and included: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Conclusion: Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.

  12. Data driven fault detection and isolation: a wind turbine scenario

    Directory of Open Access Journals (Sweden)

    Rubén Francisco Manrique Piramanrique

    2015-04-01

    Full Text Available One of the greatest drawbacks in wind energy generation is the high maintenance cost associated to mechanical faults. This problem becomes more evident in utility scale wind turbines, where the increased size and nominal capacity comes with additional problems associated with structural vibrations and aeroelastic effects in the blades. Due to the increased operation capability, it is imperative to detect system degradation and faults in an efficient manner, maintaining system integrity, reliability and reducing operation costs. This paper presents a comprehensive comparison of four different Fault Detection and Isolation (FDI filters based on “Data Driven” (DD techniques. In order to enhance FDI performance, a multi-level strategy is used where:  the first level detects the occurrence of any given fault (detection, while  the second identifies the source of the fault (isolation. Four different DD classification techniques (namely Support Vector Machines, Artificial Neural Networks, K Nearest Neighbors and Gaussian Mixture Models were studied and compared for each of the proposed classification levels. The best strategy at each level could be selected to build the final data driven FDI system. The performance of the proposed scheme is evaluated on a benchmark model of a commercial wind turbine. 

  13. Data-driven forward model inference for EEG brain imaging.

    Science.gov (United States)

    Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai

    2016-06-13

    Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain imaging device. The quality of the source reconstruction depends on the forward model which details head geometry and conductivities of different head compartments. These person-specific factors are complex to determine, requiring detailed knowledge of the subject's anatomy and physiology. In this proof-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models. Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging is possible, even when the head geometry and conductivities are unknown.

  14. Data driven uncertainty evaluation for complex engineered system design

    Science.gov (United States)

    Liu, Boyuan; Huang, Shuangxi; Fan, Wenhui; Xiao, Tianyuan; Humann, James; Lai, Yuyang; Jin, Yan

    2016-09-01

    Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail. The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carried out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.

  15. Data driven uncertainty evaluation for complex engineered system design

    Science.gov (United States)

    Liu, Boyuan; Huang, Shuangxi; Fan, Wenhui; Xiao, Tianyuan; Humann, James; Lai, Yuyang; Jin, Yan

    2016-05-01

    Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail. The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carried out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.

  16. Data-Driven Modeling and Prediction of Arctic Sea Ice

    Science.gov (United States)

    Kondrashov, Dmitri; Chekroun, Mickael; Ghil, Michael

    2016-04-01

    We present results of data-driven predictive analyses of sea ice over the main Arctic regions. Our approach relies on the Multilayer Stochastic Modeling (MSM) framework of Kondrashov, Chekroun and Ghil [Physica D, 2015] and it leads to probabilistic prognostic models of sea ice concentration (SIC) anomalies on seasonal time scales. This approach is applied to monthly time series of state-of-the-art data-adaptive decompositions of SIC and selected climate variables over the Arctic. We evaluate the predictive skill of MSM models by performing retrospective forecasts with "no-look ahead" for up to 6-months ahead. It will be shown in particular that the memory effects included intrinsically in the formulation of our non-Markovian MSM models allow for improvements of the prediction skill of large-amplitude SIC anomalies in certain Arctic regions on the one hand, and of September Sea Ice Extent, on the other. Further improvements allowed by the MSM framework will adopt a nonlinear formulation and explore next-generation data-adaptive decompositions, namely modification of Principal Oscillation Patterns (POPs) and rotated Multichannel Singular Spectrum Analysis (M-SSA).

  17. Data-driven coronal evolutionary model of active region 11944.

    Science.gov (United States)

    Kazachenko, M.

    2014-12-01

    Recent availability of systematic measurements of vector magnetic fields and Doppler velocities has allowed us to utilize a data-driven approach for modeling observed active regions (AR), a crucial step for understanding the nature of solar flare initiation. We use a sequence of vector magnetograms and Dopplergrams from the Helioseismic and Magnetic Imager (HMI) aboard the SDO to drive magnetofrictional (MF) model of the coronal magnetic field in the the vicinity of AR 11944, where an X1.2 flare on January 7 2014 occurred. To drive the coronal field we impose a time-dependent boundary condition based on temporal sequences of magnetic and electric fields at the bottom of the computational domain, i.e. the photosphere. To derive the electric fields we use a recently improved poloidal-toroidal decomposition (PTD), which we call the ``PTD-Doppler-FLCT-Ideal'' or PDFI technique. We investigate the results of the simulated coronal evolution, compare those with EUV observations from Atmospheric Imaging Assembly (AIA) and discuss what we could learn from them. This work is a a collaborative effort from the UC Berkeley Space Sciences Laboratory (SSL), Stanford University, and Lockheed-Martin and is a part of Coronal Global Evolutionary (CGEM) Model, funded jointly by NASA and NSF.

  18. A Data-Driven Approach to Realistic Shape Morphing

    KAUST Repository

    Gao, Lin

    2013-05-01

    Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper. © 2013 The Eurographics Association and Blackwell Publishing Ltd.

  19. Boosted learned kernels for data-driven vesselness measure

    Science.gov (United States)

    Grisan, E.

    2017-03-01

    Common vessel centerline extraction methods rely on the computation of a measure providing the likeness of the local appearance of the data to a curvilinear tube-like structure. The most popular techniques rely on empirically designed (hand crafted) measurements as the widely used Hessian vesselness, the recent oriented flux tubeness or filters (e.g. the Gaussian matched filter) that are developed to respond to local features, without exploiting any context information nor the rich structural information embedded in the data. At variance with the previously proposed methods, we propose a completely data-driven approach for learning a vesselness measure from expert-annotated dataset. For each data point (voxel or pixel), we extract the intensity values in a neighborhood region, and estimate the discriminative convolutional kernel yielding a positive response for vessel data and negative response for non-vessel data. The process is iterated within a boosting framework, providing a set of linear filters, whose combined response is the learned vesselness measure. We show the results of the general-use proposed method on the DRIVE retinal images dataset, comparing its performance against the hessian-based vesselness, oriented flux antisymmetry tubeness, and vesselness learned with a probabilistic boosting tree or with a regression tree. We demonstrate the superiority of our approach that yields a vessel detection accuracy of 0.95, with respect to 0.92 (hessian), 0.90 (oriented flux) and 0.85 (boosting tree).

  20. Stellar dynamics and tidal disruption events in galactic nuclei

    CERN Document Server

    Alexander, Tal

    2012-01-01

    The disruption of a star by the tidal field of a massive black hole is the final outcome of a chain of complex dynamical processes in the host galaxy. I introduce the "loss cone problem", and describe the many theoretical and numerical challenges on the path of solving it. I review various dynamical channels by which stars can be supplied to a massive black hole, and the relevant dynamical relaxation / randomization mechanisms. I briefly mention some "exotic" tidal disruption scenarios, and conclude by discussing some new dynamical results that are changing our understanding of dynamics near a massive black hole, and may well be relevant for tidal disruption dynamics.

  1. Data-driven design of fault diagnosis systems nonlinear multimode processes

    CERN Document Server

    Haghani Abandan Sari, Adel

    2014-01-01

    In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target...

  2. Data-driven fault detection for industrial processes canonical correlation analysis and projection based methods

    CERN Document Server

    Chen, Zhiwen

    2017-01-01

    Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Contents A New Index for Performance Evaluation of FD Methods CCA-based FD Method for the Monitoring of Stationary Processes Projection-based FD Method for the Monitoring of Dynamic Processes Benchmark Study and Real-Time Implementat...

  3. Design and experiment of data-driven modeling and flutter control of a prototype wing

    Science.gov (United States)

    Lum, Kai-Yew; Xu, Cai-Lin; Lu, Zhenbo; Lai, Kwok-Leung; Cui, Yongdong

    2017-06-01

    This paper presents an approach for data-driven modeling of aeroelasticity and its application to flutter control design of a wind-tunnel wing model. Modeling is centered on system identification of unsteady aerodynamic loads using computational fluid dynamics data, and adopts a nonlinear multivariable extension of the Hammerstein-Wiener system. The formulation is in modal coordinates of the elastic structure, and yields a reduced-order model of the aeroelastic feedback loop that is parametrized by airspeed. Flutter suppression is thus cast as a robust stabilization problem over uncertain airspeed, for which a low-order H∞ controller is computed. The paper discusses in detail parameter sensitivity and observability of the model, the former to justify the chosen model structure, and the latter to provide a criterion for physical sensor placement. Wind tunnel experiments confirm the validity of the modeling approach and the effectiveness of the control design.

  4. SIDEKICK: Genomic data driven analysis and decision-making framework

    Directory of Open Access Journals (Sweden)

    Yoon Kihoon

    2010-12-01

    Full Text Available Abstract Background Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation. Results Sidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick. Conclusions Sidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to

  5. Mapping landslide susceptibility using data-driven methods.

    Science.gov (United States)

    Zêzere, J L; Pereira, S; Melo, R; Oliveira, S C; Garcia, R A C

    2017-07-01

    Most epistemic uncertainty within data-driven landslide susceptibility assessment results from errors in landslide inventories, difficulty in identifying and mapping landslide causes and decisions related with the modelling procedure. In this work we evaluate and discuss differences observed on landslide susceptibility maps resulting from: (i) the selection of the statistical method; (ii) the selection of the terrain mapping unit; and (iii) the selection of the feature type to represent landslides in the model (polygon versus point). The work is performed in a single study area (Silveira Basin - 18.2km(2) - Lisbon Region, Portugal) using a unique database of geo-environmental landslide predisposing factors and an inventory of 82 shallow translational slides. The logistic regression, the discriminant analysis and two versions of the information value were used and we conclude that multivariate statistical methods perform better when computed over heterogeneous terrain units and should be selected to assess landslide susceptibility based on slope terrain units, geo-hydrological terrain units or census terrain units. However, evidence was found that the chosen terrain mapping unit can produce greater differences on final susceptibility results than those resulting from the chosen statistical method for modelling. The landslide susceptibility should be assessed over grid cell terrain units whenever the spatial accuracy of landslide inventory is good. In addition, a single point per landslide proved to be efficient to generate accurate landslide susceptibility maps, providing the landslides are of small size, thus minimizing the possible existence of heterogeneities of predisposing factors within the landslide boundary. Although during last years the ROC curves have been preferred to evaluate the susceptibility model's performance, evidence was found that the model with the highest AUC ROC is not necessarily the best landslide susceptibility model, namely when terrain

  6. Evidence-based and data-driven road safety management

    Directory of Open Access Journals (Sweden)

    Fred Wegman

    2015-07-01

    Full Text Available Over the past decades, road safety in highly-motorised countries has made significant progress. Although we have a fair understanding of the reasons for this progress, we don't have conclusive evidence for this. A new generation of road safety management approaches has entered road safety, starting when countries decided to guide themselves by setting quantitative targets (e.g. 50% less casualties in ten years' time. Setting realistic targets, designing strategies and action plans to achieve these targets and monitoring progress have resulted in more scientific research to support decision-making on these topics. Three subjects are key in this new approach of evidence-based and data-driven road safety management: ex-post and ex-ante evaluation of both individual interventions and intervention packages in road safety strategies, and transferability (external validity of the research results. In this article, we explore these subjects based on recent experiences in four jurisdictions (Western Australia, the Netherlands, Sweden and Switzerland. All four apply similar approaches and tools; differences are considered marginal. It is concluded that policy-making and political decisions were influenced to a great extent by the results of analysis and research. Nevertheless, to compensate for a relatively weak theoretical basis and to improve the power of this new approach, a number of issues will need further research. This includes ex-post and ex-ante evaluation, a better understanding of extrapolation of historical trends and the transferability of research results. This new approach cannot be realized without high-quality road safety data. Good data and knowledge are indispensable for this new and very promising approach.

  7. Testing the Accuracy of Data-driven MHD Simulations of Active Region Evolution

    Science.gov (United States)

    Leake, James E.; Linton, Mark G.; Schuck, Peter W.

    2017-04-01

    Models for the evolution of the solar coronal magnetic field are vital for understanding solar activity, yet the best measurements of the magnetic field lie at the photosphere, necessitating the development of coronal models which are “data-driven” at the photosphere. We present an investigation to determine the feasibility and accuracy of such methods. Our validation framework uses a simulation of active region (AR) formation, modeling the emergence of magnetic flux from the convection zone to the corona, as a ground-truth data set, to supply both the photospheric information and to perform the validation of the data-driven method. We focus our investigation on how the accuracy of the data-driven model depends on the temporal frequency of the driving data. The Helioseismic and Magnetic Imager on NASA’s Solar Dynamics Observatory produces full-disk vector magnetic field measurements at a 12-minute cadence. Using our framework we show that ARs that emerge over 25 hr can be modeled by the data-driving method with only ∼1% error in the free magnetic energy, assuming the photospheric information is specified every 12 minutes. However, for rapidly evolving features, under-sampling of the dynamics at this cadence leads to a strobe effect, generating large electric currents and incorrect coronal morphology and energies. We derive a sampling condition for the driving cadence based on the evolution of these small-scale features, and show that higher-cadence driving can lead to acceptable errors. Future work will investigate the source of errors associated with deriving plasma variables from the photospheric magnetograms as well as other sources of errors, such as reduced resolution, instrument bias, and noise.

  8. Research on giant magnetostrictive actuator online nonlinear modeling based on data driven principle with grating sensing technique

    Science.gov (United States)

    Han, Ping

    2017-01-01

    A novel Giant Magnetostrictive Actuator (GMA) experimental system with Fiber Bragg Grating (FBG) sensing technique and its modeling method based on data driven principle are proposed. The FBG sensors are adopted to gather the multi-physics fields' status data of GMA considering the strong nonlinearity of the Giant Magnetostrictive Material and GMA micro-actuated structure. The feedback features are obtained from the raw dynamic status data, which are preprocessed by data fill and abnormal value detection algorithms. Correspondingly the Least Squares Support Vector Machine method is utilized to realize GMA online nonlinear modeling with data driven principle. The model performance and its relative algorithms are experimentally evaluated. The model can regularly run in the frequency range from 10 to 1000 Hz and temperature range from 20 to 100 °C with the minimum prediction error stable in the range from -1.2% to 1.1%.

  9. Stable algorithm for event detection in event-driven particle dynamics: logical states

    Science.gov (United States)

    Strobl, Severin; Bannerman, Marcus N.; Pöschel, Thorsten

    2016-07-01

    Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.

  10. Stable algorithm for event detection in event-driven particle dynamics: Logical states

    CERN Document Server

    Strobl, Severin; Poeschel, Thorsten

    2015-01-01

    Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.

  11. Data-driven modeling, control and tools for cyber-physical energy systems

    Science.gov (United States)

    Behl, Madhur

    Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about

  12. RWater - A Novel Cyber-enabled Data-driven Educational Tool for Interpreting and Modeling Hydrologic Processes

    Science.gov (United States)

    Rajib, M. A.; Merwade, V.; Zhao, L.; Song, C.

    2014-12-01

    Explaining the complex cause-and-effect relationships in hydrologic cycle can often be challenging in a classroom with the use of traditional teaching approaches. With the availability of observed rainfall, streamflow and other hydrology data on the internet, it is possible to provide the necessary tools to students to explore these relationships and enhance their learning experience. From this perspective, a new online educational tool, called RWater, is developed using Purdue University's HUBzero technology. RWater's unique features include: (i) its accessibility including the R software from any java supported web browser; (ii) no installation of any software on user's computer; (iii) all the work and resulting data are stored in user's working directory on RWater server; and (iv) no prior programming experience with R software is necessary. In its current version, RWater can dynamically extract streamflow data from any USGS gaging station without any need for post-processing for use in the educational modules. By following data-driven modules, students can write small scripts in R and thereby create visualizations to identify the effect of rainfall distribution and watershed characteristics on runoff generation, investigate the impacts of landuse and climate change on streamflow, and explore the changes in extreme hydrologic events in actual locations. Each module contains relevant definitions, instructions on data extraction and coding, as well as conceptual questions based on the possible analyses which the students would perform. In order to assess its suitability in classroom implementation, and to evaluate users' perception over its utility, the current version of RWater has been tested with three different groups: (i) high school students, (ii) middle and high school teachers; and (iii) upper undergraduate/graduate students. The survey results from these trials suggest that the RWater has potential to improve students' understanding on various

  13. Using Event-Based Parsing to Support Dynamic Protocol Evolution

    Science.gov (United States)

    2003-03-01

    System Generator HTTP 1.0 Parser Composer EBP System Generator HTTP 1.0 Parser Composer Client... Generator HTTP 1.0 Parser Composer EBP System Generator HTTP 1.0 Parser Composer Client HTTP 1.1 Proxy Event Handler 1-7 8 8 Fig. 8: Modified...configuration and scenario events 9 though 19. Server HTTP 1.0 EBP System Generator HTTP 1.0 Parser Composer Client HTTP 1.1 Proxy

  14. Data-driven initialization of SParSE

    Science.gov (United States)

    Roh, Min K.; Proctor, Joshua L.

    2017-07-01

    Despite the ever-increasing affordability and availability of high performance computing platforms, computational analysis of stochastic biochemical systems remains an open problem. A recently developed event-based parameter estimation method, the stochastic parameter search for events (SParSE), is able to efficiently sample reaction rate parameter values that confer a user-specified target event with a given probability and error tolerance. Despite the substantial computational savings, the efficiency of SParSE can be further improved by intelligently generating new initial parameter sets based on previously computed trajectories. In this article, we propose a principled method which combines the efficiencies of SParSE with these geometric machine-learning methods to generate new initial parameters based on the previously collected data.

  15. Local Events and Dynamics on Weighted Complex Networks

    Institute of Scientific and Technical Information of China (English)

    ZHAO Hui; GAO Zi-You

    2006-01-01

    @@ We examine the weighted networks grown and evolved by local events, such as the addition of new vertices and links and we show that depending on frequency of the events, a generalized power-law distribution of strength can emerge. Continuum theory is used to predict the scaling function as well as the exponents, which is in good agreement with the numerical simulation results. Depending on event frequency, power-law distributions of degree and weight can also be expected. Probability saturation phenomena for small strength and degree in many real world networks can be reproduced. Particularly, the non-trivial clustering coefficient, assortativity coefficient and degree-strength correlation in our model are all consistent with empirical evidences.

  16. Improving Cybersecurity Governance Through Data-Driven Decision-Making and Execution (Briefing Charts)

    Science.gov (United States)

    2014-10-01

    Professional Sentiments Analysis Orient Unstructured Data Machine Learning Text Analysis Trend Analysis Correlation 14 Sources of Constraints and...2014 Carnegie Mellon University Improving Cybersecurity Governance Through Data-Driven Decision- Making and Execution Doug Gray Report...REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Improving Cybersecurity Governance Through Data-Driven Decision-Making and Execution

  17. Monitoring intracellular oxidative events using dynamic spectral unmixing microscopy

    Science.gov (United States)

    There is increasing interest in using live-cell imaging to monitor not just individual intracellular endpoints, but to investigate the interplay between multiple molecular events as they unfold in real time within the cell. A major impediment to simultaneous acquisition of multip...

  18. Perceived synchrony for realistic and dynamic audiovisual events.

    Science.gov (United States)

    Eg, Ragnhild; Behne, Dawn M

    2015-01-01

    In well-controlled laboratory experiments, researchers have found that humans can perceive delays between auditory and visual signals as short as 20 ms. Conversely, other experiments have shown that humans can tolerate audiovisual asynchrony that exceeds 200 ms. This seeming contradiction in human temporal sensitivity can be attributed to a number of factors such as experimental approaches and precedence of the asynchronous signals, along with the nature, duration, location, complexity and repetitiveness of the audiovisual stimuli, and even individual differences. In order to better understand how temporal integration of audiovisual events occurs in the real world, we need to close the gap between the experimental setting and the complex setting of everyday life. With this work, we aimed to contribute one brick to the bridge that will close this gap. We compared perceived synchrony for long-running and eventful audiovisual sequences to shorter sequences that contain a single audiovisual event, for three types of content: action, music, and speech. The resulting windows of temporal integration showed that participants were better at detecting asynchrony for the longer stimuli, possibly because the long-running sequences contain multiple corresponding events that offer audiovisual timing cues. Moreover, the points of subjective simultaneity differ between content types, suggesting that the nature of a visual scene could influence the temporal perception of events. An expected outcome from this type of experiment was the rich variation among participants' distributions and the derived points of subjective simultaneity. Hence, the designs of similar experiments call for more participants than traditional psychophysical studies. Heeding this caution, we conclude that existing theories on multisensory perception are ready to be tested on more natural and representative stimuli.

  19. Consuming America : A Data-Driven Analysis of the United States as a Reference Culture in Dutch Public Discourse on Consumer Goods, 1890-1990

    NARCIS (Netherlands)

    Wevers, M.J.H.F.

    2017-01-01

    Consuming America offers a data-driven, longitudinal analysis of the historical dynamics that have underpinned a long-term, layered cultural-historical process: the emergence of the United States as a dominant reference culture in Dutch public discourse on consumer goods between 1890 and 1990. The

  20. A data-driven processing scheme for the GPR signal analysis and noise patterns removal

    Science.gov (United States)

    Jeng, Yih; Chen, Chih-Sung

    2015-04-01

    GPR signal events are inevitably interfered by a variety of noises. Noise waves degrade the quality of subsurface reflections, mask the reflections from targets, and may appear like true reflections. Some investigators have proposed ways to minimize the interference of specific noise events; however, a generalized noise removal methodology is still an interesting issue. In this study, we demonstrate an effective methodology for analyzing GPR data and suppressing noise events. The processing scheme is framed by the modified multidimensional ensemble empirical mode decomposition (MDEEMD), a multidimensional extension of the EMD algorithm. The MDEEMD is a data-driven time-frequency approach that has the advantages of dealing with nonlinear and non-stationary multichannel signals, and outperforms other univariate EMD algorithms with better uniformity, closer scale alignment, and more reliable intrinsic mode functions (IMFs). The procedure is implemented by performing the EEMD (ensemble empirical mode decomposition) in both directions of the B-scan GPR data set consecutively to obtain a 2D image matrix in which the elements are images representing fragmentary features of the B-scan GPR data. The final 2D EEMD filter bank is achieved by applying the comparable minimal scale combination technique to the 2D image matrix. With the velocity analysis and pattern recognition, the noise components can be distinguished from the signal components in the 2D EEMD filter bank. By subtracting the noise components from the filter bank and combining the rest components or directly picking the signal components for final image reconstruction, the noise events in the B-scan are suppressed effectively while most of the true reflections remain. The developed approach provides an alternative efficient method for GPR signal enhancement and can be applied to extract information from other noisy multidimensional geophysical data with limited modifications.

  1. A data driven approach for detection and isolation of anomalies in a group of UAVs

    Institute of Scientific and Technical Information of China (English)

    Wang Yin; Wang Daobo; Wang Jianhong

    2015-01-01

    The use of groups of unmanned aerial vehicles (UAVs) has greatly expanded UAV’s capa-bilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar (dynamic) model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters. Simulation results have demonstrated the efficiency and flexibility of the proposed approach.

  2. A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach

    Directory of Open Access Journals (Sweden)

    Simona Bogoevska

    2017-03-01

    Full Text Available The complex dynamics of operational wind turbine (WT structures challenges the applicability of existing structural health monitoring (SHM strategies for condition assessment. At the center of Europe’s renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool.

  3. Application of a Data-Driven Fuzzy Control Design to a Wind Turbine Benchmark Model

    Directory of Open Access Journals (Sweden)

    Silvio Simani

    2012-01-01

    Full Text Available In general, the modelling of wind turbines is a challenging task, since they are complex dynamic systems, whose aerodynamics are nonlinear and unsteady. Accurate models should contain many degrees of freedom, and their control algorithm design must account for these complexities. However, these algorithms must capture the most important turbine dynamics without being too complex and unwieldy, mainly when they have to be implemented in real-time applications. The first contribution of this work consists of providing an application example of the design and testing through simulations, of a data-driven fuzzy wind turbine control. In particular, the strategy is based on fuzzy modelling and identification approaches to model-based control design. Fuzzy modelling and identification can represent an alternative for developing experimental models of complex systems, directly derived directly from measured input-output data without detailed system assumptions. Regarding the controller design, this paper suggests again a fuzzy control approach for the adjustment of both the wind turbine blade pitch angle and the generator torque. The effectiveness of the proposed strategies is assessed on the data sequences acquired from the considered wind turbine benchmark. Several experiments provide the evidence of the advantages of the proposed regulator with respect to different control methods.

  4. A data driven approach for detection and isolation of anomalies in a group of UAVs

    Directory of Open Access Journals (Sweden)

    Wang Yin

    2015-02-01

    Full Text Available The use of groups of unmanned aerial vehicles (UAVs has greatly expanded UAV’s capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar (dynamic model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters. Simulation results have demonstrated the efficiency and flexibility of the proposed approach.

  5. A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

    KAUST Repository

    Zerrouki, Nabil

    2016-07-26

    Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.

  6. Event heap: a coordination infrastructure for dynamic heterogeneous application interactions in ubiquitous computing environments

    Science.gov (United States)

    Johanson, Bradley E.; Fox, Armando; Winograd, Terry A.; Hanrahan, Patrick M.

    2010-04-20

    An efficient and adaptive middleware infrastructure called the Event Heap system dynamically coordinates application interactions and communications in a ubiquitous computing environment, e.g., an interactive workspace, having heterogeneous software applications running on various machines and devices across different platforms. Applications exchange events via the Event Heap. Each event is characterized by a set of unordered, named fields. Events are routed by matching certain attributes in the fields. The source and target versions of each field are automatically set when an event is posted or used as a template. The Event Heap system implements a unique combination of features, both intrinsic to tuplespaces and specific to the Event Heap, including content based addressing, support for routing patterns, standard routing fields, limited data persistence, query persistence/registration, transparent communication, self-description, flexible typing, logical/physical centralization, portable client API, at most once per source first-in-first-out ordering, and modular restartability.

  7. Position Control of Motor Drive Systems: A Data Driven Approach

    Directory of Open Access Journals (Sweden)

    Hossein Parastvand

    2015-08-01

    Full Text Available This paper presents a new model free approach to the design of robust PID controller for the position control of electrical machines, such as induction motor, synchronous motor and DC motor faced to un-modeled dynamics. It is illustrated that knowing the frequency response data is sufficient to calculate the family of robust PID controllers that satisfy -norm on the complementary sensitivity function. The usefulness of the proposed approach is demonstrated through simulation on an induction motor drive system.

  8. Inferring coupling strength from event-related dynamics

    Science.gov (United States)

    Łęski, Szymon; Wójcik, Daniel K.

    2008-10-01

    We propose an approach for inferring strength of coupling between two systems from their transient dynamics. This is of vital importance in cases where most information is carried by the transients, for instance, in evoked potentials measured commonly in electrophysiology. We show viability of our approach using nonlinear and linear measures of synchronization on a population model of thalamocortical loop and on a system of two coupled Rössler-type oscillators in nonchaotic regime.

  9. The dynamics of discrete populations and series of events

    CERN Document Server

    Hopcraft, Keith Iain; Ridley, Kevin D

    2014-01-01

    IntroductionReferencesStatistical PreliminariesIntroductionProbability DistributionsMoment-Generating FunctionsDiscrete ProcessesSeries of EventsSummaryFurther ReadingMarkovian Population ProcessesIntroductionBirths and DeathsImmigration and the Poisson ProcessThe Effect of MeasurementCorrelation of CountsSummaryFurther ReadingThe Birth-Death-Immigration ProcessIntroductionRate Equations for the ProcessEquation for the Generating FunctionGeneral Time-Dependent SolutionFluctuation Characteristics of a Birth-Death-Immigration PopulationSampling and Measurement ProcessesCorrelation of CountsSumma

  10. Photospheric Current Spikes and Their Possible Association with Flares - Results from an HMI Data Driven Model

    Science.gov (United States)

    Goodman, Michael; Kwan, Chiman; Ayhan, Bulent; Shang, Eric L.

    2016-01-01

    A data driven, near photospheric magnetohydrodynamic model predicts spikes in the horizontal current density, and associated resistive heating rate per unit volume Q. The spikes appear as increases by orders of magnitude above background values in neutral line regions (NLRs) of active regions (ARs). The largest spikes typically occur a few hours to a few days prior to M or X flares. The spikes correspond to large vertical derivatives of the horizontal magnetic field. The model takes as input the photospheric magnetic field observed by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. This 2.5 D field is used to determine an analytic expression for a 3 D magnetic field, from which the current density, vector potential, and electric field are computed in every AR pixel for 14 ARs. The field is not assumed to be force-free. The spurious 6, 12, and 24 hour Doppler periods due to SDO orbital motion are filtered out of the time series of the HMI magnetic field for each pixel using a band pass filter. The subset of spikes analyzed at the pixel level are found to occur on HMI and granulation scales of 1 arcsec and 12 minutes. Spikes are found in ARs with and without M or X flares, and outside as well as inside NLRs, but the largest spikes are localized in the NLRs of ARs with M or X flares. The energy to drive the heating associated with the largest current spikes comes from bulk flow kinetic energy, not the electromagnetic field, and the current density is highly non-force free. The results suggest that, in combination with the model, HMI is revealing strong, convection driven, non-force free heating events on granulation scales, and that it is plausible these events are correlated with subsequent M or X flares. More and longer time series need to be analyzed to determine if such a correlation exists. Above an AR dependent threshold value of Q, the number of events N(Q) with heating rates greater than or equal to Q obeys a scale

  11. Comparison of ACL strain estimated via a data-driven model with in vitro measurements.

    Science.gov (United States)

    Weinhandl, Joshua T; Hoch, Matthew C; Bawab, Sebastian Y; Ringleb, Stacie I

    2016-11-01

    Computer modeling and simulation techniques have been increasingly used to investigate anterior cruciate ligament (ACL) loading during dynamic activities in an attempt to improve our understanding of injury mechanisms and development of injury prevention programs. However, the accuracy of many of these models remains unknown and thus the purpose of this study was to compare estimates of ACL strain from a previously developed three-dimensional, data-driven model with those obtained via in vitro measurements. ACL strain was measured as the knee was cycled from approximately 10° to 120° of flexion at 20 deg s(-1) with static loads of 100, 50, and 50 N applied to the quadriceps, biceps femoris and medial hamstrings (semimembranosus and semitendinosus) tendons, respectively. A two segment, five-degree-of-freedom musculoskeletal knee model was then scaled to match the cadaver's anthropometry and in silico ACL strains were then determined based on the knee joint kinematics and moments of force. Maximum and minimum ACL strains estimated in silico were within 0.2 and 0.42% of that measured in vitro, respectively. Additionally, the model estimated ACL strain with a bias (mean difference) of -0.03% and dynamic accuracy (rms error) of 0.36% across the flexion-extension cycle. These preliminary results suggest that the proposed model was capable of estimating ACL strains during a simple flexion-extension cycle. Future studies should validate the model under more dynamic conditions with variable muscle loading. This model could then be used to estimate ACL strains during dynamic sporting activities where ACL injuries are more common.

  12. Mobile Parallel Manipulators, Modelling and Data-Driven Motion Planning

    Directory of Open Access Journals (Sweden)

    Amar Khoukhi

    2013-11-01

    Full Text Available This paper provides a kinematic and dynamic analysis of mobile parallel manipulators (MPM. The study is conducted on a composed multi-degree of freedom (DOF parallel robot carried by a wheeled mobile platform. Both positional and differential kinematics problems for the hybrid structure are solved, and the redundancy problem is solved using joint limit secondary criterion- based generalized-pseudo-inverse. A minimum time trajectory parameterization is obtained via cycloidal profile to initialize multi-objective trajectory planning of the MPM. Considered objectives include time energy minimization redundancy resolution and singularity avoidance. Simulation results illustrating the effectiveness of the proposed approach are presented and discussed.

  13. Data-driven Simulations of Magnetic Connectivity in Behind-the-Limb Gamma-ray Flares and Associated Coronal Mass Ejections

    Science.gov (United States)

    Jin, Meng; Petrosian, Vahe; Liu, Wei; Omodei, Nicola

    2017-08-01

    Recent Fermi detection of high-energy gamma-ray emission from the behind-the-limb (BTL) solar flares pose a puzzle on the particle acceleration and transport mechanisms in such events. Due to the large separation between the flare site and the location of gamma-ray emission, it is believed that the associated coronal mass ejections (CMEs) play an important role in accelerating and subsequently transporting particles back to the Sun to produce obseved gamma-rays. We explore this scenario by simulating the CME associated with a well-observed flare on 2014 September 1 about 40 degrees behind the east solar limb and by comparing the simulation and observational results. We utilize a data-driven global magnetohydrodynamics model (AWSoM: Alfven-wave Solar Model) to track the dynamical evolution of the global magnetic field during the event and investigate the magnetic connectivity between the CME/CME-driven shock and the Fermi emission region. Moreover, we derive the time-varying shock parameters (e.g., compression ratio, Alfven Mach number, and ThetaBN) over the area that is magnetically connected to the visible solar disk where Fermi gamma-ray emission originates. Our simulation shows that the visible solar disk develops connections both to the flare region and to the CME-driven shock during the eruption, which indicate that the CME’s interaction with the global solar corona is critical for understanding such Fermi BTL events and gamma-ray flares in general. We discuss the causes and implications of Fermi BTL events, in the framework of a potential shift of paradigm on particle acceleration in solar flares/CMEs.

  14. Applying Covariational Reasoning While Modeling Dynamic Events: A Framework and a Study.

    Science.gov (United States)

    Carlson, Marilyn; Jacobs, Sally; Coe, Edward; Larsen, Sean; Hsu, Eric

    2002-01-01

    Develops covariational reasoning and proposes a framework for describing mental actions when interpreting and representing dynamic function events. Investigates calculus students' ability to reason about covarying quantities in dynamic situations. Suggests that curriculum and instruction should emphasize moving students to a coordinated image of…

  15. Data-Driven Baseline Estimation of Residential Buildings for Demand Response

    Directory of Open Access Journals (Sweden)

    Saehong Park

    2015-09-01

    Full Text Available The advent of advanced metering infrastructure (AMI generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL estimation in demand response (DR management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities’ residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate.

  16. Data-driven inline optimization of the manufacturing process of car body parts

    Science.gov (United States)

    Purr, S.; Wendt, A.; Meinhardt, J.; Moelzl, K.; Werner, A.; Hagenah, H.; Merklein, M.

    2016-11-01

    The manufacturing process of car body parts needs to be adaptable during production because of fluctuating variables; finding the most suitable settings is often expensive. The cause-effect relation between variables and process results is currently unknown; thus, any measure taken to adjust the process is necessarily subjective and dependent on operator experience. To investigate the correlations involved, a data mining system that can detect influences and determine the quality of resulting parts is integrated into the series process. The collected data is used to analyze causes, predict defects, and optimize the overall process. In this paper, a data-driven method is proposed for the inline optimization of the manufacturing process of car body parts. The calculation of suitable settings to produce good parts is based on measurements of influencing variables, such as the characteristics of blanks. First, the available data are presented, and in the event of quality issues, current procedures are investigated. Thereafter, data mining techniques are applied to identify models that link occurring fluctuations and appropriate measures to adapt the process so that it addresses such fluctuations. Consequently, a method is derived for providing objective information on appropriate process parameters.

  17. Assessment of cardiovascular risk based on a data-driven knowledge discovery approach.

    Science.gov (United States)

    Mendes, D; Paredes, S; Rocha, T; Carvalho, P; Henriques, J; Cabiddu, R; Morais, J

    2015-01-01

    The cardioRisk project addresses the development of personalized risk assessment tools for patients who have been admitted to the hospital with acute myocardial infarction. Although there are models available that assess the short-term risk of death/new events for such patients, these models were established in circumstances that do not take into account the present clinical interventions and, in some cases, the risk factors used by such models are not easily available in clinical practice. The integration of the existing risk tools (applied in the clinician's daily practice) with data-driven knowledge discovery mechanisms based on data routinely collected during hospitalizations, will be a breakthrough in overcoming some of these difficulties. In this context, the development of simple and interpretable models (based on recent datasets), unquestionably will facilitate and will introduce confidence in this integration process. In this work, a simple and interpretable model based on a real dataset is proposed. It consists of a decision tree model structure that uses a reduced set of six binary risk factors. The validation is performed using a recent dataset provided by the Portuguese Society of Cardiology (11113 patients), which originally comprised 77 risk factors. A sensitivity, specificity and accuracy of, respectively, 80.42%, 77.25% and 78.80% were achieved showing the effectiveness of the approach.

  18. Excitable human dynamics driven by extrinsic events in massive communities

    CERN Document Server

    Mathiesen, Joachim; Ahlgren, Peter T H; Jensen, Mogens H

    2013-01-01

    Using empirical data from a social media site (Twitter) and on trading volumes of financial securities, we analyze the correlated human activity in massive social organizations. The activity, typically excited by real-world events and measured by the occurrence rate of international brand names and trading volumes, is characterized by intermittent fluctuations with bursts of high activity separated by quiescent periods. These fluctuations are broadly distributed with an inverse cubic tail and have long-range temporal correlations with a $1/f$ power spectrum. We describe the activity by a stochastic point process and derive the distribution of activity levels from the corresponding stochastic differential equation. The distribution and the corresponding power spectrum are fully consistent with the empirical observations.

  19. Active region upflows: 2. Data driven MHD modeling

    CERN Document Server

    Galsgaard, K; Vanninathan, K; Huang, Z; Presmann, M

    2015-01-01

    Context. Observations of many active regions show a slow systematic outflow/upflow from their edges lasting from hours to days. At present no physical explanation has been proven, while several suggestions have been put forward. Aims. This paper investigates one possible method for maintaining these upflows assuming that convective motions drive the magnetic field to initiate them through magnetic reconnection. Methods. We use Helioseismic and Magnetic Imager (HMI) data to provide an initial potential three dimensional magnetic field of the active region NOAA 11123 on 2010 November 13 where the characteristic upflow velocities are observed. A simple one-dimensional hydrostatic atmospheric model covering the region from the photosphere to the corona is derived. Local Correlation Tracking of the magnetic features in the HMI data is used to derive a proxy for the time dependent velocity field. The time dependent evolution of the system is solved using a resistive three-dimensional MagnetoHydro-Dynamic code. Resu...

  20. Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems.

    Science.gov (United States)

    Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

    2016-04-08

    This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.

  1. A Numerical Approach for Hybrid Simulation of Power System Dynamics Considering Extreme Icing Events

    DEFF Research Database (Denmark)

    Chen, Lizheng; Zhang, Hengxu; Wu, Qiuwei

    2017-01-01

    The global climate change leads to more extreme meteorological conditions such as icing weather, which have caused great losses to power systems. Comprehensive simulation tools are required to enhance the capability of power system risk assessment under extreme weather conditions. A hybrid...... numerical simulation scheme integrating icing weather events with power system dynamics is proposed to extend power system numerical simulation. A technique is developed to efficiently simulate the interaction of slow dynamics of weather events and fast dynamics of power systems. An extended package for PSS....../E enabling hybrid simulation of icing event and power system disturbance is developed, based on which a hybrid simulation platform is established. Numerical studies show that the functionality of power system simulation is greatly extended by taking into account the icing weather events....

  2. Lighting the Landscape: Molecular Events Under Dynamic Stark Shifts

    CERN Document Server

    Chang, Bo Y; Shin, Seokmin

    2016-01-01

    A new perspective on how to manipulate molecules by means of very strong laser pulses is emerging with insights from the so-called light-induced potentials, which are the adiabatic potential energy surfaces of molecules severely distorted by the effect of the strong field. Different effects appear depending on how the laser frequency is tuned, to a certain electronic transition, creating light-induced avoided crossings, or very off-resonant, generating Stark shifts. In the former case it is possible to induce dramatic changes in the geometry and redistribution of charges in the molecule while the lasers are acting and to fully control photodissociation reactions as well as other photochemical processes. Several theoretical proposals taken from the work of the authors are reviewed and analyzed showing the unique features that the strong-laser chemistry opens to control the transient properties and the dynamics of molecules.

  3. Identification of preseizure states in epilepsy: A data-driven approach for multichannel EEG recordings

    Directory of Open Access Journals (Sweden)

    Hinnerk eFeldwisch-Drentrup

    2011-07-01

    Full Text Available The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or nonlinear time series analysis. This approach renders time profiles of a characterizing measure – so-called measure profiles – for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating

  4. Declarative Event-Based Workflow as Distributed Dynamic Condition Response Graphs

    DEFF Research Database (Denmark)

    Hildebrandt, Thomas; Mukkamala, Raghava Rao

    2010-01-01

    We present Dynamic Condition Response Graphs (DCR Graphs) as a declarative, event-based process model inspired by the workflow language employed by our industrial partner and conservatively generalizing prime event structures. A dynamic condition response graph is a directed graph with nodes...... representing the events that can happen and arrows representing four relations between events: condition, response, include, and exclude. Distributed DCR Graphs is then obtained by assigning roles to events and principals. We give a graphical notation inspired by related work by van der Aalst et al. We...... exemplify the use of distributed DCR Graphs on a simple workflow taken from a field study at a Danish hospital, pointing out their flexibility compared to imperative workflow models. Finally we provide a mapping from DCR Graphs to Buchi-automata....

  5. A Data-Driven Reliability Estimation Approach for Phased-Mission Systems

    Directory of Open Access Journals (Sweden)

    Hua-Feng He

    2014-01-01

    Full Text Available We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example.

  6. Cloudweaver: Adaptive and Data-Driven Workload Manager for Generic Clouds

    Science.gov (United States)

    Li, Rui; Chen, Lei; Li, Wen-Syan

    Cloud computing denotes the latest trend in application development for parallel computing on massive data volumes. It relies on clouds of servers to handle tasks that used to be managed by an individual server. With cloud computing, software vendors can provide business intelligence and data analytic services for internet scale data sets. Many open source projects, such as Hadoop, offer various software components that are essential for building a cloud infrastructure. Current Hadoop (and many others) requires users to configure cloud infrastructures via programs and APIs and such configuration is fixed during the runtime. In this chapter, we propose a workload manager (WLM), called CloudWeaver, which provides automated configuration of a cloud infrastructure for runtime execution. The workload management is data-driven and can adapt to dynamic nature of operator throughput during different execution phases. CloudWeaver works for a single job and a workload consisting of multiple jobs running concurrently, which aims at maximum throughput using a minimum set of processors.

  7. Data-driven spatially-adaptive metric adjustment for visual tracking.

    Science.gov (United States)

    Jiang, Nan; Liu, Wenyu

    2014-04-01

    Matching visual appearances of the target over consecutive video frames is a fundamental yet challenging task in visual tracking. Its performance largely depends on the distance metric that determines the quality of visual matching. Rather than using fixed and predefined metric, recent attempts of integrating metric learning-based trackers have shown more robust and promising results, as the learned metric can be more discriminative. In general, these global metric adjustment methods are computationally demanding in real-time visual tracking tasks, and they tend to underfit the data when the target exhibits dynamic appearance variation. This paper presents a nonparametric data-driven local metric adjustment method. The proposed method finds a spatially adaptive metric that exhibits different properties at different locations in the feature space, due to the differences of the data distribution in a local neighborhood. It minimizes the deviation of the empirical misclassification probability to obtain the optimal metric such that the asymptotic error as if using an infinite set of training samples can be approximated. Moreover, by taking the data local distribution into consideration, it is spatially adaptive. Integrating this new local metric learning method into target tracking leads to efficient and robust tracking performance. Extensive experiments have demonstrated the superiority and effectiveness of the proposed tracking method in various tracking scenarios.

  8. Deriving Flood-Mediated Connectivity between River Channels and Floodplains: Data-Driven Approaches

    Science.gov (United States)

    Zhao, Tongtiegang; Shao, Quanxi; Zhang, Yongyong

    2017-03-01

    The flood-mediated connectivity between river channels and floodplains plays a fundamental role in flood hazard mapping and exerts profound ecological effects. The classic nearest neighbor search (NNS) fails to derive this connectivity because of spatial heterogeneity and continuity. We develop two novel data-driven connectivity-deriving approaches, namely, progressive nearest neighbor search (PNNS) and progressive iterative nearest neighbor search (PiNNS). These approaches are illustrated through a case study in Northern Australia. First, PNNS and PiNNS are employed to identify flood pathways on floodplains through forward tracking. That is, progressive search is performed to associate newly inundated cells in each time step to previously inundated cells. In particular, iterations in PiNNS ensure that the connectivity is continuous - the connection between any two cells along the pathway is built through intermediate inundated cells. Second, inundated floodplain cells are collectively connected to river channel cells through backward tracing. Certain river channel sections are identified to connect to a large number of inundated floodplain cells. That is, the floodwater from these sections causes widespread floodplain inundation. Our proposed approaches take advantage of spatial-temporal data. They can be applied to achieve connectivity from hydro-dynamic and remote sensing data and assist in river basin planning and management.

  9. Data-driven analysis of functional brain interactions during free listening to music and speech.

    Science.gov (United States)

    Fang, Jun; Hu, Xintao; Han, Junwei; Jiang, Xi; Zhu, Dajiang; Guo, Lei; Liu, Tianming

    2015-06-01

    Natural stimulus functional magnetic resonance imaging (N-fMRI) such as fMRI acquired when participants were watching video streams or listening to audio streams has been increasingly used to investigate functional mechanisms of the human brain in recent years. One of the fundamental challenges in functional brain mapping based on N-fMRI is to model the brain's functional responses to continuous, naturalistic and dynamic natural stimuli. To address this challenge, in this paper we present a data-driven approach to exploring functional interactions in the human brain during free listening to music and speech streams. Specifically, we model the brain responses using N-fMRI by measuring the functional interactions on large-scale brain networks with intrinsically established structural correspondence, and perform music and speech classification tasks to guide the systematic identification of consistent and discriminative functional interactions when multiple subjects were listening music and speech in multiple categories. The underlying premise is that the functional interactions derived from N-fMRI data of multiple subjects should exhibit both consistency and discriminability. Our experimental results show that a variety of brain systems including attention, memory, auditory/language, emotion, and action networks are among the most relevant brain systems involved in classic music, pop music and speech differentiation. Our study provides an alternative approach to investigating the human brain's mechanism in comprehension of complex natural music and speech.

  10. NERI PROJECT 99-119. TASK 2. DATA-DRIVEN PREDICTION OF PROCESS VARIABLES. FINAL REPORT

    Energy Technology Data Exchange (ETDEWEB)

    Upadhyaya, B.R.

    2003-04-10

    This report describes the detailed results for task 2 of DOE-NERI project number 99-119 entitled ''Automatic Development of Highly Reliable Control Architecture for Future Nuclear Power Plants''. This project is a collaboration effort between the Oak Ridge National Laboratory (ORNL,) The University of Tennessee, Knoxville (UTK) and the North Carolina State University (NCSU). UTK is the lead organization for Task 2 under contract number DE-FG03-99SF21906. Under task 2 we completed the development of data-driven models for the characterization of sub-system dynamics for predicting state variables, control functions, and expected control actions. We have also developed the ''Principal Component Analysis (PCA)'' approach for mapping system measurements, and a nonlinear system modeling approach called the ''Group Method of Data Handling (GMDH)'' with rational functions, and includes temporal data information for transient characterization. The majority of the results are presented in detailed reports for Phases 1 through 3 of our research, which are attached to this report.

  11. Data-Driven CFD Modeling of Turbulent Flows Through Complex Structures

    CERN Document Server

    Wang, Jian-Xun

    2016-01-01

    The growth of computational resources in the past decades has expanded the application of Computational Fluid Dynamics (CFD) from the traditional fields of aerodynamics and hydrodynamics to a number of new areas. Examples range from the heat and fluid flows in nuclear reactor vessels and in data centers to the turbulence flows through wind turbine farms and coastal vegetation plants. However, in these new applications complex structures are often exist (e.g., rod bundles in reactor vessels and turbines in wind farms), which makes fully resolved, first-principle based CFD modeling prohibitively expensive. This obstacle seriously impairs the predictive capability of CFD models in these applications. On the other hand, a limited amount of measurement data is often available in the systems in the above-mentioned applications. In this work we propose a data-driven, physics-based approach to perform full field inversion on the effects of the complex structures on the flow. This is achieved by assimilating observati...

  12. Time-dependent ambulance allocation considering data-driven empirically required coverage.

    Science.gov (United States)

    Degel, Dirk; Wiesche, Lara; Rachuba, Sebastian; Werners, Brigitte

    2015-12-01

    Empirical studies considering the location and relocation of emergency medical service (EMS) vehicles in an urban region provide important insight into dynamic changes during the day. Within a 24-hour cycle, the demand, travel time, speed of ambulances and areas of coverage change. Nevertheless, most existing approaches in literature ignore these variations and require a (temporally and spatially) fixed (double) coverage of the planning area. Neglecting these variations and fixation of the coverage could lead to an inaccurate estimation of the time-dependent fleet size and individual positioning of ambulances. Through extensive data collection, now it is possible to precisely determine the required coverage of demand areas. Based on data-driven optimization, a new approach is presented, maximizing the flexible, empirically determined required coverage, which has been adjusted for variations due to day-time and site. This coverage prevents the EMS system from unavailability of ambulances due to parallel operations to ensure an improved coverage of the planning area closer to realistic demand. An integer linear programming model is formulated in order to locate and relocate ambulances. The use of such a programming model is supported by a comprehensive case study, which strongly suggests that through such a model, these objectives can be achieved and lead to greater cost-effectiveness and quality of emergency care.

  13. Data-driven modelling of structured populations a practical guide to the integral projection model

    CERN Document Server

    Ellner, Stephen P; Rees, Mark

    2016-01-01

    This book is a “How To” guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includes code in the R language (in the text and online) to carry out all computations. The intended audience are ecologists, evolutionary biologists, and mathematical biologists interested in developing data-driven models for animal and plant populations. IPMs may seem hard as they involve integrals. The aim of this book is to demystify IPMs, so they become the model of choice for populations structured by size or other continuously varying traits. The book uses real examples of increasing complexity to show how the life-cycle of the study organism naturally leads to the appropriate statistical analysis, which leads directly to the IPM itself. A wide range of model types and analyses are presented, including model construction, computational methods, and the underlying theory, with the more technical material in B...

  14. Data-driven automatic parking constrained control for four-wheeled mobile vehicles

    Directory of Open Access Journals (Sweden)

    Wenxu Yan

    2016-11-01

    Full Text Available In this article, a novel data-driven constrained control scheme is proposed for automatic parking systems. The design of the proposed scheme only depends on the steering angle and the orientation angle of the car, and it does not involve any model information of the car. Therefore, the proposed scheme-based automatic parking system is applicable to different kinds of cars. In order to further reduce the desired trajectory coordinate tracking errors, a coordinates compensation algorithm is also proposed. In the design procedure of the controller, a novel dynamic anti-windup compensator is used to deal with the change magnitude and rate saturations of automatic parking control input. It is theoretically proven that all the signals in the closed-loop system are uniformly ultimately bounded based on Lyapunov stability analysis method. Finally, a simulation comparison among the proposed scheme with coordinates compensation and Proportion Integration Differentiation (PID control algorithm is given. It is shown that the proposed scheme with coordinates compensation has smaller tracking errors and more rapid responses than PID scheme.

  15. Designing Data-Driven Battery Prognostic Approaches for Variable Loading Profiles: Some Lessons Learned

    Data.gov (United States)

    National Aeronautics and Space Administration — Among various approaches for implementing prognostic algorithms data-driven algorithms are popular in the industry due to their intuitive nature and relatively fast...

  16. Tracking Invasive Alien Species (TrIAS: Building a data-driven framework to inform policy

    Directory of Open Access Journals (Sweden)

    Sonia Vanderhoeven

    2017-05-01

    Full Text Available Imagine a future where dynamically, from year to year, we can track the progression of alien species (AS, identify emerging problem species, assess their current and future risk and timely inform policy in a seamless data-driven workflow. One that is built on open science and open data infrastructures. By using international biodiversity standards and facilities, we would ensure interoperability, repeatability and sustainability. This would make the process adaptable to future requirements in an evolving AS policy landscape both locally and internationally. In recent years, Belgium has developed decision support tools to inform invasive alien species (IAS policy, including information systems, early warning initiatives and risk assessment protocols. However, the current workflows from biodiversity observations to IAS science and policy are slow, not easily repeatable, and their scope is often taxonomically, spatially and temporally limited. This is mainly caused by the diversity of actors involved and the closed, fragmented nature of the sources of these biodiversity data, which leads to considerable knowledge gaps for IAS research and policy. We will leverage expertise and knowledge from nine former and current BELSPO projects and initiatives: Alien Alert, Invaxen, Diars, INPLANBEL, Alien Impact, Ensis, CORDEX.be, Speedy and the Belgian Biodiversity Platform. The project will be built on two components: 1 The establishment of a data mobilization framework for AS data from diverse data sources and 2 the development of data-driven procedures for risk evaluation based on risk modelling, risk mapping and risk assessment. We will use facilities from the Global Biodiversity Information Facility (GBIF, standards from the Biodiversity Information Standards organization (TDWG and expertise from Lifewatch to create and facilitate a systematic workflow. Alien species data will be gathered from a large set of regional, national and international

  17. Estimation of Effectivty Connectivity via Data-Driven Neural Modeling

    Directory of Open Access Journals (Sweden)

    Dean Robert Freestone

    2014-11-01

    Full Text Available This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used the track the mechanisms involved in seizure initiation and termination.

  18. Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

    Directory of Open Access Journals (Sweden)

    Roerdink Jos BTM

    2008-04-01

    Full Text Available Abstract Background We present a simple, data-driven method to extract haemodynamic response functions (HRF from functional magnetic resonance imaging (fMRI time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD technique. HRF data are required for many fMRI applications, such as defining region-specific HRFs, effciently representing a general HRF, or comparing subject-specific HRFs. Results ForWaRD is applied to fMRI time signals, after removing low-frequency trends by a wavelet-based method, and the output of ForWaRD is a time series of volumes, containing the HRF in each voxel. Compared to more complex methods, this extraction algorithm requires few assumptions (separability of signal and noise in the frequency and wavelet domains and the general linear model and it is fast (HRF extraction from a single fMRI data set takes about the same time as spatial resampling. The extraction method is tested on simulated event-related activation signals, contaminated with noise from a time series of real MRI images. An application for HRF data is demonstrated in a simple event-related experiment: data are extracted from a region with significant effects of interest in a first time series. A continuous-time HRF is obtained by fitting a nonlinear function to the discrete HRF coeffcients, and is then used to analyse a later time series. Conclusion With the parameters used in this paper, the extraction method presented here is very robust to changes in signal properties. Comparison of analyses with fitted HRFs and with a canonical HRF shows that a subject-specific, regional HRF significantly improves detection power. Sensitivity and specificity increase not only in the region from which the HRFs are extracted, but also in other regions of interest.

  19. The Potential of Knowing More: A Review of Data-Driven Urban Water Management.

    Science.gov (United States)

    Eggimann, Sven; Mutzner, Lena; Wani, Omar; Schneider, Mariane Yvonne; Spuhler, Dorothee; Moy de Vitry, Matthew; Beutler, Philipp; Maurer, Max

    2017-02-14

    The promise of collecting and utilizing large amounts of data has never been greater in the history of urban water management (UWM). This paper reviews several data-driven approaches which play a key role in bringing forward a sea change. It critically investigates whether data-driven UWM offers a promising foundation for addressing current challenges and supporting fundamental changes in UWM. We discuss the examples of better rain-data management, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation and management, integrated design and management, increasing water productivity, wastewater-based epidemiology and on-site water and wastewater treatment. The accumulated evidence from literature points toward a future UWM that offers significant potential benefits thanks to increased collection and utilization of data. The findings show that data-driven UWM allows us to develop and apply novel methods, to optimize the efficiency of the current network-based approach, and to extend functionality of today's systems. However, generic challenges related to data-driven approaches (e.g., data processing, data availability, data quality, data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access and ownership, current engineering practices and the difficulty of assessing the cost benefits of data-driven UWM.

  20. The influence of data-driven versus conceptually-driven processing on the development of PTSD-like symptoms

    NARCIS (Netherlands)

    M. Kindt; M. van den Hout; A. Arntz; J. Drost

    2008-01-01

    Ehlers and Clark [(2000). A cognitive model of posttraumatic stress disorder. Behaviour Research and Therapy, 38, 319-345] propose that a predominance of data-driven processing during the trauma predicts subsequent PTSD. We wondered whether, apart from data-driven encoding, sustained data-driven pro

  1. Extreme events induced by self-action of laser beams in dynamic nonlinear liquid crystal cells

    Science.gov (United States)

    Bugaychuk, S.; Iljin, A.; Chunikhina, K.

    2017-06-01

    Optical extreme events represent a feature of nonlinear systems where there may emerge individual pulses possessing very high (or very low) intensity hardly probable statistically. Such property is being connected with the generation of solitons in the nonlinear systems. We carry out the first experiments for detection of extreme events during two-wave mixing with nonlinear dynamical liquid crystal (LC) cells. We investigate the statistics of the extreme events in dependence on relation between the duration of a laser pulse and the time characteristic of dynamic grating relaxation in LC cell. Our research shows that the self-diffraction of laser beams with a dynamical grating support the generation of envelope solitons in this system.

  2. Events

    Directory of Open Access Journals (Sweden)

    Igor V. Karyakin

    2016-02-01

    Full Text Available The 9th ARRCN Symposium 2015 was held during 21st–25th October 2015 at the Novotel Hotel, Chumphon, Thailand, one of the most favored travel destinations in Asia. The 10th ARRCN Symposium 2017 will be held during October 2017 in the Davao, Philippines. International Symposium on the Montagu's Harrier (Circus pygargus «The Montagu's Harrier in Europe. Status. Threats. Protection», organized by the environmental organization «Landesbund für Vogelschutz in Bayern e.V.» (LBV was held on November 20-22, 2015 in Germany. The location of this event was the city of Wurzburg in Bavaria.

  3. DYNAMICS AND EFFICIENCY OF EVENTS TOURISM, FACTORS IN GLOBAL ECONOMIC GROWTH

    Directory of Open Access Journals (Sweden)

    Raluca Georgiana Stoian

    2016-09-01

    Full Text Available Meetings, Incentives, Conventions, and Exhibitions (MICE is an elite segment of tourism linked to business tourism. It has become dynamic worldwide in recent years. The efficiency of tourism events emerges with the connection between the corporate world and world travel organizations. This connection is a dynamic link that is profitable for all parties involved. Currently, about 40% of the activity and profit is due to worldwide business travel and the event industry. This paper aims to highlight the efficient role of tourism events through the dynamic “Convention Bureau”, at both the international and Romanian level, in terms of global economic growth. We found from the study of this activity sector that one of the important directions of innovation and raising the competitiveness of the tourist offer of any country is given the additional service diversification by stimulating tourism dynamics of events. The advantages and benefits that may be mentioned in business events tourism are revenues from services such as accommodation, facilities conference, catering, leisure, transport and entertainment. These revenues are stimulating the growth of the world economy.

  4. Event-chain algorithm for the Heisenberg model: Evidence for z ≃1 dynamic scaling

    Science.gov (United States)

    Nishikawa, Yoshihiko; Michel, Manon; Krauth, Werner; Hukushima, Koji

    2015-12-01

    We apply the event-chain Monte Carlo algorithm to the three-dimensional ferromagnetic Heisenberg model. The algorithm is rejection-free and also realizes an irreversible Markov chain that satisfies global balance. The autocorrelation functions of the magnetic susceptibility and the energy indicate a dynamical critical exponent z ≈1 at the critical temperature, while that of the magnetization does not measure the performance of the algorithm. We show that the event-chain Monte Carlo algorithm substantially reduces the dynamical critical exponent from the conventional value of z ≃2 .

  5. Event-chain algorithm for the Heisenberg model: Evidence for z≃1 dynamic scaling.

    Science.gov (United States)

    Nishikawa, Yoshihiko; Michel, Manon; Krauth, Werner; Hukushima, Koji

    2015-12-01

    We apply the event-chain Monte Carlo algorithm to the three-dimensional ferromagnetic Heisenberg model. The algorithm is rejection-free and also realizes an irreversible Markov chain that satisfies global balance. The autocorrelation functions of the magnetic susceptibility and the energy indicate a dynamical critical exponent z≈1 at the critical temperature, while that of the magnetization does not measure the performance of the algorithm. We show that the event-chain Monte Carlo algorithm substantially reduces the dynamical critical exponent from the conventional value of z≃2.

  6. A hybrid evolutionary data driven model for river water quality early warning.

    Science.gov (United States)

    Burchard-Levine, Alejandra; Liu, Shuming; Vince, Francois; Li, Mingming; Ostfeld, Avi

    2014-10-01

    China's fast pace industrialization and growing population has led to several accidental surface water pollution events in the last decades. The government of China, after the 2005 Songhua River incident, has pushed for the development of early warning systems (EWS) for drinking water source protection. However, there are still many weaknesses in EWS in China such as the lack of pollution monitoring and advanced water quality prediction models. The application of Data Driven Models (DDM) such as Artificial Neural Networks (ANN) has acquired recent attention as an alternative to physical models. For a case study in a south industrial city in China, a DDM based on genetic algorithm (GA) and ANN was tested to increase the response time of the city's EWS. The GA-ANN model was used to predict NH3-N, CODmn and TOC variables at station B 2 h ahead of time while showing the most sensitive input variables available at station A, 12 km upstream. For NH3-N, the most sensitive input variables were TOC, CODmn, TP, NH3-N and Turbidity with model performance giving a mean square error (MSE) of 0.0033, mean percent error (MPE) of 6% and regression (R) of 92%. For COD, the most sensitive input variables were Turbidity and CODmn with model performance giving a MSE of 0.201, MPE of 5% and R of 0.87. For TOC, the most sensitive input variables were Turbidity and CODmn with model performance giving a MSE of 0.101, MPE of 2% and R of 0.94. In addition, the GA-ANN model performed better for 8 h ahead of time. For future studies, the use of a GA-ANN modelling technique can be very useful for water quality prediction in Chinese monitoring stations which already measure and have immediately available water quality data.

  7. A 1985-2015 data-driven global reconstruction of GRACE total water storage

    Science.gov (United States)

    Humphrey, Vincent; Gudmundsson, Lukas; Isabelle Seneviratne, Sonia

    2016-04-01

    After thirteen years of measurements, the Gravity Recovery and Climate Experiment (GRACE) mission has enabled for an unprecedented view on total water storage (TWS) variability. However, the relatively short record length, irregular time steps and multiple data gaps since 2011 still represent important limitations to a wider use of this dataset within the hydrological and climatological community especially for applications such as model evaluation or assimilation of GRACE in land surface models. To address this issue, we make use of the available GRACE record (2002-2015) to infer local statistical relationships between detrended monthly TWS anomalies and the main controlling atmospheric drivers (e.g. daily precipitation and temperature) at 1 degree resolution (Humphrey et al., in revision). Long-term and homogeneous monthly time series of detrended anomalies in total water storage are then reconstructed for the period 1985-2015. The quality of this reconstruction is evaluated in two different ways. First we perform a cross-validation experiment to assess the performance and robustness of the statistical model. Second we compare with independent basin-scale estimates of TWS anomalies derived by means of combined atmospheric and terrestrial water-balance using atmospheric water vapor flux convergence and change in atmospheric water vapor content (Mueller et al. 2011). The reconstructed time series are shown to provide robust data-driven estimates of global variations in water storage over large regions of the world. Example applications are provided for illustration, including an analysis of some selected major drought events which occurred before the GRACE era. References Humphrey V, Gudmundsson L, Seneviratne SI (in revision) Assessing global water storage variability from GRACE: trends, seasonal cycle, sub-seasonal anomalies and extremes. Surv Geophys Mueller B, Hirschi M, Seneviratne SI (2011) New diagnostic estimates of variations in terrestrial water storage

  8. A data-driven approach for processing heterogeneous categorical sensor signals

    Science.gov (United States)

    Calderon, Christopher P.; Jones, Austin; Lundberg, Scott; Paffenroth, Randy

    2011-09-01

    False alarms generated by sensors pose a substantial problem to a variety of fusion applications. We focus on situations where the frequency of a genuine alarm is "rare" but the false alarm rate is high. The goal is to mitigate the false alarms while retaining power to detect true events. We propose to utilize data streams contaminated by false alarms (generated in the field) to compute statistics on a single sensor's misclassification rate. The nominal misclassification rate of a deployed sensor is often suspect because it is unlikely that these rates were tuned to the specific environmental conditions in which the sensor was deployed. Recent categorical measurement error methods will be applied to the collection of data streams to "train" the sensors and provide point estimates along with confidence intervals for the parameters characterizing sensor performance. By pooling a relatively small collection of random variables arising from a single sensor and using data-driven misclassification rate estimates along with estimated confidence bands, we show how one can transform the stream of categorical random variables into a test statistic with a limiting standard normal distribution. The procedure shows promise for normalizing sequences of misclassified random variables coming from different sensors (with a priori unknown population parameters) to comparable test statistics; this facilitates fusion through various downstream processing mechanisms. We have explored some possible downstream processing mechanisms that rely on false discovery rate (FDR) methods. The FDR methods exploit the test statistics we have computed in a chemical sensor fusion context where reducing false alarms and maintaining substantial power is important. FDR methods also provide a framework to fuse signals coming from non-chem/bio sensors in order to improve performance. Simulation results illustrating these ideas are presented. Extensions, future work and open problems are also briefly

  9. A data-driven model of biomarker changes in sporadic Alzheimer's disease.

    Science.gov (United States)

    Young, Alexandra L; Oxtoby, Neil P; Daga, Pankaj; Cash, David M; Fox, Nick C; Ourselin, Sebastien; Schott, Jonathan M; Alexander, Daniel C

    2014-09-01

    conversion from both mild cognitive impairment to Alzheimer's disease (P = 2.06 × 10(-7)) and cognitively normal to mild cognitive impairment (P = 0.033). The data-driven model we describe supports hypothetical models of biomarker ordering in amyloid-positive and APOE-positive subjects, but suggests that biomarker ordering in the wider population may diverge from this sequence. The model provides useful disease staging information across the full spectrum of disease progression, from cognitively normal to mild cognitive impairment to Alzheimer's disease. This approach has broad application across neurodegenerative disease, providing insights into disease biology, as well as staging and prognostication.

  10. Dynamic Statistical Characterization of Variation in Source Processes of Microseismic Events

    Science.gov (United States)

    Smith-Boughner, L.; Viegas, G. F.; Urbancic, T.; Baig, A. M.

    2015-12-01

    During a hydraulic fracture, water is pumped at high pressure into a formation. A proppant, typically sand is later injected in the hope that it will make its way into a fracture, keep it open and provide a path for the hydrocarbon to enter the well. This injection can create micro-earthquakes, generated by deformation within the reservoir during treatment. When these injections are monitored, thousands of microseismic events are recorded within several hundred cubic meters. For each well-located event, many source parameters are estimated e.g. stress drop, Savage-Wood efficiency and apparent stress. However, because we are evaluating outputs from a power-law process, the extent to which the failure is impacted by fluid injection or stress triggering is not immediately clear. To better detect differences in source processes, we use a set of dynamic statistical parameters which characterize various force balance assumptions using the average distance to the nearest event, event rate, volume enclosed by the events, cumulative moment and energy from a group of events. One parameter, the Fracability index, approximates the ratio of viscous to elastic forcing and highlights differences in the response time of a rock to changes in stress. These dynamic parameters are applied to a database of more than 90 000 events in a shale-gas play in the Horn River Basin to characterize spatial-temporal variations in the source processes. In order to resolve these differences, a moving window, nearest neighbour approach was used. First, the center of mass of the local distribution was estimated for several source parameters. Then, a set of dynamic parameters, which characterize the response of the rock were estimated. These techniques reveal changes in seismic efficiency and apparent stress and often coincide with marked changes in the Fracability index and other dynamic statistical parameters. Utilizing these approaches allowed for the characterization of fluid injection related

  11. Data-Driven Modeling for UGI Gasification Processes via an Enhanced Genetic BP Neural Network With Link Switches.

    Science.gov (United States)

    Liu, Shida; Hou, Zhongsheng; Yin, Chenkun

    2016-12-01

    In this brief, an enhanced genetic back-propagation neural network with link switches (EGA-BPNN-LS) is proposed to address a data-driven modeling problem for gasification processes inside United Gas Improvement (UGI) gasifiers. The online-measured temperature of crude gas produced during the gasification processes plays a dominant role in the syngas industry; however, it is difficult to model temperature dynamics via first principles due to the practical complexity of the gasification process, especially as reflected by severe changes in the gas temperature resulting from infrequent manipulations of the gasifier in practice. The proposed data-driven modeling approach, EGA-BPNN-LS, incorporates an NN-LS, an EGA, and the Levenberg-Marquardt (LM) algorithm. The approach cannot only learn the relationships between the control input and the system output from historical data using an optimized network structure through a combination of EGA and NN-LS but also makes use of the networks gradient information via the LM algorithm. EGA-BPNN-LS is applied to a set of data collected from the field to model the UGI gasification processes, and the effectiveness of EGA-BPNN-LS is verified.

  12. Data-driven aerosol development in the GEOS-5 modeling and data assimilation system

    Science.gov (United States)

    Darmenov, A.; da Silva, A.; Liu, X.; Colarco, P. R.

    2013-12-01

    Atmospheric aerosols are important radiatively active agents that also affect clouds, atmospheric chemistry, the water cycle, land and ocean biogeochemistry. Furthermore, exposure to anthropogenic and/or natural fine particulates can have negative health effects. No single instrument or model is capable of quantifying the diverse and dynamic nature of aerosols at the range of spatial and temporal scales at which they interact with the other constituents and components of the Earth system. However, applying model-data integration techniques can minimize limitations of individual data products and remedy model deficiencies. The Goddard Earth Observing System Model, Version 5 (GEOS-5) is the latest version of the NASA Global Modeling and Assimilation Office (GMAO) Earth system model. GEOS-5 is a modeling and data assimilation framework well suited for aerosol research. It is being used to perform aerosol re-analysis and near real-time aerosol forecast on a global scale at resolutions comparable to those of aerosol products from modern spaceborne instruments. The aerosol processes in GEOS-5 derive from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) but it is implemented on-line, within the climate model. GEOS-5 aerosol modeling capabilities have recently been enhanced by inclusion of the Modal Aerosol Microphysics module (MAM-7) originally developed in the Community Earth System Model (CESM) model. This work will present examples of data driven model development that include refining parameterization of sea-salt emissions, tuning of biomass burning emissions from vegetation fires and the effect of the updated emissions on the modeled direct aerosol forcing. We will also present results from GOES-5/MAM-7 model evaluation against AOD and particulate pollution datasets, and outline future directions of aerosol data assimilation in the GEOS-5 system.

  13. Application of a data-driven simulation method to the reconstruction of the coronal magnetic field

    Institute of Scientific and Technical Information of China (English)

    Yu-Liang Fan; Hua-Ning Wang; Han He; Xiao-Shuai Zhu

    2012-01-01

    Ever since the magnetohydrodynamic (MHD) method for extrapolation of the solar coronal magnetic field was first developed to study the dynamic evolution of twisted magnetic flux tubes,it has proven to be efficient in the reconstruction of the solar coronal magnetic field.A recent example is the so-called data-driven simulation method (DDSM),which has been demonstrated to be valid by an application to model analytic solutions such as a force-free equilibrium given by Low and Lou.We use DDSM for the observed magnetograms to reconstruct the magnetic field above an active region.To avoid an unnecessary sensitivity to boundary conditions,we use a classical total variation diminishing Lax-Friedrichs formulation to iteratively compute the full MHD equations.In order to incorporate a magnetogram consistently and stably,the bottom boundary conditions are derived from the characteristic method.In our simulation,we change the tangential fields continually from an initial potential field to the vector magnetogram.In the relaxation,the initial potential field is changed to a nonlinear magnetic field until the MHD equilibrium state is reached.Such a stable equilibrium is expected to be able to represent the solar atmosphere at a specified time.By inputting the magnetograms before and after the X3.4 flare that occurred on 2006 December 13,we find a topological change after comparing the magnetic field before and after the flare.Some discussions are given regarding the change of magnetic configuration and current distribution.Furthermore,we compare the reconstructed field line configuration with the coronal loop observations by XRT onboard Hinode.The comparison shows a relatively good correlation.

  14. Kinetic modelling of [{sup 11}C]flumazenil using data-driven methods

    Energy Technology Data Exchange (ETDEWEB)

    Miederer, Isabelle; Ziegler, Sibylle I.; Liedtke, Christoph; Miederer, Matthias; Drzezga, Alexander [Technische Universitaet Muenchen, Department of Nuclear Medicine, Klinikum rechts der Isar, Munich (Germany); Spilker, Mary E. [GE Global Research, Computational Biology and Biostatistics Laboratory, Niscayuna, NY (United States); Sprenger, Till [Technische Universitaet Muenchen, Department of Neurology, Klinikum rechts der Isar, Munich (Germany); Wagner, Klaus J. [Technische Universitaet Muenchen, Department of Anaesthesiology, Klinikum rechts der Isar, Munich (Germany); Boecker, Henning [Universitaet Bonn, Department of Radiology, Bonn (Germany)

    2009-04-15

    [{sup 11}C]Flumazenil (FMZ) is a benzodiazepine receptor antagonist that binds reversibly to central-type gamma-aminobutyric acid (GABA-A) sites. A validated approach for analysis of [{sup 11}C]FMZ is the invasive one-tissue (1T) compartmental model. However, it would be advantageous to analyse FMZ binding with whole-brain pixel-based methods that do not require a-priori hypotheses regarding preselected regions. Therefore, in this study we compared invasive and noninvasive data-driven methods (Logan graphical analysis, LGA; multilinear reference tissue model, MRTM2; spectral analysis, SA; basis pursuit denoising, BPD) with the 1T model. We focused on two aspects: (1) replacing the arterial input function analyses with a reference tissue method using the pons as the reference tissue, and (2) shortening the scan protocol from 90 min to 60 min. Dynamic PET scans were conducted in seven healthy volunteers with arterial blood sampling. Distribution volume ratios (DVRs) were selected as the common outcome measure. The SA, LGA with and without arterial input, and MRTM2 agreed best with the 1T model DVR values. The invasive and noninvasive BPD were slightly less well correlated. The full protocol of a 90-min emission data performed better than the 60-min protocol, but the 60-min protocol still delivered useful data, as assessed by the coefficient of variation, and the correlation and bias analyses. This study showed that the SA, LGA and MRTM2 are valid methods for the quantification of benzodiazepine receptor binding with [{sup 11}C]FMZ using an invasive or noninvasive protocol, and therefore have the potential to reduce the invasiveness of the procedure. (orig.)

  15. Data-Driven Synthesis for Investigating Food Systems Resilience to Climate Change

    Science.gov (United States)

    Magliocca, N. R.; Hart, D.; Hondula, K. L.; Munoz, I.; Shelley, M.; Smorul, M.

    2014-12-01

    The production, supply, and distribution of our food involves a complex set of interactions between farmers, rural communities, governments, and global commodity markets that link important issues such as environmental quality, agricultural science and technology, health and nutrition, rural livelihoods, and social institutions and equality - all of which will be affected by climate change. The production of actionable science is thus urgently needed to inform and prepare the public for the consequences of climate change for local and global food systems. Access to data that spans multiple sectors/domains and spatial and temporal scales is key to beginning to tackle such complex issues. As part of the White House's Climate Data Initiative, the USDA and the National Socio-Environmental Synthesis Center (SESYNC) are launching a new collaboration to catalyze data-driven research to enhance food systems resilience to climate change. To support this collaboration, SESYNC is developing a new "Data to Motivate Synthesis" program designed to engage early career scholars in a highly interactive and dynamic process of real-time data discovery, analysis, and visualization to catalyze new research questions and analyses that would not have otherwise been possible and/or apparent. This program will be supported by an integrated, spatially-enabled cyberinfrastructure that enables the management, intersection, and analysis of large heterogeneous datasets relevant to food systems resilience to climate change. Our approach is to create a series of geospatial abstraction data structures and visualization services that can be used to accelerate analysis and visualization across various socio-economic and environmental datasets (e.g., reconcile census data with remote sensing raster datasets). We describe the application of this approach with a pilot workshop of socio-environmental scholars that will lay the groundwork for the larger SESYNC-USDA collaboration. We discuss the

  16. Analysis of grain boundary dynamics using event detection and cumulative averaging

    Energy Technology Data Exchange (ETDEWEB)

    Gautam, A.; Ophus, C. [National Center for Electron Microscopy, LBNL, Berkeley, CA 94720 (United States); Lançon, F. [Laboratoire de Simulation Atomistique L-Sim, SP2M, INAC, CEA, 38054 Grenoble (France); Denes, P. [National Center for Electron Microscopy, LBNL, Berkeley, CA 94720 (United States); Dahmen, U., E-mail: udahmen@lbl.gov [National Center for Electron Microscopy, LBNL, Berkeley, CA 94720 (United States)

    2015-04-15

    To analyze extended time series of high resolution images, we have employed automated frame-by-frame comparisons that are able to detect dynamic changes in the structure of a grain boundary in Au. Using cumulative averaging of images between events allowed high resolution measurements of the atomic relaxation in the interface with sufficient accuracy for comparison with atomistic models. Cumulative averaging was also used to observe the structural rearrangement of atomic columns at a moving step in the grain boundary. The technique of analyzing changing features in high resolution images by averaging between incidents can be used to deconvolute stochastic events that occur at random intervals and on time scales well beyond that accessible to single-shot imaging. - Highlights: • We have observed dynamic structural changes in extended time series of atomic resolution images. • Application of edge detection in the time domain isolates stochastic events in dynamic observations. • Splitting time series at stochastic events highlights changes in local atomic structure. • Cumulative averaging between events generates precise atomic resolution structural images.

  17. Diagnosing the drivers of rain on snow events in Alaska using dynamical downscaling

    Science.gov (United States)

    Bieniek, P.; Bhatt, U. S.; Lader, R.; Walsh, J. E.; Rupp, S. T.

    2015-12-01

    Rain on snow (ROS) events are fairly rare in Alaska but have broad impacts ranging from economic losses to hazardous driving conditions to difficult caribou foraging due to ice formation on the snow. While rare, these events have recently increased in frequency in Alaska and may continue to increase under the projected warming climate. Dynamically downscaled data are now available for Alaska based on historical reanalysis for 1979-2013, while CMIP5 historical and future scenario downscaling are in progress. These new data offer a detailed, gridded product of rain and snowfall not previously possible in the spatially and temporally coarser reanalysis and GCM output currently available. Preliminary analysis shows that the dynamical downscaled data can identify extreme ROS events in Interior Alaska. The ROS events in the dynamically downscaled data are analyzed against observations and the ERA-Interim reanalysis data used to force the historical downscaling simulations. Additionally, the synoptic atmospheric circulations conditions that correspond to major ROS events in various regions of Alaska are identified with Self-Organizing Map (SOM) analysis. Such analysis is beneficial for operational forecasters with the National Weather Service and for diagnosing the mechanisms of change in future climate projections.

  18. Mind the gap: modelling event-based and millennial-scale landscape dynamics

    NARCIS (Netherlands)

    Baartman, J.E.M.

    2012-01-01

    This research looks at landscape dynamics – erosion and deposition – from two different perspectives: long-term landscape evolution over millennial timescales on the one hand and short-term event-based erosion and deposition at the other hand. For the first, landscape evolution models (L

  19. Dynamic load management in a smart home to participate in demand response events

    DEFF Research Database (Denmark)

    Fernandes, Filipe; Morais, Hugo; Vale, Zita

    2014-01-01

    contribution of this work is to include time constraints in resources management, and the context evaluation in order to ensure the required comfort levels. The dynamic resources management methodology allows a better resources’ management in a demand response event, mainly the ones of long duration...

  20. Incorporating planned activities and events in a dynamic multi-day activity agenda generator

    NARCIS (Netherlands)

    Nijland, L.; Arentze, T.; Timmermans, H.J.P.

    2012-01-01

    Daily agenda formation is influenced by formal commitments, satisfaction of needs surpassing some threshold and the desire to conduct particular activities in anticipation of socially and religiously driven events such as birthdays, Christmas, etc. As part of a research program to develop a dynamic

  1. A dynamic hierarchical clustering method for trajectory-based unusual video event detection.

    Science.gov (United States)

    Jiang, Fan; Wu, Ying; Katsaggelos, Aggelos K

    2009-04-01

    The proposed unusual video event detection method is based on unsupervised clustering of object trajectories, which are modeled by hidden Markov models (HMM). The novelty of the method includes a dynamic hierarchical process incorporated in the trajectory clustering algorithm to prevent model overfitting and a 2-depth greedy search strategy for efficient clustering.

  2. Design of video quality metrics with multi-way data analysis a data driven approach

    CERN Document Server

    Keimel, Christian

    2016-01-01

    This book proposes a data-driven methodology using multi-way data analysis for the design of video-quality metrics. It also enables video- quality metrics to be created using arbitrary features. This data- driven design approach not only requires no detailed knowledge of the human visual system, but also allows a proper consideration of the temporal nature of video using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, the author demonstrates not only that this purely data- driven approach outperforms state-of-the-art video-quality metrics, which are often optimized for specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling. .

  3. Service and Data Driven Multi Business Model Platform in a World of Persuasive Technologies

    DEFF Research Database (Denmark)

    Andersen, Troels Christian; Bjerrum, Torben Cæsar Bisgaard

    2016-01-01

    companies in establishing a service organization that delivers, creates and captures value through service and data driven business models by utilizing their network, resources and customers and/or users. Furthermore, based on literature and collaboration with the case company, the suggestion of a new...... framework provides the necessary construction of how the manufac- turing companies can evolve their current business to provide multi service and data driven business models, using the same resources, networks and customers.......This article provides a new contribution to the concept of business models with the focus on the emerging gap between the usage of data, service and business models by suggesting a framework that function as a service and data driven business model platform. The purpose is to support manufacturing...

  4. Data-driven remaining useful life prognosis techniques stochastic models, methods and applications

    CERN Document Server

    Si, Xiao-Sheng; Hu, Chang-Hua

    2017-01-01

    This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based pro...

  5. Data-Driven Microbial Modeling for Soil Carbon Decomposition and Stabilization

    Science.gov (United States)

    Luo, Yiqi; Chen, Ji; Chen, Yizhao; Feng, Wenting

    2017-04-01

    Microorganisms have long been known to catalyze almost all the soil organic carbon (SOC) transformation processes (e.g., decomposition, stabilization, and mineralization). Representing microbial processes in Earth system models (ESMs) has the potential to improve projections of SOC dynamics. We have recently examined (1) relationships of microbial functions with environmental factors and (2) microbial regulations of decomposition and other key soil processes. According to three lines of evidence, we have developed a data-driven enzyme (DENZY) model to simulate soil microbial decomposition and stabilization. First, our meta-analysis of 64 published field studies showed that field experimental warming significantly increased soil microbial communities abundance, which is negatively correlated with the mean annual temperature. The negative correlation indicates that warming had stronger effects in colder than warmer regions. Second, we found that the SOC decomposition, especially the transfer between labile SOC and protected SOC, is nonlinearly regulated by soil texture parameters, such as sand and silt contents. Third, we conducted a global analysis of the C-degrading enzyme activities, soil respiration, and SOC content under N addition. Our results show that N addition has contrasting effects on cellulase (hydrolytic C-degrading enzymes) and ligninase (oxidative C-degrading enzymes) activities. N-enhanced cellulase activity contributes to the minor stimulation of soil respiration whereas N-induced repression on ligninase activity drives soil C sequestration. Our analysis links the microbial extracellular C-degrading enzymes to the SOC dynamics at ecosystem scales across scores of experimental sites around the world. It offers direct evidence that N-induced changes in microbial community and physiology play fundamental roles in controlling the soil C cycle. Built upon those three lines of empirical evidence, the DENZY model includes two enzyme pools and explicitly

  6. A generalized framework for quantifying the dynamics of EEG event-related desynchronization.

    Directory of Open Access Journals (Sweden)

    Steven Lemm

    2009-08-01

    Full Text Available Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent--to a large extent--from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD, which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization, which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a "conditional ERD," through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli.

  7. Sustainability from the Occurrence of Critical Dynamic Power System Blackout Determined by Using the Stochastic Event Tree Technique

    National Research Council Canada - National Science Library

    Muhammad Murtadha Othman; Nur Ashida Salim; Ismail Musirin

    2017-01-01

    .... This paper presents the proposed stochastic event tree technique used to assess the sustainability against the occurrence of dynamic power system blackout emanating from implication of critical...

  8. Stochastic Optimal Regulation of Nonlinear Networked Control Systems by Using Event-Driven Adaptive Dynamic Programming.

    Science.gov (United States)

    Sahoo, Avimanyu; Jagannathan, Sarangapani

    2017-02-01

    In this paper, an event-driven stochastic adaptive dynamic programming (ADP)-based technique is introduced for nonlinear systems with a communication network within its feedback loop. A near optimal control policy is designed using an actor-critic framework and ADP with event sampled state vector. First, the system dynamics are approximated by using a novel neural network (NN) identifier with event sampled state vector. The optimal control policy is generated via an actor NN by using the NN identifier and value function approximated by a critic NN through ADP. The stochastic NN identifier, actor, and critic NN weights are tuned at the event sampled instants leading to aperiodic weight tuning laws. Above all, an adaptive event sampling condition based on estimated NN weights is designed by using the Lyapunov technique to ensure ultimate boundedness of all the closed-loop signals along with the approximation accuracy. The net result is event-driven stochastic ADP technique that can significantly reduce the computation and network transmissions. Finally, the analytical design is substantiated with simulation results.

  9. Imaging-Duration Embedded Dynamic Scheduling of Earth Observation Satellites for Emergent Events

    Directory of Open Access Journals (Sweden)

    Xiaonan Niu

    2015-01-01

    Full Text Available We present novel two-stage dynamic scheduling of earth observation satellites to provide emergency response by making full use of the duration of the imaging task execution. In the first stage, the multiobjective genetic algorithm NSGA-II is used to produce an optimal satellite imaging schedule schema, which is robust to dynamic adjustment as possible emergent events occur in the future. In the second stage, when certain emergent events do occur, a dynamic adjusting heuristic algorithm (CTM-DAHA is applied to arrange new tasks into the robust imaging schedule. Different from the existing dynamic scheduling methods, the imaging duration is embedded in the two stages to make full use of current satellite resources. In the stage of robust satellite scheduling, total task execution time is used as a robust indicator to obtain a satellite schedule with less imaging time. In other words, more imaging time is preserved for future emergent events. In the stage of dynamic adjustment, a compact task merging strategy is applied to combine both of existing tasks and emergency tasks into a composite task with least imaging time. Simulated experiments indicate that the proposed method can produce a more robust and effective satellite imaging schedule.

  10. Events

    Directory of Open Access Journals (Sweden)

    Igor V. Karyakin

    2015-12-01

    Full Text Available On the April 8-10 of 2014 an International Conference “Birds of Prey in the North Caucasus and Adjacent Regions: distribution, ecology, population dynamics, protection” was held in Sochi National Park, Sochi, Russia. The Saker Falcon Falco cherrug Global Action Plan (SakerGAP has been presented at the 11th Meeting of the Parties of the Bonn Convention (CMS, which took place in Quito (Ecuador on 4-9 November 2014. On the December 17 of 2014 a meeting between inspectors of Nature Reserve “Khakasskiy”, police of Khakasia Republic and experts of Siberian Environmental Center was held in the Nature Reserve “Khakasskiy”. On the December 20 of 2014 an annual meeting of members of Siberian Environmental Center (SEC was held in Akademgorodok, Novosibirsk, Russia. Project leaders presented reports on the main activities and achievements gained in 2014. The Long-eared Owl (Asio otus became the Bird of the Year announced by the public organization "APB-BirdLife Belarus". The 9th ARRCN Symposium 2015 will be held during 21st–25th October 2015 at the Novotel Hotel, Chumphon, Thailand, one of the most favored travel destinations in Asia.

  11. Texts and the Dynamics of Cultural Transfer – Translations as Events

    Directory of Open Access Journals (Sweden)

    Ton Naaijkens

    2008-12-01

    Full Text Available The notion of a moveable text involves projection - projection in the form of interpretation, projection also in the form of translation, so that something like a double movement comes into being. Translations constitute a special case of cultural dynamics as, in a sense, they both repeat and change what was written before. They function and are effective in a new environment. Their outcome is not wholly a new original, such as writers produce, but neither a noncommittal reaction or detached study, such as critics deliver. In translations we see the workings of cultural dynamics in optima forma. In order to interpret these dynamics and the receptional afterlife of a text, a distinction should be made between reception events and reception incidents. The author of the article suggests that there is a strong case to award translations the status of event.

  12. Sensorimotor Event. An Approach to the Dynamic, Embodied and Embedded Nature of Sensorimotor Cognition

    Directory of Open Access Journals (Sweden)

    Oscar eVilarroya

    2014-01-01

    Full Text Available In this paper, I explore the notion of sensorimotor event as the building block of sensorimotor cognition. A sensorimotor event is presented here as a neurally-controlled event that recruits those processes and elements that are necessary to address the demands of the situation in which the individual is involved. The notion of sensorimotor event is intended to subsume the dynamic, embodied, and embedded nature of sensorimotor cognition, in agreement with the satisficing and bricoleur approach to sensorimotor cognition presented elsewhere (Vilarroya 2012. In particular, the notion of sensorimotor event encompasses those relevant neural processes, but also those bodily and environmental elements, that are necessary to deal with the situation in which the individual is involved. This continuum of neural processes as well as bodily and environmental elements can be characterized, and this characterization is considered the basis for the identification of the particular sensorimotor event. Among other consequences, the notion of sensorimotor event suggests a different approach to the classical account of sensory-input mapping onto a motor-output. Instead of characterizing how a neural system responds to an external input, the idea defended here is to characterize how system-in-an-environment responds to its antecedent situation.

  13. Sensorimotor event: an approach to the dynamic, embodied, and embedded nature of sensorimotor cognition.

    Science.gov (United States)

    Vilarroya, Oscar

    2014-01-01

    In this paper, I explore the notion of sensorimotor event as the building block of sensorimotor cognition. A sensorimotor event is presented here as a neurally controlled event that recruits those processes and elements that are necessary to address the demands of the situation in which the individual is involved. The notion of sensorimotor event is intended to subsume the dynamic, embodied, and embedded nature of sensorimotor cognition, in agreement with the satisficing and bricoleur approach to sensorimotor cognition presented elsewhere (Vilarroya, 2012). In particular, the notion of sensorimotor event encompasses those relevant neural processes, but also those bodily and environmental elements, that are necessary to deal with the situation in which the individual is involved. This continuum of neural processes as well as bodily and environmental elements can be characterized, and this characterization is considered the basis for the identification of the particular sensorimotor event. Among other consequences, the notion of sensorimotor event suggests a different approach to the classical account of sensory-input mapping onto a motor output. Instead of characterizing how a neural system responds to an external input, the idea defended here is to characterize how system-in-an-environment responds to its antecedent situation.

  14. Sensorimotor event: an approach to the dynamic, embodied, and embedded nature of sensorimotor cognition

    Science.gov (United States)

    Vilarroya, Oscar

    2014-01-01

    In this paper, I explore the notion of sensorimotor event as the building block of sensorimotor cognition. A sensorimotor event is presented here as a neurally controlled event that recruits those processes and elements that are necessary to address the demands of the situation in which the individual is involved. The notion of sensorimotor event is intended to subsume the dynamic, embodied, and embedded nature of sensorimotor cognition, in agreement with the satisficing and bricoleur approach to sensorimotor cognition presented elsewhere (Vilarroya, 2012). In particular, the notion of sensorimotor event encompasses those relevant neural processes, but also those bodily and environmental elements, that are necessary to deal with the situation in which the individual is involved. This continuum of neural processes as well as bodily and environmental elements can be characterized, and this characterization is considered the basis for the identification of the particular sensorimotor event. Among other consequences, the notion of sensorimotor event suggests a different approach to the classical account of sensory-input mapping onto a motor output. Instead of characterizing how a neural system responds to an external input, the idea defended here is to characterize how system-in-an-environment responds to its antecedent situation. PMID:24427133

  15. The event-driven constant volume method for particle coagulation dynamics

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Monte Carlo (MC) method, which tracks small numbers of the dispersed simulation parti- cles and then describes the dynamic evolution of large numbers of real particles, consti- tutes an important class of methods for the numerical solution of population balance modeling. Particle coagulation dynamics is a complex task for MC. Event-driven MC ex- hibits higher accuracy and efficiency than time-driven MC on the whole. However, these available event-driven MCs track the "equally weighted simulation particle population" and maintain the number of simulated particles within bounds at the cost of "regulating" com- putational domain, which results in some constraints and drawbacks. This study designed the procedure of "differently weighted fictitious particle population" and the corresponding coagulation rule for differently weighted fictitious particles. And then, a new event-driven MC method was promoted to describe the coagulation dynamics between differently weighted fictitious particles, where "constant number scheme" and "stepwise constant number scheme" were developed to maintain the number of fictitious particles within bounds as well as the constant computational domain. The MC is named event-driven constant volume (EDCV) method. The quantitative comparison among several popular MCs shows that the EDCV method has the advantages of computational precision and computational efficiency over other available MCs.

  16. The event-driven constant volume method for particle coagulation dynamics

    Institute of Scientific and Technical Information of China (English)

    ZHAO HaiBo; ZHENG ChuGuang

    2008-01-01

    Monte Carlo (MC) method, which tracks small numbers of the dispersed simulation parti-cles and then describes the dynamic evolution of large numbers of real particles, consti-tutes an important class of methods for the numerical solution of population balance modeling. Particle coagulation dynamics is a complex task for MC. Event-driven MC ex-hibits higher accuracy and efficiency than time-driven MC on the whole. However, these available event-driven MCs track the "equally weighted simulation particle population" and maintain the number of simulated particles within bounds at the cost of "regulating" com-putational domain, which results in some constraints and drawbacks. This study designed the procedure of "differently weighted fictitious particle population" and the corresponding coagulation rule for differently weighted fictitious particles. And then, a new event-driven MC method was promoted to describe the coagulation dynamics between differently weighted fictitious particles, where "constant number scheme" and "stepwise constant number scheme" were developed to maintain the number of fictitious particles within bounds as well as the constant computational domain. The MC is named event-driven constant volume (EDCV) method. The quantitative comparison among several popular MCs shows that the EDCV method has the advantages of computational precision and computational efficiency over other available MCs.

  17. Imaging single endocytic events reveals diversity in clathrin, dynamin and vesicle dynamics.

    Science.gov (United States)

    Mattheyses, Alexa L; Atkinson, Claire E; Simon, Sanford M

    2011-10-01

    The dynamics of clathrin-mediated endocytosis can be assayed using fluorescently tagged proteins and total internal reflection fluorescence microscopy. Many of these proteins, including clathrin and dynamin, are soluble and changes in fluorescence intensity can be attributed either to membrane/vesicle movement or to changes in the numbers of individual molecules. It is important for assays to discriminate between physical membrane events and the dynamics of molecules. Two physical events in endocytosis were investigated: vesicle scission from the plasma membrane and vesicle internalization. Single vesicle analysis allowed the characterization of dynamin and clathrin dynamics relative to scission and internalization. We show that vesicles remain proximal to the plasma membrane for variable amounts of time following scission, and that uncoating of clathrin can occur before or after vesicle internalization. The dynamics of dynamin also vary with respect to scission. Results from assays based on physical events suggest that disappearance of fluorescence from the evanescent field should be re-evaluated as an assay for endocytosis. These results illustrate the heterogeneity of behaviors of endocytic vesicles and the importance of establishing suitable evaluation criteria for biophysical processes.

  18. Data-Driven Learning of Speech Acts Based on Corpora of DVD Subtitles

    Science.gov (United States)

    Kitao, S. Kathleen; Kitao, Kenji

    2013-01-01

    Data-driven learning (DDL) is an inductive approach to language learning in which students study examples of authentic language and use them to find patterns of language use. This inductive approach to learning has the advantages of being learner-centered, encouraging hypothesis testing and learner autonomy, and helping develop learning skills.…

  19. Federal Policy to Local Level Decision-Making: Data Driven Education Planning in Nigeria

    Science.gov (United States)

    Iyengar, Radhika; Mahal, Angelique R.; Felicia, Ukaegbu-Nnamchi Ifeyinwa; Aliyu, Balaraba; Karim, Alia

    2015-01-01

    This article discusses the implementation of local level education data-driven planning as implemented by the Office of the Senior Special Assistant to the President of Nigeria on the Millennium Development Goals (OSSAP-MDGs) in partnership with The Earth Institute, Columbia University. It focuses on the design and implementation of the…

  20. Data-Driven Decision Making: Teachers' Use of Data in the Classroom

    Science.gov (United States)

    Moriarty, Tammy Wu

    2013-01-01

    Data-driven decision making has become an important educational issue in the United States, primarily because of federal and state emphasis on school accountability and achievement. Data use has been highlighted as a key factor in monitoring student progress and informing decision making at various levels of the education system. Federal and state…

  1. Data-driven medicinal chemistry in the era of big data

    NARCIS (Netherlands)

    Lusher, S.J.; McGuire, R.; Schaik, R.C. Van; Nicholson, C.D.; Vlieg, J. de

    2014-01-01

    Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enabl

  2. Data-Driven Hint Generation in Vast Solution Spaces: A Self-Improving Python Programming Tutor

    Science.gov (United States)

    Rivers, Kelly; Koedinger, Kenneth R.

    2017-01-01

    To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not…

  3. Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

    NARCIS (Netherlands)

    Caat, Michael ten; Maurits, Natasha M.; Roerdink, Jos B.T.M.

    2008-01-01

    A typical data- driven visualization of electroencephalography ( EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter, w

  4. Teacher Perceptions and Use of Data-Driven Instruction: A Qualitative Study

    Science.gov (United States)

    Melucci, Laura

    2013-01-01

    The purpose of this study was to determine how teacher perceptions of data and use of data-driven instruction affect student performance in English language arts (ELA). This study investigated teachers' perceptions of using data in the classroom and what supports they need to do so. The goal of the research was to increase the level of knowledge…

  5. Exploring Techniques of Developing Writing Skill in IELTS Preparatory Courses: A Data-Driven Study

    Science.gov (United States)

    Ostovar-Namaghi, Seyyed Ali; Safaee, Seyyed Esmail

    2017-01-01

    Being driven by the hypothetico-deductive mode of inquiry, previous studies have tested the effectiveness of theory-driven interventions under controlled experimental conditions to come up with universally applicable generalizations. To make a case in the opposite direction, this data-driven study aims at uncovering techniques and strategies…

  6. Physical Strength as a Cue to Dominance : A Data-Driven Approach

    NARCIS (Netherlands)

    Toscano, Hugo; Schubert, Thomas W; Dotsch, Ron; Falvello, Virginia; Todorov, Alexander

    2016-01-01

    We investigate both similarities and differences between dominance and strength judgments using a data-driven approach. First, we created statistical face shape models of judgments of both dominance and physical strength. The resulting faces representing dominance and strength were highly similar,

  7. A Case for Reevaluating Teacher's Role in Data-Driven Learning (DDL) of English Articles

    Institute of Scientific and Technical Information of China (English)

    ZHAO Juan

    2013-01-01

    A case study has been made to explore whether the teacher’s role in data-driven learning (DDL) can be minimized. The outcome shows that the teacher’s role in offering an explicit instruction may be indispensable and even central to the acquisi-tion of English articles.

  8. Hybrid models for hydrological forecasting: integration of data-driven and conceptual modelling techniques

    NARCIS (Netherlands)

    Corzo Perez, G.A.

    2009-01-01

    This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following top

  9. Hybrid models for hydrological forecasting: Integration of data-driven and conceptual modelling techniques

    NARCIS (Netherlands)

    Corzo Perez, G.A.

    2009-01-01

    This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following top

  10. Data-driven modeling of nano-nose gas sensor arrays

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård

    2010-01-01

    We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence the nu......-of-the-art machine learning methods and the Bayesian learning paradigm....

  11. Data-Driven Learning: Changing the Teaching of Grammar in EFL Classes

    Science.gov (United States)

    Lin, Ming Huei; Lee, Jia-Ying

    2015-01-01

    This study aims to investigate the experience of six early-career teachers who team-taught grammar to EFL college students using data-driven learning (DDL) for the first time. The results show that the teachers found DDL an innovative and interesting approach to teaching grammar, approved of DDL's capacity to provide more incentives for students…

  12. Using data-driven model-brain mappings to constrain formal models of cognition

    NARCIS (Netherlands)

    Borst, Jelmer P; Nijboer, Menno; Taatgen, Niels A; van Rijn, Hedderik; Anderson, John R

    2015-01-01

    In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping f

  13. Ability Grouping and Differentiated Instruction in an Era of Data-Driven Decision Making

    Science.gov (United States)

    Park, Vicki; Datnow, Amanda

    2017-01-01

    Despite data-driven decision making being a ubiquitous part of policy and school reform efforts, little is known about how teachers use data for instructional decision making. Drawing on data from a qualitative case study of four elementary schools, we examine the logic and patterns of teacher decision making about differentiation and ability…

  14. A framework for the automated data-driven constitutive characterization of composites

    Science.gov (United States)

    J.G. Michopoulos; John Hermanson; T. Furukawa; A. Iliopoulos

    2010-01-01

    We present advances on the development of a mechatronically and algorithmically automated framework for the data-driven identification of constitutive material models based on energy density considerations. These models can capture both the linear and nonlinear constitutive response of multiaxially loaded composite materials in a manner that accounts for progressive...

  15. Data-Driven Hint Generation in Vast Solution Spaces: A Self-Improving Python Programming Tutor

    Science.gov (United States)

    Rivers, Kelly; Koedinger, Kenneth R.

    2017-01-01

    To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not…

  16. Data-driven modeling of nano-nose gas sensor arrays

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård

    2010-01-01

    We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence...

  17. Hybrid models for hydrological forecasting: integration of data-driven and conceptual modelling techniques

    NARCIS (Netherlands)

    Corzo Perez, G.A.

    2009-01-01

    This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following

  18. Hybrid models for hydrological forecasting: Integration of data-driven and conceptual modelling techniques

    NARCIS (Netherlands)

    Corzo Perez, G.A.

    2009-01-01

    This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following

  19. Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities.

    Science.gov (United States)

    Del Sol, Antonio; Thiesen, Hans J; Imitola, Jaime; Carazo Salas, Rafael

    2017-02-02

    Achieving the promises of stem cell science to generate precise disease models and designer cell samples for personalized therapeutics will require harnessing pheno-genotypic cell-level data quantitatively and predictively in the lab and clinic. Those requirements could be met by developing a Big-Data-driven stem cell science strategy and community.

  20. Development of a Scale to Measure Learners' Perceived Preferences and Benefits of Data-Driven Learning

    Science.gov (United States)

    Mizumoto, Atsushi; Chujo, Kiyomi; Yokota, Kenji

    2016-01-01

    In spite of researchers' and practitioners' increasing attention to data-driven learning (DDL) and increasing numbers of DDL studies, a multi-item scale to measure learners' attitude toward DDL has not been developed thus far. In the present study, we developed and validated a psychometric scale to measure learners' perceived preferences and…

  1. Using Flexible Data-Driven Frameworks to Enhance School Psychology Training and Practice

    Science.gov (United States)

    Coleman, Stephanie L.; Hendricker, Elise

    2016-01-01

    While a great number of scientific advances have been made in school psychology, the research to practice gap continues to exist, which has significant implications for training future school psychologists. Training in flexible, data-driven models may help school psychology trainees develop important competencies that will benefit them throughout…

  2. Robust Data-Driven Inference for Density-Weighted Average Derivatives

    DEFF Research Database (Denmark)

    Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael

    This paper presents a new data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density- weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error...

  3. Data-Driven Learning: Changing the Teaching of Grammar in EFL Classes

    Science.gov (United States)

    Lin, Ming Huei; Lee, Jia-Ying

    2015-01-01

    This study aims to investigate the experience of six early-career teachers who team-taught grammar to EFL college students using data-driven learning (DDL) for the first time. The results show that the teachers found DDL an innovative and interesting approach to teaching grammar, approved of DDL's capacity to provide more incentives for students…

  4. A Hybrid Approach to Combine Physically Based and Data-Driven Models in Simulating Sediment Transportation

    NARCIS (Netherlands)

    Sewagudde, S.

    2008-01-01

    The objective of this study is to develop a methodology for hybrid modelling of sedimentation in a coastal basin or large shallow lake where physically based and data driven approaches are combined. This research was broken down into three blocks. The first block explores the possibility of approxim

  5. PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

    DEFF Research Database (Denmark)

    Churchill, Nathan W.; Yourganov, Grigori; Spring, Robyn

    2012-01-01

    challenge for identifying and removing such artifact. This paper presents a multivariate, data-driven method for the characterization and removal of physiological noise in fMRI data, termed PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis). The method identifies high frequency...

  6. Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death

    Directory of Open Access Journals (Sweden)

    Andrew Abboud

    2016-09-01

    Full Text Available Inflammation induced by traumatic brain injury (TBI is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1 and 26 were survivors (GOS > 1. A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS] vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI.

  7. Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death

    Science.gov (United States)

    Abboud, Andrew; Mi, Qi; Puccio, Ava; Okonkwo, David; Buliga, Marius; Constantine, Gregory; Vodovotz, Yoram

    2016-01-01

    Inflammation induced by traumatic brain injury (TBI) is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF) samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1) and 26 were survivors (GOS > 1). A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS]) vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL)-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α) were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI. PMID:27729864

  8. The Heliophysics Event Knowledgebase for the Solar Dynamics Observatory - A User's Perspective

    Science.gov (United States)

    Slater, Gregory L.; Cheung, M.; Hurlburt, N.; Schrijver, C.; Somani, A.; Freeland, S. L.; Timmons, R.; Kobashi, A.; Serafin, J.; Schiff, D.; Seguin, R.

    2010-05-01

    The recently launched Solar Dynamics Observatory (SDO) will generated over 2 petabytes of imagery in its 5 year mission. The Heliophysics Events Knowledgebase (HEK) system has been developed to continuously build a database of solar features and events contributed by a combination of machine recognition algorithms run on every single image, and human interactive data exploration. Access to this growing database is provided through a set of currently existing tools as well as an open source API. We present an overview of the user interface tools including illustrative examples of their use.

  9. Eventful Evolution of Giant Molecular Clouds in Dynamically Evolving Spiral Arms

    CERN Document Server

    Baba, Junichi; Saitoh, Takayuki R

    2016-01-01

    The formation and evolution of giant molecular clouds (GMCs) in spiral galaxies have been investigated in the traditional framework of the combined quasi-stationary density wave and galactic shock model. However, our understanding of the dynamics of spiral arms is changing from the traditional spiral model to a dynamically evolving spiral model. In this study, we investigate the structure and evolution of GMCs in a dynamically evolving spiral arm using a three-dimensional N-body/hydrodynamic simulation of a barred spiral galaxy at parsec-scale resolution. This simulation incorporated self-gravity, molecular hydrogen formation, radiative cooling, heating due to interstellar far-ultraviolet radiation, and stellar feedback by both HII regions and Type-II supernovae. In contrast to a simple expectation based on the traditional spiral model, the GMCs exhibited no systematic evolutionary sequence across the spiral arm. Our simulation showed that the GMCs behaved as highly dynamic objects with eventful lives involvi...

  10. Data-driven region-of-interest selection without inflating Type I error rate.

    Science.gov (United States)

    Brooks, Joseph L; Zoumpoulaki, Alexia; Bowman, Howard

    2017-01-01

    In ERP and other large multidimensional neuroscience data sets, researchers often select regions of interest (ROIs) for analysis. The method of ROI selection can critically affect the conclusions of a study by causing the researcher to miss effects in the data or to detect spurious effects. In practice, to avoid inflating Type I error rate (i.e., false positives), ROIs are often based on a priori hypotheses or independent information. However, this can be insensitive to experiment-specific variations in effect location (e.g., latency shifts) reducing power to detect effects. Data-driven ROI selection, in contrast, is nonindependent and uses the data under analysis to determine ROI positions. Therefore, it has potential to select ROIs based on experiment-specific information and increase power for detecting effects. However, data-driven methods have been criticized because they can substantially inflate Type I error rate. Here, we demonstrate, using simulations of simple ERP experiments, that data-driven ROI selection can indeed be more powerful than a priori hypotheses or independent information. Furthermore, we show that data-driven ROI selection using the aggregate grand average from trials (AGAT), despite being based on the data at hand, can be safely used for ROI selection under many circumstances. However, when there is a noise difference between conditions, using the AGAT can inflate Type I error and should be avoided. We identify critical assumptions for use of the AGAT and provide a basis for researchers to use, and reviewers to assess, data-driven methods of ROI localization in ERP and other studies.

  11. Sign determination methods for the respiratory signal in data-driven PET gating

    Science.gov (United States)

    Bertolli, Ottavia; Arridge, Simon; Wollenweber, Scott D.; Stearns, Charles W.; Hutton, Brian F.; Thielemans, Kris

    2017-04-01

    Patient respiratory motion during PET image acquisition leads to blurring in the reconstructed images and may cause significant artifacts, resulting in decreased lesion detectability, inaccurate standard uptake value calculation and incorrect treatment planning in radiation therapy. To reduce these effects data can be regrouped into (nearly) ‘motion-free’ gates prior to reconstruction by selecting the events with respect to the breathing phase. This gating procedure therefore needs a respiratory signal: on current scanners it is obtained from an external device, whereas with data driven (DD) methods it can be directly obtained from the raw PET data. DD methods thus eliminate the use of external equipment, which is often expensive, needs prior setup and can cause patient discomfort, and they could also potentially provide increased fidelity to the internal movement. DD methods have been recently applied on PET data showing promising results. However, many methods provide signals whose direction with respect to the physical motion is uncertain (i.e. their sign is arbitrary), therefore a maximum in the signal could refer either to the end-inspiration or end-expiration phase, possibly causing inaccurate motion correction. In this work we propose two novel methods, CorrWeights and CorrSino, to detect the correct direction of the motion represented by the DD signal, that is obtained by applying principal component analysis (PCA) on the acquired data. They only require the PET raw data, and they rely on the assumption that one of the major causes of change in the acquired data related to the chest is respiratory motion in the axial direction, that generates a cranio-caudal motion of the internal organs. We also implemented two versions of a published registration-based method, that require image reconstruction. The methods were first applied on XCAT simulations, and later evaluated on cancer patient datasets monitored by the Varian Real-time Position ManagementTM (RPM

  12. Continuous and discrete extreme climatic events affecting the dynamics of a high-arctic reindeer population.

    Science.gov (United States)

    Chan, Kung-Sik; Mysterud, Atle; Øritsland, Nils Are; Severinsen, Torbjørn; Stenseth, Nils Chr

    2005-10-01

    Climate at northern latitudes are currently changing both with regard to the mean and the temporal variability at any given site, increasing the frequency of extreme events such as cold and warm spells. Here we use a conceptually new modelling approach with two different dynamic terms of the climatic effects on a Svalbard reindeer population (the Brøggerhalvøya population) which underwent an extreme icing event ("locked pastures") with 80% reduction in population size during one winter (1993/94). One term captures the continuous and linear effect depending upon the Arctic Oscillation and another the discrete (rare) "event" process. The introduction of an "event" parameter describing the discrete extreme winter resulted in a more parsimonious model. Such an approach may be useful in strongly age-structured ungulate populations, with young and very old individuals being particularly prone to mortality factors during adverse conditions (resulting in a population structure that differs before and after extreme climatic events). A simulation study demonstrates that our approach is able to properly detect the ecological effects of such extreme climate events.

  13. On the dynamics of synoptic scale cyclones associated with flood events in Crete

    Science.gov (United States)

    Flocas, Helena; Katavoutas, George; Tsanis, Ioannis; Iordanidou, Vasiliki

    2015-04-01

    Flood events in the Mediterranean are frequently linked to synoptic scale cyclones, although topographical or anthropogenic factors can play important role. The knowledge of the vertical profile and dynamics of these cyclones can serve as a reliable early flood warning system that can further help in hazard mitigation and risk management planning. Crete is the second largest island in the eastern Mediterranean region, being characterized by high precipitation amounts during winter, frequently causing flood events. The objective of this study is to examine the dynamic and thermodynamic mechanisms at the upper and lower levels responsible for the generation of these events, according to their origin domain. The flooding events were recorded for a period of almost 20 years. The surface cyclones are identified with the aid of MS scheme that was appropriately modified and extensively employed in the Mediterranean region in previous studies. Then, the software VTS, specially developed for the Mediterranean cyclones, was employed to investigate the vertical extension, slope and dynamic/kinematic characteristics of the surface cyclones. Composite maps of dynamic/thermodynamic parameters, such as potential vorticity, temperature advection, divergence, surface fluxes were then constructed before and during the time of the flood. The dataset includes 6-hourly surface and isobaric analyses on a 0.5° x 0.5° regular latitude-longitude grid, as derived from the ERA-INTERIM Reanalysis of the ECMWF. It was found that cyclones associated with flood events in Crete mainly generate over northern Africa or southern eastern Mediterranean region and experience their minimum pressure over Crete or southwestern Greece. About 84% of the cyclones extend up to 500hPa, demonstrating that they are well vertically well-organized systems. The vast majority (almost 84%) of the surface cyclones attains their minimum pressure when their 500 hpa counterparts are located in the NW or SW, confirming

  14. 数据驱动系统方法概述%Notes on Data-driven System Approaches

    Institute of Scientific and Technical Information of China (English)

    许建新; 侯忠生

    2009-01-01

    In this paper, we present several considerations centered around the data-driven system approaches. We briefly explore three main issues: the evolving relationship between off-line and on-line data processing methods, the complementary relationship between the data-driven and model-based methods, and the perspectives of data-driven system approaches. Instead of offering solutions to data-driven system problems, which is impossible at the present level of knowledge and research, in this article we aim at categorizing and classifying open problems, exploring possible directions that may offer alternatives or potentials for the four key fields of interests: control, decision making, scheduling, and fault diagnosis.

  15. Preface: Impacts of extreme climate events and disturbances on carbon dynamics

    Science.gov (United States)

    Xiao, Jingfeng; Liu, Shuguang; Stoy, Paul C.

    2016-06-01

    The impacts of extreme climate events and disturbances (ECE&D) on the carbon cycle have received growing attention in recent years. This special issue showcases a collection of recent advances in understanding the impacts of ECE&D on carbon cycling. Notable advances include quantifying how harvesting activities impact forest structure, carbon pool dynamics, and recovery processes; observed drastic increases of the concentrations of dissolved organic carbon and dissolved methane in thermokarst lakes in western Siberia during a summer warming event; disentangling the roles of herbivores and fire on forest carbon dioxide flux; direct and indirect impacts of fire on the global carbon balance; and improved atmospheric inversion of regional carbon sources and sinks by incorporating disturbances. Combined, studies herein indicate several major research needs. First, disturbances and extreme events can interact with one another, and it is important to understand their overall impacts and also disentangle their effects on the carbon cycle. Second, current ecosystem models are not skillful enough to correctly simulate the underlying processes and impacts of ECE&D (e.g., tree mortality and carbon consequences). Third, benchmark data characterizing the timing, location, type, and magnitude of disturbances must be systematically created to improve our ability to quantify carbon dynamics over large areas. Finally, improving the representation of ECE&D in regional climate/earth system models and accounting for the resulting feedbacks to climate are essential for understanding the interactions between climate and ecosystem dynamics.

  16. Rapid dynamic thinning events during 1985-2010 on Upernavik Isstrøm, West Greenland

    Science.gov (United States)

    Khan, S. A.; Kjaer, K. H.; Korsgaard, N. J.; Wahr, J. M.; Joughin, I. R.; Bamber, J. L.; Csatho, B. M.; van den Broeke, M. R.; Stearns, L. A.; Nielsen, K.; Babonis, G. S.; Hamilton, G. S.; Hurkmans, R. T.; Timm, L. H.

    2011-12-01

    Many glaciers along the southeast and northwest coast of Greenland have accelerated, increasing the ice sheet's contribution to global sea-level rise. Here, we map elevation changes on Upernavik Isstrøm (UI), West Greenland, during 2003-2009 using high-resolution Ice, Cloud and land Elevation Satellite (ICESat) laser altimeter data supplemented with altimeter surveys from NASA's Airborne Topographic Mapper (ATM) during 2002-2010. To assess thinning prior to 2002, we analyze aerial photographs from 1985. We document at least two distinct ice loss events characterized by rapid dynamic thinning, increased ice speed, and a retreat of the calving front. The most recent event coincides with the speedup of several glaciers along the northwest coast of Greenland in 2005, and with changes in the rate of mass loss observed using data from the Gravity Recovery and Climate Experiment (GRACE) satellite gravity mission. The first event of increased ice loss could have also taken place along the extended northwest coast. The total dynamic induced ice volume loss on the frontal portion of UI caused by the two events is 45.5 +/- 5.4 km3 (during 1985-2010), while the total melt induced ice volume loss during this same period is 7.4 +/- 1.3 km3.

  17. Van Allen Probes observations of EMIC events triggered by solar wind dynamic pressure enhancements

    Science.gov (United States)

    Lee, D. Y.; Cho, J.; Roh, S. J.; Shin, D. K.; Hwang, J.; Kim, K. C.; Choi, C.; Kletzing, C.; Wygant, J. R.; Thaller, S. A.; Larsen, B.; Skoug, R. M.

    2015-12-01

    Electromagnetic ion cyclotron (EMIC) waves are one of the key plasma waves that can affect charged particle dynamics in the Earth's inner magnetosphere. One of the generation mechanisms of EMIC waves has long been known to be due to magnetospheric compression due to impact by enhanced solar wind dynamic pressure Pdyn. With the Van Allen Probes observations, we have identified 4 EMIC wave events that are triggered by Pdyn enhancements under northward IMF, prolonged quiet time conditions. We find the following features of the EMIC events. (1) They are triggered immediately at the Pdyn impact and remain active during the same period as the enhanced Pdyn duration. (2) They occur in either H band or He band or both. (3) Two events occur inside the plasmasphere and the other two outside the plasmasphere. (4) The wave polarization, either R or L, are highly elliptical, being close to be linear. (5) The wave normal angles are quite large, well away from being field-aligned. (6) About 10 - 50 keV proton fluxes indicate enhanced flux state with ~90 deg-peaked anisotropy in velocity distribution after the Pdyn impact. (7) From low altitude NOAA POES satellite observations of particles we find no obvious evidence for relativistic electron precipitation due to these Pdyn-triggered EMIC events. We will discuss implications of these observations on wave generation mechanism and interaction with radiation belt electrons.

  18. Spatial patterns of sediment dynamics within a medium-sized watershed over an extreme storm event

    Science.gov (United States)

    Gao, Peng; Zhang, Zhirou

    2016-08-01

    In this study, we quantified spatial patterns of sediment dynamics in a watershed of 311 km2 over an extreme storm event using watershed modeling and statistical analyses. First, we calibrated a watershed model, Dynamic Watershed Simulation Model (DWSM) by comparing the predicted with calculated hydrograph and sedigraph at the outlet for this event. Then we predicted values of event runoff volume (V), peak flow (Qpeak), and two types of event sediment yields for lumped morphological units that contain 42 overland elements and 21 channel segments within the study watershed. Two overland elements and the connected channel segment form a first-order subwatershed, several of which constitute a larger nested subwatershed. Next we examined (i) the relationships between these variables and area (A), precipitation (P), mean slope (S), soil erodibility factor, and percent of crop and pasture lands for all overland elements (i.e., the small spatial scale, SSS), and (ii) those between sediment yield, Qpeak, A, P, and event runoff depth (h) for the first-order and nested subwatersheds along two main creeks of the study watershed (i.e., the larger spatial scales, LSS). We found that at the SSS, sediment yield was nonlinearly well related to A and P, but not Qpeak and h; whereas at the LSS, linear relationships between sediment yield and Qpeak existed, so did the Qpeak-A, and Qpeak-P relationships. This linearity suggests the increased connectivity from the SSS to LSS, which was caused by ignorance of channel processes within overland elements. It also implies that sediment was transported at capacity during the extreme event. So controlling sediment supply from the most erodible overland elements may not efficiently reduce the downstream sediment load.

  19. Influence of a Carrington-like event on the atmospheric chemistry, temperature and dynamics

    Directory of Open Access Journals (Sweden)

    M. Calisto

    2012-06-01

    Full Text Available We have modeled the atmospheric impact of a major solar energetic particle event similar in intensity to what is thought of the Carrington Event of 1–2 September 1859. Ionization rates for the August 1972 solar proton event, which had an energy spectrum comparable to the Carrington Event, were scaled up in proportion to the fluence estimated for both events. We have assumed such an event to take place in the year 2020 in order to investigate the impact on the modern, near future atmosphere. Effects on atmospheric chemistry, temperature and dynamics were investigated using the 3-D Chemistry Climate Model SOCOL v2.0. We find significant responses of NOx, HOx, ozone, temperature and zonal wind. Ozone and NOx have in common an unusually strong and long-lived response to this solar proton event. The model suggests a 3-fold increase of NOx generated in the upper stratosphere lasting until the end of November, and an up to 10-fold increase in upper mesospheric HOx. Due to the NOx and HOx enhancements, ozone reduces by up to 60–80% in the mesosphere during the days after the event, and by up to 20–40% in the middle stratosphere lasting for several months after the event. Total ozone is reduced by up to 20 DU in the Northern Hemisphere and up to 10 DU in the Southern Hemisphere. Free tropospheric and surface air temperatures show a significant cooling of more than 3 K and zonal winds change significantly by 3–5 m s−1 in the UTLS region. In conclusion, a solar proton event, if it took place in the near future with an intensity similar to that ascribed to of the Carrington Event of 1859, must be expected to have a major impact on atmospheric composition throughout the middle atmosphere, resulting in significant and persistent decrease in total ozone.

  20. Influence of a Carrington-like event on the atmospheric chemistry, temperature and dynamics

    Directory of Open Access Journals (Sweden)

    M. Calisto

    2012-09-01

    Full Text Available We have modeled the atmospheric impact of a major solar energetic particle event similar in intensity to what is thought of the Carrington Event of 1–2 September 1859. Ionization rates for the August 1972 solar proton event, which had an energy spectrum comparable to the Carrington Event, were scaled up in proportion to the fluence estimated for both events. We have assumed such an event to take place in the year 2020 in order to investigate the impact on the modern, near future atmosphere. Effects on atmospheric chemistry, temperature and dynamics were investigated using the 3-D Chemistry Climate Model SOCOL v2.0. We find significant responses of NOx, HOx, ozone, temperature and zonal wind. Ozone and NOx have in common an unusually strong and long-lived response to this solar proton event. The model suggests a 3-fold increase of NOx generated in the upper stratosphere lasting until the end of November, and an up to 10-fold increase in upper mesospheric HOx. Due to the NOx and HOx enhancements, ozone reduces by up to 60–80% in the mesosphere during the days after the event, and by up to 20–40% in the middle stratosphere lasting for several months after the event. Total ozone is reduced by up to 20 DU in the Northern Hemisphere and up to 10 DU in the Southern Hemisphere. Free tropospheric and surface air temperatures show a significant cooling of more than 3 K and zonal winds change significantly by 3–5 m s−1 in the UTLS region. In conclusion, a solar proton event, if it took place in the near future with an intensity similar to that ascribed to of the Carrington Event of 1859, must be expected to have a major impact on atmospheric composition throughout the middle atmosphere, resulting in significant and persistent decrease in total ozone.

  1. Dynamical regimes underlying epileptiform events: role of instabilities and bifurcations in brain activity

    Science.gov (United States)

    Perez Velazquez, Jose L.; Cortez, Miguel A.; Snead, O. Carter; Wennberg, Richard

    2003-12-01

    Epileptic seizures represent a sudden and transient change in the synchronised firing of neuronal brain ensembles. While the transition of the collective neuronal activity towards the ictal event is not well understood, some progress has been made using nonlinear time series analysis methods. We present here an analysis of the dynamical regimes of the epileptic activity in three patients suffering from intractable (drug-resistant) seizures, and compare these with the dynamics in rodent epilepsy models. We used the time interval between spikes found in the electroencephalographic recordings as our variable to construct interpeak interval (IPI) time delay plots to study the neuronal interictal (activity between seizures), preictal, and seizure activity. A one-dimensional mapping function was obtained by approximation of the IPI plots with a polynomial. Two main dynamical regimes are obtained from the analysis of the mapping function, derived from the subharmonic bifurcation present in the map: period doubling and intermittency, both of which are observed in human and rat seizures. Hence, our simple model obtained from experimental data captures essential phenomena for the collective dynamics of brain networks, that are found in recordings from human and animal epilepsies. The description of the neuronal dynamics based on one-dimensional maps, widely used in other systems, may prove useful for the understanding of the collective population dynamics of brain activity.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-09-01

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

  3. Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming.

    Science.gov (United States)

    Zhang, Qichao; Zhao, Dongbin; Wang, Ding

    2016-10-18

    In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system. Note that the event-based controller is updated only when the triggering condition is satisfied, which can save the communication resources between the plant and the controller. Then, a single network adaptive dynamic programming structure with experience replay technique is constructed to approach the optimal control policies. The stability of the closed-loop system with the event-based control policy and the augmented control policy is analyzed using the Lyapunov approach. Furthermore, we prove that the minimal intersample time is bounded by a nonzero positive constant, which excludes Zeno behavior during the learning process. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

  4. Data-Driven Temporal Filtering on Teager Energy Time Trajectory for Robust Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    ZHAO Jun-hui; XIE Xiang; KUANG Jing-ming

    2006-01-01

    Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation (TEO) based feature parameter for improving the robustness of speech recognition system against noise.Three kinds of data-driven temporal filters are investigated for the motivation of alleviating the harmful effects that the environmental factors have on the speech. The filters include: principle component analysis (PCA) based filters, linear discriminant analysis (LDA) based filters and minimum classification error (MCE) based filters. Detailed comparative analysis among these temporal filtering approaches applied in Teager energy domain is presented. It is shown that while all of them can improve the recognition performance of the original TEO based feature parameter in adverse environment, MCE based temporal filtering can provide the lowest error rate as SNR decreases than any other algorithms.

  5. Domain-oriented data-driven data mining:a new understanding for data mining

    Institute of Scientific and Technical Information of China (English)

    WANG Guo-yin; WANG Yan

    2008-01-01

    Recent advances in computing, communications, digital storage technologies, and high-throughput dam-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from database. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc.There are many researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunate-ly, most data mining researchers pay much attention to technique problems for developing data mining models and methods,while little to basic issues of data mining. In this paper, we will propose a new understanding for data mining, that is, do-main-oriented data-driven data mining (3DM) model. Some data-driven data mining algorithms developed in our Lab are al-so presented to show its validity.

  6. A data-driven approach for quality assessment of radiologic interpretations.

    Science.gov (United States)

    Hsu, William; Han, Simon X; Arnold, Corey W; Bui, Alex At; Enzmann, Dieter R

    2016-04-01

    Given the increasing emphasis on delivering high-quality, cost-efficient healthcare, improved methodologies are needed to measure the accuracy and utility of ordered diagnostic examinations in achieving the appropriate diagnosis. Here, we present a data-driven approach for performing automated quality assessment of radiologic interpretations using other clinical information (e.g., pathology) as a reference standard for individual radiologists, subspecialty sections, imaging modalities, and entire departments. Downstream diagnostic conclusions from the electronic medical record are utilized as "truth" to which upstream diagnoses generated by radiology are compared. The described system automatically extracts and compares patient medical data to characterize concordance between clinical sources. Initial results are presented in the context of breast imaging, matching 18 101 radiologic interpretations with 301 pathology diagnoses and achieving a precision and recall of 84% and 92%, respectively. The presented data-driven method highlights the challenges of integrating multiple data sources and the application of information extraction tools to facilitate healthcare quality improvement.

  7. Specification and Verification of Multi-user Data-Driven Web Applications

    Science.gov (United States)

    Marcus, Monica

    We propose a model for multi-user data-driven communicating Web applications. An arbitrary number of users may access the application concurrently through Web sites and Web services. A Web service may have an arbitrary number of instances. The interaction between users and Web application is data-driven. Synchronous communication is done by shared access to the database and global application state. Private information may be stored in a local state. Asynchronous communication is done by message passing. A version of first-order linear time temporal logic (LTL-FO) is proposed to express behavioral properties of Web applications. The model is used to formally specify a significant fragment of an e-business application. Some of its desirable properties are expressed as LTL-FO formulas. We study a decision problem, namely whether the model satisfies an LTL-FO formula. We show the undecidability of the unrestricted verification problem and discuss some restrictions that ensure decidability.

  8. CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization

    CERN Document Server

    Zhan, Ruohan

    2016-01-01

    This paper proposes a spatial-Radon domain CT image reconstruction model based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model combines the idea of joint image and Radon domain inpainting model of \\cite{Dong2013X} and that of the data-driven tight frames for image denoising \\cite{cai2014data}. It is different from existing models in that both CT image and its corresponding high quality projection image are reconstructed simultaneously using sparsity priors by tight frames that are adaptively learned from the data to provide optimal sparse approximations. An alternative minimization algorithm is designed to solve the proposed model which is nonsmooth and nonconvex. Convergence analysis of the algorithm is provided. Numerical experiments showed that the SRD-DDTF model is superior to the model by \\cite{Dong2013X} especially in recovering some subtle structures in the images.

  9. Data-driven diagnostics of terrestrial carbon dynamics over North America

    Science.gov (United States)

    The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of t...

  10. Modelling the dynamics of the innovation process : a data-driven agent-based approach

    NARCIS (Netherlands)

    Zhao, Yuanyuan

    2015-01-01

    Decision making on innovation is difficult because innovation involves large numbers of and constantly changing interactions between actors and their activities. Decision makers lack information about these complex interactions. This makes it hard for them to predict the relationships between decisi

  11. Dynamic Data-Driven Prognostics and Condition Monitoring of On-board Electronics

    Science.gov (United States)

    2012-08-27

    Completion Date(f-2): 1 a. 1 b. University of Maryland 3181 Glen L Martin Hall College Park MD 20742 Collaborated with QSI in developing STSA algorithms and...12) 209-212 m sp fl pt Baro altimeter bias 1-sigma 213-216 m sp fl pt Baro altimeter scale factor 1-sigma 217-220 ppm sp fl pt Qualtech Systems, Inc

  12. Dynamic-Data Driven Modeling of Uncertainties and 3D Effects of Porous Shape Memory Alloys

    Science.gov (United States)

    2014-02-03

    have had important impacts on materials used in the medical and aerospace industries [17], [6]. Release mechanisms (bolts that expand and contract) are...Abdullah University of Science & Technology, Thuwal, Saudi Arabia, Petascale hydrologic modeling: needs and challenges, May, 2012. Invited. C. C

  13. An Introduction to a Porous Shape Memory Alloy Dynamic Data Driven Application System

    KAUST Repository

    Douglas, Craig C.

    2012-06-02

    Shape Memory Alloys are capable of changing their crystallographic structure due to changes of temperature and/or stress. Our research focuses on three points: (1) Iterative Homogenization of Porous SMAs: Development of a Multiscale Model of porous SMAs utilizing iterative homogenization and based on existing knowledge of constitutive modeling of polycrystalline SMAs. (2) DDDAS: Develop tools to turn on and off the sensors and heating unit(s), to monitor on-line data streams, to change scales based on incoming data, and to control what type of data is generated. The application must have the capability to be run and steered remotely. (3) Modeling and applications of porous SMA: Vibration isolation devices with SMA and porous SMA components for aerospace applications will be analyzed and tested. Numerical tools for modeling porous SMAs with a second viscous phase will be developed.The outcome will be a robust, three-dimensional, multiscale model of porous SMA that can be used in complicated, real-life structural analysis of SMA components using a DDDAS framework.

  14. Estimation of real-time N load in surface water using dynamic data driven application system

    Science.gov (United States)

    Y. Ouyang; S.M. Luo; L.H. Cui; Q. Wang; J.E. Zhang

    2011-01-01

    Agricultural, industrial, and urban activities are the major sources for eutrophication of surface water ecosystems. Currently, determination of nutrients in surface water is primarily accomplished by manually collecting samples for laboratory analysis, which requires at least 24 h. In other words, little to no effort has been devoted to monitoring real-time variations...

  15. Least squares approach for initial data recovery in dynamic data-driven applications simulations

    KAUST Repository

    Douglas, C.

    2010-12-01

    In this paper, we consider the initial data recovery and the solution update based on the local measured data that are acquired during simulations. Each time new data is obtained, the initial condition, which is a representation of the solution at a previous time step, is updated. The update is performed using the least squares approach. The objective function is set up based on both a measurement error as well as a penalization term that depends on the prior knowledge about the solution at previous time steps (or initial data). Various numerical examples are considered, where the penalization term is varied during the simulations. Numerical examples demonstrate that the predictions are more accurate if the initial data are updated during the simulations. © Springer-Verlag 2011.

  16. Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis

    Directory of Open Access Journals (Sweden)

    Ágatha Nogueira Previdelli

    2016-09-01

    Full Text Available The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis. Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information; thus, we aimed to use both approaches to gain knowledge of adolescents’ dietary patterns. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR. In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits, while, in data-driven analysis, fruits and whole grains were not presented in any pattern. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations.

  17. Image Resolution Enhancement via Data-Driven Parametric Models in the Wavelet Space

    OpenAIRE

    2007-01-01

    We present a data-driven, project-based algorithm which enhances image resolution by extrapolating high-band wavelet coefficients. High-resolution images are reconstructed by alternating the projections onto two constraint sets: the observation constraint defined by the given low-resolution image and the prior constraint derived from the training data at the high resolution (HR). Two types of prior constraints are considered: spatially homogeneous constraint suitable for texture images and p...

  18. Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis

    Science.gov (United States)

    Previdelli, Ágatha Nogueira; de Andrade, Samantha Caesar; Fisberg, Regina Mara; Marchioni, Dirce Maria

    2016-01-01

    The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis. Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information; thus, we aimed to use both approaches to gain knowledge of adolescents’ dietary patterns. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR). In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits), while, in data-driven analysis, fruits and whole grains were not presented in any pattern. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar) components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations. PMID:27669289

  19. Data Driven Marketing in Apple and Back to School Campaign 2011

    OpenAIRE

    Bernátek, Martin

    2011-01-01

    Out of the campaign analysis the most important contribution is that Data-Driven Marketing makes sense only once it is already part of the marketing plan. So the team preparing the marketing plan defines the goals and sets the proper measurement matrix according to those goals. It enables to adjust the marketing plan to extract more value, watch the execution and do adjustments if necessary and evaluate at the end of the campaign.

  20. Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests

    Science.gov (United States)

    2014-10-02

    Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests Erik Frisk1, Mattias Krysander2, and Emil Larsson3 1,2,3 Department of...driven approach using random survival forests is proposed where the prognostic algorithm has access to fleet manage- ment data including 291 variables...but it is also used to, for example, power auxiliary units such as heating and kitchen Erik Frisk et al. This is an open-access article distributed

  1. Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis.

    Science.gov (United States)

    Previdelli, Ágatha Nogueira; de Andrade, Samantha Caesar; Fisberg, Regina Mara; Marchioni, Dirce Maria

    2016-09-23

    The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis. Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information; thus, we aimed to use both approaches to gain knowledge of adolescents' dietary patterns. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR). In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits), while, in data-driven analysis, fruits and whole grains were not presented in any pattern. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar) components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations.

  2. Data-driven non-linear elasticity: constitutive manifold construction and problem discretization

    Science.gov (United States)

    Ibañez, Ruben; Borzacchiello, Domenico; Aguado, Jose Vicente; Abisset-Chavanne, Emmanuelle; Cueto, Elias; Ladeveze, Pierre; Chinesta, Francisco

    2017-07-01

    The use of constitutive equations calibrated from data has been implemented into standard numerical solvers for successfully addressing a variety problems encountered in simulation-based engineering sciences (SBES). However, the complexity remains constantly increasing due to the need of increasingly detailed models as well as the use of engineered materials. Data-Driven simulation constitutes a potential change of paradigm in SBES. Standard simulation in computational mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,\\ldots ), whereas the second one consists of models that scientists have extracted from collected, either natural or synthetic, data. Data-driven (or data-intensive) simulation consists of directly linking experimental data to computers in order to perform numerical simulations. These simulations will employ laws, universally recognized as epistemic, while minimizing the need of explicit, often phenomenological, models. The main drawback of such an approach is the large amount of required data, some of them inaccessible from the nowadays testing facilities. Such difficulty can be circumvented in many cases, and in any case alleviated, by considering complex tests, collecting as many data as possible and then using a data-driven inverse approach in order to generate the whole constitutive manifold from few complex experimental tests, as discussed in the present work.

  3. Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features

    Science.gov (United States)

    Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan

    2016-01-01

    It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training. PMID:27631005

  4. Data-driven matched field processing for Lamb wave structural health monitoring.

    Science.gov (United States)

    Harley, Joel B; Moura, José M F

    2014-03-01

    Matched field processing is a model-based framework for localizing targets in complex propagation environments. In underwater acoustics, it has been extensively studied for improving localization performance in multimodal and multipath media. For guided wave structural health monitoring problems, matched field processing has not been widely applied but is an attractive option for damage localization due to equally complex propagation environments. Although effective, matched field processing is often challenging to implement because it requires accurate models of the propagation environment, and the optimization methods used to generate these models are often unreliable and computationally expensive. To address these obstacles, this paper introduces data-driven matched field processing, a framework to build models of multimodal propagation environments directly from measured data, and then use these models for localization. This paper presents the data-driven framework, analyzes its behavior under unmodeled multipath interference, and demonstrates its localization performance by distinguishing two nearby scatterers from experimental measurements of an aluminum plate. Compared with delay-based models that are commonly used in structural health monitoring, the data-driven matched field processing framework is shown to successfully localize two nearby scatterers with significantly smaller localization errors and finer resolutions.

  5. Using data-driven model-brain mappings to constrain formal models of cognition.

    Directory of Open Access Journals (Sweden)

    Jelmer P Borst

    Full Text Available In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings.

  6. KNMI DataLab experiences in serving data-driven innovations

    Science.gov (United States)

    Noteboom, Jan Willem; Sluiter, Raymond

    2016-04-01

    Climate change research and innovations in weather forecasting rely more and more on (Big) data. Besides increasing data from traditional sources (such as observation networks, radars and satellites), the use of open data, crowd sourced data and the Internet of Things (IoT) is emerging. To deploy these sources of data optimally in our services and products, KNMI has established a DataLab to serve data-driven innovations in collaboration with public and private sector partners. Big data management, data integration, data analytics including machine learning and data visualization techniques are playing an important role in the DataLab. Cross-domain data-driven innovations that arise from public-private collaborative projects and research programmes can be explored, experimented and/or piloted by the KNMI DataLab. Furthermore, advice can be requested on (Big) data techniques and data sources. In support of collaborative (Big) data science activities, scalable environments are offered with facilities for data integration, data analysis and visualization. In addition, Data Science expertise is provided directly or from a pool of internal and external experts. At the EGU conference, gained experiences and best practices are presented in operating the KNMI DataLab to serve data-driven innovations for weather and climate applications optimally.

  7. A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.

    Directory of Open Access Journals (Sweden)

    Warren D Anderson

    2017-07-01

    Full Text Available Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension. We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

  8. Temporal dynamics and impact of event interactions in cyber-social populations

    Science.gov (United States)

    Zhang, Yi-Qing; Li, Xiang

    2013-03-01

    The advance of information technologies provides powerful measures to digitize social interactions and facilitate quantitative investigations. To explore large-scale indoor interactions of a social population, we analyze 18 715 users' Wi-Fi access logs recorded in a Chinese university campus during 3 months, and define event interaction (EI) to characterize the concurrent interactions of multiple users inferred by their geographic coincidences—co-locating in the same small region at the same time. We propose three rules to construct a transmission graph, which depicts the topological and temporal features of event interactions. The vertex dynamics of transmission graph tells that the active durations of EIs fall into the truncated power-law distributions, which is independent on the number of involved individuals. The edge dynamics of transmission graph reports that the transmission durations present a truncated power-law pattern independent on the daily and weekly periodicities. Besides, in the aggregated transmission graph, low-degree vertices previously neglected in the aggregated static networks may participate in the large-degree EIs, which is verified by three data sets covering different sizes of social populations with various rendezvouses. This work highlights the temporal significance of event interactions in cyber-social populations.

  9. Data Use: Data-Driven Decision Making Takes a Big-Picture View of the Needs of Teachers and Students

    Science.gov (United States)

    Bernhardt, Victoria L.

    2009-01-01

    Data-driven decision making is the process of using data to inform decisions to improve teaching and learning. Schools typically engage in two kinds of data-driven decision making--at the school level and at the classroom level. The first leads to the second. In this article, the author describes how Marylin Avenue Elementary School successfully…

  10. A SOI-Based Low Noise and Wide Dynamic Range Event-Driven Detector for X-Ray Imaging

    CERN Document Server

    Shrestha, Sumeet; Kawahito, Shoji; Yasutomi, Keita; Kagawa, Keiichiro; Takeda, Ayaki; Tsuru, Takeshi Go; Arai, Yasuo

    2015-01-01

    A low noise and wide dynamic range event driven detector for the detection of X-Ray energy is realized using 0.2 [um] Silicon on insulator (SOI) technology. Pixel circuits are divided into two parts; signal sensing circuit and event detection circuit. Event detection circuit is activated when X-Ray energy falls into the detector. In-pixel gain selection is implemented for the detection of a small signal and wide band of energy particle. Adaptive gain and capability of correlated double sampling (CDS) technique for the kTC noise canceling of charge detector realizes the low noise and high dynamic range event driven detector.

  11. Dynamic reorganization of Amazon forest structure and canopy illumination from tree and branch fall events

    Science.gov (United States)

    Morton, D. C.; Leitold, V.; Longo, M.; dos-Santos, M. N.; Keller, M. M.; Cook, B.

    2016-12-01

    Amazon forests are dynamic ecosystems that store and cycle globally-significant amounts of atmospheric CO2. Forest inventory plots and atmospheric CO2 measurements integrate long-term and large-scale changes in Amazon forests, respectively, but neither approach captures the dynamic reorganization of Amazon forests at fine spatial and temporal scales necessary to refine estimates of the Amazon forest carbon sink. Here, we used multi-temporal airborne lidar data to characterize changes in canopy structure and illumination in the Brazilian Amazon. Annualized rates of canopy turnover varied four-fold across study sites (1.18 to 4.63% yr-1). Branch fall events (4 - 25 m2) were widespread and accounted for one-third of total canopy turnover. Branch and tree fall events created intermediate or low illumination conditions in 80% of canopy turnover areas, regardless of size, as taller neighbors partially shaded areas with canopy height losses. Importantly, canopy losses also redistributed light to adjacent canopy trees, doubling the canopy area influenced by turnover dynamics. Linking multi-temporal lidar measurements with field data on tree mortality and coarse woody debris, our analysis provides a critical link between existing forest inventory data and next generation ecosystem models with full three-dimensional representation of tropical forest structure and canopy dynamics. Current ecosystem models do not capture the influence of forest structure on canopy illumination, dynamism in canopy light availability over short (1-4 yr) time scales, or contributions from branch falls to canopy turnover. These mechanisms alter Amazon forest productivity over time scales relevant for carbon cycle science and climate mitigation efforts.

  12. Dynamics of pollutant indicators during flood events in a small river under strong anthropogenic pressures

    Science.gov (United States)

    Brion, Natacha; Carbonnel, Vincent; Elskens, Marc; Claeys, Philippe; Verbanck, Michel A.

    2017-04-01

    In densely populated regions, human activities profoundly modify natural water circulation as well as water quality, with increased hydrological risks (floods, droughts,…) and chemical hazards (untreated sewage releases, industrial pollution,…) as consequence. In order to assess water and pollutants dynamics and their mass-balance in strongly modified river system, it is important to take into account high flow events as a significant fraction of water and pollutants loads may occur during these short events which are generally underrepresented in classical mass balance studies. A good example of strongly modified river systems is the Zenne river in and around the city of Brussels (Belgium).The Zenne River (Belgium) is a rather small but dynamic rain fed river (about 10 m3/s in average) that is under the influence of strong contrasting anthropogenic pressures along its stretch. While the upstream part of its basin is rather characterized by agricultural land-use, urban and industrial areas dominate the downstream part. In particular, the city of Brussels (1.1M inhabitants) discharges in the Zenne River amounts of wastewater that are large compared to the natural riverine flow. In order to assess water and pollutants dynamics and their mass-balance in the Zenne hydrographic network, we followed water flows and concentrations of several water quality tracers during several flood episodes with an hourly frequency and at different locations along the stretch of the River. These parameters were chosen as indicators of a whole range of pollutions and anthropogenic activities. Knowledge of the high-frequency pollutants dynamics during floods is required for establishing accurate mass-balances of these elements. We thus report here the dynamics of selected parameters during entire flood events, from the baseline to the decreasing phase and at hourly frequency. Dynamics at contrasting locations, in agricultural or urban environments are compared. In particular, the

  13. A Dynamic Analysis of a Record Breaking Winter Season Blocking Event

    Directory of Open Access Journals (Sweden)

    Andrew D. Jensen

    2015-01-01

    Full Text Available The objective of this work is to study in detail a strong North Pacific, large amplitude, and long-lived blocking event that occurred during January 23–February 16, 2014. Indeed, it was the 11th strongest Northern Hemisphere event lasting longer than 20 days since 1968. This event formed out of the strong ridge that was associated with the devastating drought in the Western United States during the winter season of 2013-2014. This blocking event had many outstanding dynamical characteristics, the chief of which was that it survived an abrupt change in the planetary-scale flow when the Pacific North American pattern index changed from positive to negative in early February. The block then reintensified and persisted into mid-February. Several diagnostic techniques are employed to investigate the change in the planetary-scale flow during early February 2014 that have been applied to blocking before but aren’t as well known in the blocking literature.

  14. Neural dynamics of event segmentation in music: converging evidence for dissociable ventral and dorsal networks.

    Science.gov (United States)

    Sridharan, Devarajan; Levitin, Daniel J; Chafe, Chris H; Berger, Jonathan; Menon, Vinod

    2007-08-02

    The real world presents our sensory systems with a continuous stream of undifferentiated information. Segmentation of this stream at event boundaries is necessary for object identification and feature extraction. Here, we investigate the neural dynamics of event segmentation in entire musical symphonies under natural listening conditions. We isolated time-dependent sequences of brain responses in a 10 s window surrounding transitions between movements of symphonic works. A strikingly right-lateralized network of brain regions showed peak response during the movement transitions when, paradoxically, there was no physical stimulus. Model-dependent and model-free analysis techniques provided converging evidence for activity in two distinct functional networks at the movement transition: a ventral fronto-temporal network associated with detecting salient events, followed in time by a dorsal fronto-parietal network associated with maintaining attention and updating working memory. Our study provides direct experimental evidence for dissociable and causally linked ventral and dorsal networks during event segmentation of ecologically valid auditory stimuli.

  15. Cell-matrix interactions in Schistosomal portal fibrosis: a dynamic event

    Directory of Open Access Journals (Sweden)

    Jean-Alexis Grimaud

    1987-01-01

    Full Text Available In recent years, one of the most significant progress in the understanding of liver diseases was the demonstration that liver fibrosis is a dynamic process resulting from a balance between synthesis and degradation of several matrix components, collagen in particular. Thus, fibrosis has been found to be a very early event during liver diseases, be it of toxic, viral or parasitic origin, and to be spontaneously reversible, either partially or totally. In liver fibrosis cell matrix interactions are dependent on the existence of the many factors (sometimes acting in combination which produce the same events at the cellular and molecular levels. These events are: (i the recruitment of fiber-producing cells, (ii their proliferation, (iii the secretion of matrix constituents of the extracellular matrix, and (iv the remodeling and degradation of the newly formed matrix. All these events represent, at least in principle, a target for a therapeutic intervention aimed at influencing the experimentally induced hepatic fibrosis. In this context, hepatosplenic schistosomiasis is of particular interest, being an immune cell-mediated granulomatous disease and a model of liver fibrosis allowing extensive studies in human and animals as well as providing original in vitro models.

  16. Rapid dynamic thinning events during 1985-2010 on Upernavik Isstrøm, West Greenland

    DEFF Research Database (Denmark)

    Khan, Shfaqat Abbas; Kjær, Kurt H.; Korsgaard, Niels Jákup;

    Many glaciers along the southeast and northwest coast of Greenland have accelerated, increasing the ice sheet's contribution to global sea-level rise. Here, we map elevation changes on Upernavik Isstrøm (UI), West Greenland, during 2003-2009 using high-resolution Ice, Cloud and land Elevation...... Satellite (ICESat) laser altimeter data supplemented with altimeter surveys from NASA's Airborne Topographic Mapper (ATM) during 2002-2010. To assess thinning prior to 2002, we analyze aerial photographs from 1985. We document at least two distinct ice loss events characterized by rapid dynamic thinning......, increased ice speed, and a retreat of the calving front. The most recent event coincides with the speedup of several glaciers along the northwest coast of Greenland in 2005, and with changes in the rate of mass loss observed using data from the Gravity Recovery and Climate Experiment (GRACE) satellite...

  17. Seamless Level 2/Level 3 probabilistic risk assessment using dynamic event tree analysis

    Science.gov (United States)

    Osborn, Douglas Matthew

    The current approach to Level 2 and Level 3 probabilistic risk assessment (PRA) using the conventional event-tree/fault-tree methodology requires pre-specification of event order occurrence which may vary significantly in the presence of uncertainties. Manual preparation of input data to evaluate the possible scenarios arising from these uncertainties may also lead to errors from faulty/incomplete input preparation and their execution using serial runs may lead to computational challenges. A methodology has been developed for Level 2 analysis using dynamic event trees (DETs) that removes these limitations with systematic and mechanized quantification of the impact of aleatory uncertainties on possible consequences and their likelihoods. The methodology is implemented using the Analysis of Dynamic Accident Progression Trees (ADAPT) software. For the purposes of this work, aleatory uncertainties are defined as those arising from the stochastic nature of the processes under consideration, such as the variability of weather, in which the probability of weather patterns is predictable but the conditions at the time of the accident are a matter of chance. Epistemic uncertainties are regarded as those arising from the uncertainty in the model (system code) input parameters (e.g., friction or heat transfer correlation parameters). This work conducts a seamless Level 2/3 PRA using a DET analysis. The research helps to quantify and potentially reduce the magnitude of the source term uncertainty currently experienced in Level 3 PRA. Current techniques have been demonstrated with aleatory uncertainties for environmental releases of radioactive materials. This research incorporates epistemic and aleatory uncertainties in a phenomenologically consistent manner through use of DETs. The DETs were determined using the ADAPT framework and linking ADAPT with MELCOR, MELMACCS, and the MELCOR Accident Consequence Code System, Version 2. Aleatory and epistemic uncertainties incorporated

  18. Implications of interacting microscale habitat heterogeneity and disturbance events on Folsomia candida (Collembola) population dynamics

    DEFF Research Database (Denmark)

    Meli, Mattia; Palmqvist, Annemette; Forbes, Valery E

    2014-01-01

    The authors implemented a fractal algorithm in a spatially explicit individual-based model, in order to generate landscapes with different microscale patterns of habitat fragmentation and disturbance events, and studied their effects on population dynamics of the collembolan Folsomia candida. Among......, they are exposed to natural stressors, which might influence the effects of chemicals on populations. We designed simulation experiments that incorporate these 3 factors, and investigated their effects on populations of F. candida, in presence or absence of behavioural avoidance of contaminated habitat. Simulation...

  19. A systemic approach for managing extreme risk events-dynamic financial analysis

    Directory of Open Access Journals (Sweden)

    Ph.D.Student Rodica Ianole

    2011-12-01

    Full Text Available Following the Black Swan logic, it often happens that what we do not know becomes more relevant that what we (believe to know. The management of extreme risks falls under this paradigm in the sense that it cannot be limited to a static approach based only on objective and easily quantifiable variables. Making appeal to the operational tools developed primarily for the insurance industry, the present paper aims to investigate how dynamic financial analysis (DFA can be used within the framework of extreme risk events.

  20. Predictive Event Triggered Control based on Heuristic Dynamic Programming for Nonlinear Continuous Time Systems

    Science.gov (United States)

    2015-08-17

    our best knowledge , this is the first study of using a “predictive” approach through a model network to design the event-triggered ADP. This is the...investigated in the com- munity before, to our best knowledge , this is the first study of using a “predictive” approach through a model network to...programming has been used to solve the optimal control for many years. However, due to the ” curse of di- mensionality” [9], [10], the adaptive dynamic

  1. Heliophysics Event Knowledgebase for the Solar Dynamics Observatory (SDO) and Beyond

    Science.gov (United States)

    Hurlburt, N.; Cheung, M.; Schrijver, C.; Chang, L.; Freeland, S.; Green, S.; Heck, C.; Jaffey, A.; Kobashi, A.; Schiff, D.; Serafin, J.; Seguin, R.; Slater, G.; Somani, A.; Timmons, R.

    2012-01-01

    The immense volume of data generated by the suite of instruments on the Solar Dynamics Observatory (SDO) requires new tools for efficient identifying and accessing data that is most relevant for research. We have developed the Heliophysics Events Knowledgebase (HEK) to fill this need. The HEK system combines automated data mining using feature-detection methods and high-performance visualization systems for data markup. In addition, web services and clients are provided for searching the resulting metadata, reviewing results, and efficiently accessing the data. We review these components and present examples of their use with SDO data.

  2. A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols.

    Science.gov (United States)

    Pedrotti, Marco; Lei, Shengguang; Dzaack, Jeronimo; Rötting, Matthias

    2011-06-01

    Event detection is the conversion of raw eye-tracking data into events--such as fixations, saccades, glissades, blinks, and so forth--that are relevant for researchers. In eye-tracking studies, event detection algorithms can have a serious impact on higher level analyses, although most studies do not accurately report their settings. We developed a data-driven eyeblink detection algorithm (Identification-Artifact Correction [I-AC]) for 50-Hz eye-tracking protocols. I-AC works by first correcting blink-related artifacts within pupil diameter values and then estimating blink onset and offset. Artifact correction is achieved with data-driven thresholds, and more reliable pupil data are output. Blink parameters are defined according to previous studies on blink-related visual suppression. Blink detection performance was tested with experimental data by visually checking the actual correspondence between I-AC output and participants' eye images, recorded by the eyetracker simultaneously with gaze data. Results showed a 97% correct detection percentage.

  3. Nonlinear data-driven identification of polymer electrolyte membrane fuel cells for diagnostic purposes: A Volterra series approach

    Science.gov (United States)

    Ritzberger, D.; Jakubek, S.

    2017-09-01

    In this work, a data-driven identification method, based on polynomial nonlinear autoregressive models with exogenous inputs (NARX) and the Volterra series, is proposed to describe the dynamic and nonlinear voltage and current characteristics of polymer electrolyte membrane fuel cells (PEMFCs). The structure selection and parameter estimation of the NARX model is performed on broad-band voltage/current data. By transforming the time-domain NARX model into a Volterra series representation using the harmonic probing algorithm, a frequency-domain description of the linear and nonlinear dynamics is obtained. With the Volterra kernels corresponding to different operating conditions, information from existing diagnostic tools in the frequency domain such as electrochemical impedance spectroscopy (EIS) and total harmonic distortion analysis (THDA) are effectively combined. Additionally, the time-domain NARX model can be utilized for fault detection by evaluating the difference between measured and simulated output. To increase the fault detectability, an optimization problem is introduced which maximizes this output residual to obtain proper excitation frequencies. As a possible extension it is shown, that by optimizing the periodic signal shape itself that the fault detectability is further increased.

  4. A Method for Selecting Software for Dynamic Event Analysis II: the Taylor Anvil and Dynamic Brazilian Tests

    Energy Technology Data Exchange (ETDEWEB)

    W. D. Richins; J. M. Lacy; T. K. Larson; S. R. Novascone

    2008-05-01

    New nuclear power reactor designs will require resistance to a variety of possible malevolent attacks as well as traditional dynamic accident scenarios. The design/analysis team may be faced with a broad range of phenomena including air and ground blasts, high-velocity penetrators or shaped charges, and vehicle or aircraft impacts. With a host of software tools available to address these high-energy events, the analysis team must evaluate and select the software most appropriate for their particular set of problems. The accuracy of the selected software should then be validated with respect to the phenomena governing the interaction of the threat and structure. Several software codes are available for the study of blast, impact, and other shock phenomena. At the Idaho National Laboratory (INL), a study is underway to investigate the comparative characteristics of a group of shock and high-strain rate physics codes including ABAQUS, LS-DYNA, CTH, ALEGRA, and ALE-3D. In part I of this report, a series of five benchmark problems to exercise some important capabilities of the subject software was identified. The benchmark problems selected are a Taylor cylinder test, a split Hopkinson pressure bar test, a free air blast, the dynamic splitting tension (Brazilian) test, and projectile penetration of a concrete slab. Part II-- this paper-- reports the results of two of the benchmark problems: the Taylor cylinder and the dynamic Brazilian test. The Taylor cylinder test is a method to determine the dynamic yield properties of materials. The test specimen is a right circular cylinder which is impacted against a theoretically rigid target. The cylinder deforms upon impact, with the final shape depending upon the dynamic yield stress, in turn a function of strain and strain rate. The splitting tension test, or Brazilian test, is a method to measure the tensile strength of concrete using a cylindrical specimen. The specimen is loaded diametrically in compression, producing a

  5. Data-driven background estimation for the H -> tau+tau- -> lh search at 7 TeV with the ATLAS detector

    CERN Document Server

    The ATLAS collaboration

    2010-01-01

    Events characterised by the presence of an electron or muon, plus a hadronically decaying tau lepton and large missing transverse momentum are searched for in 7 TeV proton-proton collision data at the LHC. This event topology is of interest to the search for Higgs bosons with H -> tau+tau- -> lh (l=e,mu and h=hadronically decaying tau). The total integrated luminosity of the analysed data is 310 nb-1. Candidate events with missing transverse momentum above 20 GeV were found, 12 in the electron and 17 in the muon channels. The sum is consistent with 25+-9 expected from a data-driven background estimation and the observed visible mass distribution is agreed with the shape from the estimation. This note describes how this estimation is made, using information from data and Monte Carlo simulation.

  6. StreamSqueeze: a dynamic stream visualization for monitoring of event data

    Science.gov (United States)

    Mansmann, Florian; Krstajic, Milos; Fischer, Fabian; Bertini, Enrico

    2012-01-01

    While in clear-cut situations automated analytical solution for data streams are already in place, only few visual approaches have been proposed in the literature for exploratory analysis tasks on dynamic information. However, due to the competitive or security-related advantages that real-time information gives in domains such as finance, business or networking, we are convinced that there is a need for exploratory visualization tools for data streams. Under the conditions that new events have higher relevance and that smooth transitions enable traceability of items, we propose a novel dynamic stream visualization called StreamSqueeze. In this technique the degree of interest of recent items is expressed through an increase in size and thus recent events can be shown with more details. The technique has two main benefits: First, the layout algorithm arranges items in several lists of various sizes and optimizes the positions within each list so that the transition of an item from one list to the other triggers least visual changes. Second, the animation scheme ensures that for 50 percent of the time an item has a static screen position where reading is most effective and then continuously shrinks and moves to the its next static position in the subsequent list. To demonstrate the capability of our technique, we apply it to large and high-frequency news and syslog streams and show how it maintains optimal stability of the layout under the conditions given above.

  7. An Event-Driven Hybrid Molecular Dynamics and Direct Simulation Monte Carlo Algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Donev, A; Garcia, A L; Alder, B J

    2007-07-30

    A novel algorithm is developed for the simulation of polymer chains suspended in a solvent. The polymers are represented as chains of hard spheres tethered by square wells and interact with the solvent particles with hard core potentials. The algorithm uses event-driven molecular dynamics (MD) for the simulation of the polymer chain and the interactions between the chain beads and the surrounding solvent particles. The interactions between the solvent particles themselves are not treated deterministically as in event-driven algorithms, rather, the momentum and energy exchange in the solvent is determined stochastically using the Direct Simulation Monte Carlo (DSMC) method. The coupling between the solvent and the solute is consistently represented at the particle level, however, unlike full MD simulations of both the solvent and the solute, the spatial structure of the solvent is ignored. The algorithm is described in detail and applied to the study of the dynamics of a polymer chain tethered to a hard wall subjected to uniform shear. The algorithm closely reproduces full MD simulations with two orders of magnitude greater efficiency. Results do not confirm the existence of periodic (cycling) motion of the polymer chain.

  8. Data-driven methods to improve baseflow prediction of a regional groundwater model

    Science.gov (United States)

    Xu, Tianfang; Valocchi, Albert J.

    2015-12-01

    Physically-based models of groundwater flow are powerful tools for water resources assessment under varying hydrologic, climate and human development conditions. One of the most important topics of investigation is how these conditions will affect the discharge of groundwater to rivers and streams (i.e. baseflow). Groundwater flow models are based upon discretized solution of mass balance equations, and contain important hydrogeological parameters that vary in space and cannot be measured. Common practice is to use least squares regression to estimate parameters and to infer prediction and associated uncertainty. Nevertheless, the unavoidable uncertainty associated with physically-based groundwater models often results in both aleatoric and epistemic model calibration errors, thus violating a key assumption for regression-based parameter estimation and uncertainty quantification. We present a complementary data-driven modeling and uncertainty quantification (DDM-UQ) framework to improve predictive accuracy of physically-based groundwater models and to provide more robust prediction intervals. First, we develop data-driven models (DDMs) based on statistical learning techniques to correct the bias of the calibrated groundwater model. Second, we characterize the aleatoric component of groundwater model residual using both parametric and non-parametric distribution estimation methods. We test the complementary data-driven framework on a real-world case study of the Republican River Basin, where a regional groundwater flow model was developed to assess the impact of groundwater pumping for irrigation. Compared to using only the flow model, DDM-UQ provides more accurate monthly baseflow predictions. In addition, DDM-UQ yields prediction intervals with coverage probability consistent with validation data. The DDM-UQ framework is computationally efficient and is expected to be applicable to many geoscience models for which model structural error is not negligible.

  9. Interpreting the power spectrum of Dansgaard-Oeschger events via stochastic dynamical systems

    Science.gov (United States)

    Mitsui, Takahito; Lenoir, Guillaume; Crucifix, Michel

    2017-04-01

    Dansgaard-Oeschger (DO) events are abrupt climate shifts, which are particularly pronounced in the North Atlantic region during glacial periods [Dansgaard et al. 1993]. The signals are most clearly found in δ 18O or log [Ca2+] records of Greenland ice cores. The power spectrum S(f) of DO events has attracted attention over two decades with debates on the apparent 1.5-kyr periodicity [Grootes & Stuiver 1997; Schultz et al. 2002; Ditlevsen et al. 2007] and scaling property over several time scales [Schmitt, Lovejoy, & Schertzer 1995; Rypdal & Rypdal 2016]. The scaling property is written most simply as S(f)˜ f-β , β ≈ 1.4. However, physical as well as underlying dynamics of the periodicity and the scaling property are still not clear. Pioneering works for modelling the spectrum of DO events are done by Cessi (1994) and Ditlevsen (1999), but their model-data comparisons of the spectra are rather qualitative. Here, we show that simple stochastic dynamical systems can generate power spectra statistically consistent with the observed spectra over a wide range of frequency from orbital to the Nyquist frequency (=1/40 yr-1). We characterize the scaling property of the spectrum by defining a local scaling exponentβ _loc. For the NGRIP log [Ca2+] record, the local scaling exponent β _loc increases from ˜ 1 to ˜ 2 as the frequency increases from ˜ 1/5000 yr-1 to ˜ 1/500 yr-1, and β _loc decreases toward zero as the frequency increases from ˜ 1/500 yr-1 to the Nyquist frequency. For the δ 18O record, the local scaling exponent β _loc increases from ˜ 1 to ˜ 1.5 as the frequency increases from ˜ 1/5000 yr^{-1 to ˜ 1/1000 yr-1, and β _loc decreases toward zero as the frequency increases from ˜ 1/1000 yr-1 to the Nyquist frequency. This systematic breaking of a single scaling is reproduced by the simple stochastic models. Especially, the models suggest that the flattening of the spectra starting from multi-centennial scale and ending at the Nyquist frequency

  10. Jet Substructure Templates: Data-driven QCD Backgrounds for Fat Jet Searches

    CERN Document Server

    Cohen, Timothy; Lisanti, Mariangela; Lou, Hou Keong; Wacker, Jay G

    2014-01-01

    QCD is often the dominant background to new physics searches for which jet substructure provides a useful handle. Due to the challenges associated with modeling this background, data-driven approaches are necessary. This paper presents a novel method for determining QCD predictions using templates -- probability distribution functions for jet substructure properties as a function of kinematic inputs. Templates can be extracted from a control region and then used to compute background distributions in the signal region. Using Monte Carlo, we illustrate the procedure with two case studies and show that the template approach effectively models the relevant QCD background. This work strongly motivates the application of these techniques to LHC data.

  11. Jet substructure templates: data-driven QCD backgrounds for fat jet searches

    Energy Technology Data Exchange (ETDEWEB)

    Cohen, Timothy [Theory Group, SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Jankowiak, Martin [Institut für Theoretische Physik, Universität Heidelberg, 69120 Heidelberg (Germany); Lisanti, Mariangela; Lou, Hou Keong [Physics Department, Princeton University, Princeton, NJ 08544 (United States); Wacker, Jay G. [Theory Group, SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States)

    2014-05-05

    QCD is often the dominant background to new physics searches for which jet substructure provides a useful handle. Due to the challenges associated with modeling this background, data-driven approaches are necessary. This paper presents a novel method for determining QCD predictions using templates — probability distribution functions for jet substructure properties as a function of kinematic inputs. Templates can be extracted from a control region and then used to compute background distributions in the signal region. Using Monte Carlo, we illustrate the procedure with two case studies and show that the template approach effectively models the relevant QCD background. This work strongly motivates the application of these techniques to LHC data.

  12. Formal techniques for a data-driven certification of advanced railway signalling systems

    DEFF Research Database (Denmark)

    Fantechi, Alessandro

    2016-01-01

    The technological evolution of railway signalling equipment promises significant increases in transport capacity, in operation regularity, in quality and safety of the service offered.This evolution is based on the massive use of computer control units on board trains and on the ground, that aims...... to advocate the adoption of a novel, data-driven safety certification approach, based on formal verification techniques, focusing on the desired attributes of the exchanged information. A discussion on this issue is presented, based on some initial observations of the needed concepts....

  13. Introducing the new GRASS module g.infer for data-driven rule-based applications

    Directory of Open Access Journals (Sweden)

    Peter Löwe

    2012-10-01

    Full Text Available This paper introduces the new GRASS GIS add-on module g.infer. The module enables rule-based analysis and workflow management in GRASS GIS, via data-driven inference processes based on the expert system shell CLIPS. The paper discusses the theoretical and developmental background that will help prepare the reader to use the module for Knowledge Engineering applications. In addition, potential application scenarios are sketched out, ranging from the rule-driven formulation of nontrivial GIS-classification tasks and GIS workflows to ontology management and intelligent software agents.

  14. A data-driven model for maximization of methane production in a wastewater treatment plant.

    Science.gov (United States)

    Kusiak, Andrew; Wei, Xiupeng

    2012-01-01

    A data-driven approach for maximization of methane production in a wastewater treatment plant is presented. Industrial data collected on a daily basis was used to build the model. Temperature, total solids, volatile solids, detention time and pH value were selected as parameters for the model construction. First, a prediction model of methane production was built by a multi-layer perceptron neural network. Then a particle swarm optimization algorithm was used to maximize methane production based on the model developed in this research. The model resulted in a 5.5% increase in methane production.

  15. Data-driven Discovery: A New Era of Exploiting the Literature and Data

    Directory of Open Access Journals (Sweden)

    Ying Ding

    2016-11-01

    Full Text Available In the current data-intensive era, the traditional hands-on method of conducting scientific research by exploring related publications to generate a testable hypothesis is well on its way of becoming obsolete within just a year or two. Analyzing the literature and data to automatically generate a hypothesis might become the de facto approach to inform the core research efforts of those trying to master the exponentially rapid expansion of publications and datasets. Here, viewpoints are provided and discussed to help the understanding of challenges of data-driven discovery.

  16. FaultBuster: data driven fault detection and diagnosis for industrial systems

    DEFF Research Database (Denmark)

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    Efficient and reliable monitoring systems are mandatory to assure the required security standards in industrial complexes. This paper describes the recent developments of FaultBuster, a purely data-driven diagnostic system. It is designed so to be easily scalable to different monitor tasks....... Multivariate statistical models based on principal components are used to detect abnormal situations. Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most probable diagnosis. Results from the DX 09 Diagnostic Challenge shown strong detection properties, while...

  17. A research about data-driven simulation approach for Rolls-Royce 150 seater engine supply chain

    OpenAIRE

    Shi, Shuaijie

    2007-01-01

    This dissertation is about a data-driven simulation applied on the supply chain improvement project for Rolls-Royce. Rolls-Royce is now planning design an engine for 150 seats Boeing 737. One of requirements of Boeing is less than 65 days lead time. Compared current 2 years lead time, it is a big challenge for Rolls-Royce's supply chain. A data-driven simulation method is applied in this article to solve this problem. The model of 150 seater engine supply chain is built by data-driven simulat...

  18. Spatial and temporal dynamics of superspreading events in the 2014–2015 West Africa Ebola epidemic

    Science.gov (United States)

    Lau, Max S. Y.; Dalziel, Benjamin Douglas; Funk, Sebastian; McClelland, Amanda; Tiffany, Amanda; Riley, Steven; Metcalf, C. Jessica E.; Grenfell, Bryan T.

    2017-01-01

    The unprecedented scale of the Ebola outbreak in Western Africa (2014–2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion (∼61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures. PMID:28193880

  19. Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications.

    Science.gov (United States)

    Kalantari, Zahra; Cavalli, Marco; Cantone, Carolina; Crema, Stefano; Destouni, Georgia

    2017-03-01

    Climate-driven increase in the frequency of extreme hydrological events is expected to impose greater strain on the built environment and major transport infrastructure, such as roads and railways. This study develops a data-driven spatial-statistical approach to quantifying and mapping the probability of flooding at critical road-stream intersection locations, where water flow and sediment transport may accumulate and cause serious road damage. The approach is based on novel integration of key watershed and road characteristics, including also measures of sediment connectivity. The approach is concretely applied to and quantified for two specific study case examples in southwest Sweden, with documented road flooding effects of recorded extreme rainfall. The novel contributions of this study in combining a sediment connectivity account with that of soil type, land use, spatial precipitation-runoff variability and road drainage in catchments, and in extending the connectivity measure use for different types of catchments, improve the accuracy of model results for road flood probability. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. Evaluation of System Dynamic Responses during a SWR Event in KALIMER-600

    Energy Technology Data Exchange (ETDEWEB)

    Eoh, Jae Hyuk; Kim, Se Yun; Kim, Seong O

    2006-03-15

    A sodium-water reaction (SWR) has been considered as one of the most important safety issues to be resolved for designing the steam generator and the related systems of a sodium-cooled fast reactor (SFR). Since the system dynamic responses during a SWR event obviously show different characteristics between the initial wave propagation stage and the long-term period of a bulk motion, its analysis should also be performed for both major events in general. Based on the considerations of fundamental features of SWR phenomena, the whole stage of a SWR event in KALIMER-600 including the initial wave propagation and the long-term mass and energy transfer was evaluated by using the SPIKE and the SELPSTA code, which are developed for solving initial wave propagation phenomena and for considering long-term mass and energy transfer phenomena, respectively. In this study, to simulate the SWR event of KALIMER-600, the procedures of the input parameter preparation for both codes such as geometry data, water/steam leak rate, and hydrogen generation data, etc. are provided, and the pressure transient analyses were made on the basis of organized code systems. By using the evaluation results preformed in this study, the guidelines for an appropriate pressure relief system design including R/D break pressure, SG sizing, etc. are also proposed with sufficient considerations of the system design features. It is expected that the results of this study will contribute to the design improvement of SG and IHTS and design optimization of the SWR mitigation system in KALIMER-600 in the future.

  1. Using the relational event model (REM) to investigate the temporal dynamics of animal social networks.

    Science.gov (United States)

    Tranmer, Mark; Marcum, Christopher Steven; Morton, F Blake; Croft, Darren P; de Kort, Selvino R

    2015-03-01

    Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula, in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

  2. Visualization of early events in acetic acid denaturation of HIV-1 protease: a molecular dynamics study.

    Directory of Open Access Journals (Sweden)

    Aditi Narendra Borkar

    Full Text Available Protein denaturation plays a crucial role in cellular processes. In this study, denaturation of HIV-1 Protease (PR was investigated by all-atom MD simulations in explicit solvent. The PR dimer and monomer were simulated separately in 9 M acetic acid (9 M AcOH solution and water to study the denaturation process of PR in acetic acid environment. Direct visualization of the denaturation dynamics that is readily available from such simulations has been presented. Our simulations in 9 M AcOH reveal that the PR denaturation begins by separation of dimer into intact monomers and it is only after this separation that the monomer units start denaturing. The denaturation of the monomers is flagged off by the loss of crucial interactions between the α-helix at C-terminal and surrounding β-strands. This causes the structure to transit from the equilibrium dynamics to random non-equilibrating dynamics. Residence time calculations indicate that denaturation occurs via direct interaction of the acetic acid molecules with certain regions of the protein in 9 M AcOH. All these observations have helped to decipher a picture of the early events in acetic acid denaturation of PR and have illustrated that the α-helix and the β-sheet at the C-terminus of a native and functional PR dimer should maintain both the stability and the function of the enzyme and thus present newer targets for blocking PR function.

  3. Emergence of the ability to perceive dynamic events from still pictures in human infants.

    Science.gov (United States)

    Shirai, Nobu; Imura, Tomoko

    2016-11-17

    The ability to understand a visual scene depicted in a still image is among the abilities shared by all human beings. The aim of the present study was to examine when human infants acquire the ability to perceive the dynamic events depicted in still images (implied motion perception). To this end, we tested whether 4- and 5-month-old infants shifted their gaze toward the direction cued by a dynamic running action depicted in a still figure of a person. Results indicated that the 5- but not the 4-month-olds showed a significant gaze shift toward the direction implied by the posture of the runner (Experiments 1, 2, and 3b). Moreover, the older infants showed no significant gaze shift toward the direction cued by control stimuli, which depicted a figure in a non-dynamic standing posture (Experiment 1), an inverted running figure (Experiment 2), and some of the body parts of a running figure (Experiment 3a). These results suggest that only the older infants responded in the direction of the implied running action of the still figure; thus, implied motion perception emerges around 5 months of age in human infants.

  4. A Data-Driven Approach to Reverse Engineering Customer Engagement Models: Towards Functional Constructs

    Science.gov (United States)

    de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo

    2014-01-01

    Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The ‘communities’ of questionnaire items that emerge from our community detection method form possible ‘functional constructs’ inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such ‘functional constructs’ suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling. PMID:25036766

  5. Data driven approaches vs. qualitative approaches in climate change impact and vulnerability assessment.

    Science.gov (United States)

    Zebisch, Marc; Schneiderbauer, Stefan; Petitta, Marcello

    2015-04-01

    In the last decade the scope of climate change science has broadened significantly. 15 years ago the focus was mainly on understanding climate change, providing climate change scenarios and giving ideas about potential climate change impacts. Today, adaptation to climate change has become an increasingly important field of politics and one role of science is to inform and consult this process. Therefore, climate change science is not anymore focusing on data driven approaches only (such as climate or climate impact models) but is progressively applying and relying on qualitative approaches including opinion and expertise acquired through interactive processes with local stakeholders and decision maker. Furthermore, climate change science is facing the challenge of normative questions, such us 'how important is a decrease of yield in a developed country where agriculture only represents 3% of the GDP and the supply with agricultural products is strongly linked to global markets and less depending on local production?'. In this talk we will present examples from various applied research and consultancy projects on climate change vulnerabilities including data driven methods (e.g. remote sensing and modelling) to semi-quantitative and qualitative assessment approaches. Furthermore, we will discuss bottlenecks, pitfalls and opportunities in transferring climate change science to policy and decision maker oriented climate services.

  6. An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models

    Science.gov (United States)

    Taormina, R.; Galelli, S.; Karakaya, G.; Ahipasaoglu, S. D.

    2016-11-01

    This work investigates the uncertainty associated to the presence of multiple subsets of predictors yielding data-driven models with the same, or similar, predictive accuracy. To handle this uncertainty effectively, we introduce a novel input variable selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), specifically conceived to identify all alternate subsets of predictors in a given dataset. The search process is based on a four-objective optimization problem that minimizes the number of selected predictors, maximizes the predictive accuracy of a data-driven model and optimizes two information theoretic metrics of relevance and redundancy, which guarantee that the selected subsets are highly informative and with little intra-subset similarity. The algorithm is first tested on two synthetic test problems and then demonstrated on a real-world streamflow prediction problem in the Yampa River catchment (US). Results show that complex hydro-meteorological datasets are characterized by a large number of alternate subsets of predictors, which provides useful insights on the underlying physical processes. Furthermore, the presence of multiple subsets of predictors-and associated models-helps find a better trade-off between different measures of predictive accuracy commonly adopted for hydrological modelling problems.

  7. Data-driven HR how to use analytics and metrics to drive performance

    CERN Document Server

    Marr, Bernard

    2018-01-01

    Traditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-driven HR is a practical guide which enables HR practitioners to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, how to collect information in a transparent way that is in line with data protection requirements and how to turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR profession...

  8. Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images.

    Directory of Open Access Journals (Sweden)

    Karin Wolffhechel

    Full Text Available Several lines of evidence suggest that facial cues of adiposity may be important for human social interaction. However, tests for quantifiable cues of body mass index (BMI in the face have examined only a small number of facial proportions and these proportions were found to have relatively low predictive power. Here we employed a data-driven approach in which statistical models were built using principal components (PCs derived from objectively defined shape and color characteristics in face images. The predictive power of these models was then compared with models based on previously studied facial proportions (perimeter-to-area ratio, width-to-height ratio, and cheek-to-jaw width. Models based on 2D shape-only PCs, color-only PCs, and 2D shape and color PCs combined each performed significantly and substantially better than models based on one or more of the previously studied facial proportions. A non-linear PC model considering both 2D shape and color PCs was the best predictor of BMI. These results highlight the utility of a "bottom-up", data-driven approach for assessing BMI from face images.

  9. Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images.

    Science.gov (United States)

    Wolffhechel, Karin; Hahn, Amanda C; Jarmer, Hanne; Fisher, Claire I; Jones, Benedict C; DeBruine, Lisa M

    2015-01-01

    Several lines of evidence suggest that facial cues of adiposity may be important for human social interaction. However, tests for quantifiable cues of body mass index (BMI) in the face have examined only a small number of facial proportions and these proportions were found to have relatively low predictive power. Here we employed a data-driven approach in which statistical models were built using principal components (PCs) derived from objectively defined shape and color characteristics in face images. The predictive power of these models was then compared with models based on previously studied facial proportions (perimeter-to-area ratio, width-to-height ratio, and cheek-to-jaw width). Models based on 2D shape-only PCs, color-only PCs, and 2D shape and color PCs combined each performed significantly and substantially better than models based on one or more of the previously studied facial proportions. A non-linear PC model considering both 2D shape and color PCs was the best predictor of BMI. These results highlight the utility of a "bottom-up", data-driven approach for assessing BMI from face images.

  10. Data-driven risk identification in phase III clinical trials using central statistical monitoring.

    Science.gov (United States)

    Timmermans, Catherine; Venet, David; Burzykowski, Tomasz

    2016-02-01

    Our interest lies in quality control for clinical trials, in the context of risk-based monitoring (RBM). We specifically study the use of central statistical monitoring (CSM) to support RBM. Under an RBM paradigm, we claim that CSM has a key role to play in identifying the "risks to the most critical data elements and processes" that will drive targeted oversight. In order to support this claim, we first see how to characterize the risks that may affect clinical trials. We then discuss how CSM can be understood as a tool for providing a set of data-driven key risk indicators (KRIs), which help to organize adaptive targeted monitoring. Several case studies are provided where issues in a clinical trial have been identified thanks to targeted investigation after the identification of a risk using CSM. Using CSM to build data-driven KRIs helps to identify different kinds of issues in clinical trials. This ability is directly linked with the exhaustiveness of the CSM approach and its flexibility in the definition of the risks that are searched for when identifying the KRIs. In practice, a CSM assessment of the clinical database seems essential to ensure data quality. The atypical data patterns found in some centers and variables are seen as KRIs under a RBM approach. Targeted monitoring or data management queries can be used to confirm whether the KRIs point to an actual issue or not.

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

    Science.gov (United States)

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

    2010-01-01

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

  12. A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

    Directory of Open Access Journals (Sweden)

    Natalie Jane de Vries

    Full Text Available Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

  13. Data-driven integration of genome-scale regulatory and metabolic network models

    Directory of Open Access Journals (Sweden)

    Saheed eImam

    2015-05-01

    Full Text Available Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription and signaling have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert – a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

  14. The effects of data-driven learning activities on EFL learners' writing development.

    Science.gov (United States)

    Luo, Qinqin

    2016-01-01

    Data-driven learning has been proved as an effective approach in helping learners solve various writing problems such as correcting lexical or grammatical errors, improving the use of collocations and generating ideas in writing, etc. This article reports on an empirical study in which data-driven learning was accomplished with the assistance of the user-friendly BNCweb, and presents the evaluation of the outcome by comparing the effectiveness of BNCweb and a search engine Baidu which is most commonly used as reference resource by Chinese learners of English as a foreign language. The quantitative results about 48 Chinese college students revealed that the experimental group which used BNCweb performed significantly better in the post-test in terms of writing fluency and accuracy, as compared with the control group which used the search engine Baidu. However, no significant difference was found between the two groups in terms of writing complexity. The qualitative results about the interview revealed that learners generally showed a positive attitude toward the use of BNCweb but there were still some problems of using corpora in the writing process, thus the combined use of corpora and other types of reference resource was suggested as a possible way to counter the potential barriers for Chinese learners of English.

  15. A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

    Science.gov (United States)

    de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo

    2014-01-01

    Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

  16. Data-driven normalization strategies for high-throughput quantitative RT-PCR

    Directory of Open Access Journals (Sweden)

    Suzuki Harukazu

    2009-04-01

    Full Text Available Abstract Background High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand, and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline. Results We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project. Conclusion The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.

  17. Using data-driven discrete-time models and the unscented Kalman filter to estimate unobserved variables of nonlinear systems

    Science.gov (United States)

    Aguirre, Luis Antonio; Teixeira, Bruno Otávio S.; Tôrres, Leonardo Antônio B.

    2005-08-01

    This paper addresses the problem of state estimation for nonlinear systems by means of the unscented Kalman filter (UKF). Compared to the traditional extended Kalman filter, the UKF does not require the local linearization of the system equations used in the propagation stage. Important results using the UKF have been reported recently but in every case the system equations used by the filter were considered known. Not only that, such models are usually considered to be differential equations, which requires that numerical integration be performed during the propagation phase of the filter. In this paper the dynamical equations of the system are taken to be difference equations—thus avoiding numerical integration—and are built from data without prior knowledge. The identified models are subsequently implemented in the filter in order to accomplish state estimation. The paper discusses the impact of not knowing the exact equations and using data-driven models in the context of state and joint state-and-parameter estimation. The procedure is illustrated by means of examples that use simulated and measured data.

  18. Sensitivity of a data-driven soil water balance model to estimate summer evapotranspiration along a forest chronosequence

    Directory of Open Access Journals (Sweden)

    J. A. Breña Naranjo

    2011-11-01

    Full Text Available The hydrology of ecosystem succession gives rise to new challenges for the analysis and modelling of water balance components. Recent large-scale alterations of forest cover across the globe suggest that a significant portion of new biophysical environments will influence the long-term dynamics and limits of water fluxes compared to pre-succession conditions. This study assesses the estimation of summer evapotranspiration along three FLUXNET sites at Campbell River, British Columbia, Canada using a data-driven soil water balance model validated by Eddy Covariance measurements. It explores the sensitivity of the model to different forest succession states, a wide range of computational time steps, rooting depths, and canopy interception capacity values. Uncertainty in the measured EC fluxes resulting in an energy imbalance was consistent with previous studies and does not affect the validation of the model. The agreement between observations and model estimates proves that the usefulness of the method to predict summer AET over mid- and long-term periods is independent of stand age. However, an optimal combination of the parameters rooting depth, time step and interception capacity threshold is needed to avoid an underestimation of AET as seen in past studies. The study suggests that summer AET could be estimated and monitored in many more places than those equipped with Eddy Covariance or sap-flow measurements to advance the understanding of water balance changes in different successional ecosystems.

  19. An optimal baseline selection methodology for data-driven damage detection and temperature compensation in acousto-ultrasonics

    Science.gov (United States)

    Torres-Arredondo, M.-A.; Sierra-Pérez, Julián; Cabanes, Guénaël

    2016-05-01

    The process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). For the design of a trustworthy health monitoring system, a vast amount of information regarding the inherent physical characteristics of the sources and their propagation and interaction across the structure is crucial. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organising maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and identifiable.

  20. On the data-driven inference of modulatory networks in climate science: an application to West African rainfall

    Science.gov (United States)

    González, D. L., II; Angus, M. P.; Tetteh, I. K.; Bello, G. A.; Padmanabhan, K.; Pendse, S. V.; Srinivas, S.; Yu, J.; Semazzi, F.; Kumar, V.; Samatova, N. F.

    2015-01-01

    Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall~variability. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood. We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships influencing a climate phenomenon of interest. We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that modulate Sahel rainfall. These relationships fall into two categories: well-known associations from prior climate knowledge, such as the relationship with the El Niño-Southern Oscillation (ENSO) and putative links, such as North Atlantic Oscillation, that invite further research.

  1. Homologous Helical Jets: Observations by IRIS, SDO and Hinode and Magnetic Modeling with Data-Driven Simulations

    CERN Document Server

    Cheung, Mark C M; Tarbell, T D; Fu, Y; Tian, H; Testa, P; Reeves, K K; Martinez-Sykora, J; Boerner, P; Wuelser, J P; Lemen, J; Title, A M; Hurlburt, N; Kleint, L; Kankelborg, C; Jaeggli, S; Golub, L; McKillop, S; Saar, S; Carlsson, M; Hansteen, V

    2015-01-01

    We report on observations of recurrent jets by instruments onboard the Interface Region Imaging Spectrograph (IRIS), Solar Dynamics Observatory (SDO) and Hinode spacecrafts. Over a 4-hour period on July 21st 2013, recurrent coronal jets were observed to emanate from NOAA Active Region 11793. FUV spectra probing plasma at transition region temperatures show evidence of oppositely directed flows with components reaching Doppler velocities of +/- 100 km/s. Raster Doppler maps using a Si IV transition region line show all four jets to have helical motion of the same sense. Simultaneous observations of the region by SDO and Hinode show that the jets emanate from a source region comprising a pore embedded in the interior of a supergranule. The parasitic pore has opposite polarity flux compared to the surrounding network field. This leads to a spine-fan magnetic topology in the coronal field that is amenable to jet formation. Time-dependent data-driven simulations are used to investigate the underlying drivers for t...

  2. Retrospective Analysis of Communication Events - Understanding the Dynamics of Collaborative Multi-Party Discourse

    Energy Technology Data Exchange (ETDEWEB)

    Cowell, Andrew J.; Haack, Jereme N.; McColgin, Dave W.

    2006-06-08

    This research is aimed at understanding the dynamics of collaborative multi-party discourse across multiple communication modalities. Before we can truly make sig-nificant strides in devising collaborative communication systems, there is a need to understand how typical users utilize com-putationally supported communications mechanisms such as email, instant mes-saging, video conferencing, chat rooms, etc., both singularly and in conjunction with traditional means of communication such as face-to-face meetings, telephone calls and postal mail. Attempting to un-derstand an individual’s communications profile with access to only a single modal-ity is challenging at best and often futile. Here, we discuss the development of RACE – Retrospective Analysis of Com-munications Events – a test-bed prototype to investigate issues relating to multi-modal multi-party discourse.

  3. Estimating time-correlation functions by sampling and unbiasing dynamically activated events

    CERN Document Server

    Athènes, Manuel; Jourdan, Thomas; 10.1063/1.4766458

    2012-01-01

    Transition path sampling is a rare-event method that estimates state-to-state timecorrelation functions in many-body systems from samples of short trajectories. In this framework, it is proposed to bias the importance function using the lowest Jacobian eigenvalue moduli along the dynamical trajectory. A lowest eigenvalue modulus is related to the lowest eigenvalue of the Hessian matrix and is evaluated here using the Lanczos algorithm as in activation-relaxation techniques. This results in favoring the sampling of activated trajectories and enhancing the occurrence of the rare reactive trajectories of interest, those corresponding to transitions between locally stable states. Estimating the time-correlation functions involves unbiasing the sample of simulated trajectories which is done using the multi-state Bennett acceptance ratio (MBAR) method. To assess the performance of our procedure, we compute the time-correlation function associated with the migration of a vacancy in {\\alpha}-iron. The derivative of t...

  4. Modulations of 'late' event-related brain potentials in humans by dynamic audiovisual speech stimuli.

    Science.gov (United States)

    Lebib, Riadh; Papo, David; Douiri, Abdel; de Bode, Stella; Gillon Dowens, Margaret; Baudonnière, Pierre-Marie

    2004-11-30

    Lipreading reliably improve speech perception during face-to-face conversation. Within the range of good dubbing, however, adults tolerate some audiovisual (AV) discrepancies and lipreading, then, can give rise to confusion. We used event-related brain potentials (ERPs) to study the perceptual strategies governing the intermodal processing of dynamic and bimodal speech stimuli, either congruently dubbed or not. Electrophysiological analyses revealed that non-coherent audiovisual dubbings modulated in amplitude an endogenous ERP component, the N300, we compared to a 'N400-like effect' reflecting the difficulty to integrate these conflicting pieces of information. This result adds further support for the existence of a cerebral system underlying 'integrative processes' lato sensu. Further studies should take advantage of this 'N400-like effect' with AV speech stimuli to open new perspectives in the domain of psycholinguistics.

  5. Spatial and Temporal Dynamics of a Mortality Event among Central African Great Apes.

    Directory of Open Access Journals (Sweden)

    Kenneth N Cameron

    Full Text Available In 2006-2007 we observed an unusual mortality event among apes in northern Republic of Congo that, although not diagnostically confirmed, we believe to have been a disease outbreak. In 2007-2011 we conducted ape nest surveys in the region, recording 11,835 G. g. gorilla nests (2,262 groups and 5,548 P. t. troglodytes nests (2,139 groups. We developed a statistical model to determine likely points of origin of the outbreak to help identify variables associated with disease emergence and spread. We modeled disease spread across the study area, using suitable habitat conditions for apes as proxy for local ape densities. Infectious status outputs from that spread model were then used alongside vegetation, temperature, precipitation and human impact factors as explanatory variables in a Generalized Linear Model framework to explain observed 2007-2011 ape nest trends in the region. The best models predicted emergence in the western region of Odzala-Kokoua National Park and north of the last confirmed Ebola virus disease epizootics. Roads were consistently associated with attenuation of modeled virus spread. As disease is amongst the leading threats to great apes, gaining a better understanding of disease transmission dynamics in these species is imperative. Identifying ecological drivers underpinning a disease emergence event and transmission dynamics in apes is critical to creating better predictive models to guide wildlife management, develop potential protective measures for wildlife and to reduce potential zoonotic transmission to humans. The results of our model represent an important step in understanding variables related to great ape disease ecology in Central Africa.

  6. A Data-driven Analytic Model for Proton Acceleration by Large-scale Solar Coronal Shocks

    Science.gov (United States)

    Kozarev, Kamen A.; Schwadron, Nathan A.

    2016-11-01

    We have recently studied the development of an eruptive filament-driven, large-scale off-limb coronal bright front (OCBF) in the low solar corona, using remote observations from the Solar Dynamics Observatory’s Advanced Imaging Assembly EUV telescopes. In that study, we obtained high-temporal resolution estimates of the OCBF parameters regulating the efficiency of charged particle acceleration within the theoretical framework of diffusive shock acceleration (DSA). These parameters include the time-dependent front size, speed, and strength, as well as the upstream coronal magnetic field orientations with respect to the front’s surface normal direction. Here we present an analytical particle acceleration model, specifically developed to incorporate the coronal shock/compressive front properties described above, derived from remote observations. We verify the model’s performance through a grid of idealized case runs using input parameters typical for large-scale coronal shocks, and demonstrate that the results approach the expected DSA steady-state behavior. We then apply the model to the event of 2011 May 11 using the OCBF time-dependent parameters derived by Kozarev et al. We find that the compressive front likely produced energetic particles as low as 1.3 solar radii in the corona. Comparing the modeled and observed fluences near Earth, we also find that the bulk of the acceleration during this event must have occurred above 1.5 solar radii. With this study we have taken a first step in using direct observations of shocks and compressions in the innermost corona to predict the onsets and intensities of solar energetic particle events.

  7. Impact of Unexpected Events, Shocking News and Rumours on Foreign Exchange Market Dynamics

    CERN Document Server

    McDonald, M; Williams, S; Howison, S; Johnson, N F; Donald, Mark Mc; Suleman, Omer; Williams, Stacy; Howison, Sam; Johnson, Neil F.

    2006-01-01

    We analyze the dynamical response of the world's financial community to various types of unexpected events, including the 9/11 terrorist attacks as they unfolded on a minute-by-minute basis. We find that there are various 'species' of news, characterized by how quickly the news get absorbed, how much meaning and importance is assigned to it by the community, and what subsequent actions are then taken. For example, the response to the unfolding events of 9/11 shows a gradual collective understanding of what was happening, rather than an immediate realization. For news items which are not simple economic statements, and hence whose implications are not immediately obvious, we uncover periods of collective discovery during which collective opinions seem to oscillate in a remarkably synchronized way. In the case of a rumour, our findings also provide a concrete example of contagion in inter-connected communities. Practical applications of this work include the possibility of producing selective newsfeeds for spec...

  8. Pollutants dynamics in a rice field and an upland field during storm events

    Science.gov (United States)

    Kim, Jin Soo; Park, Jong-Wha; Jang, Hoon; Kim, Young Hyeon

    2010-05-01

    We investigated the dynamics of pollutants such as total nitrogen (TN), total phosphorous (TP), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and suspended sediment (SS) in runoff from a rice field and an upland field near the upper stream of the Han river in South Korea for multiple storm events. The upland field was cropped with red pepper, sweet potato, beans, and sesame. Runoff from the rice field started later than that from the upland field due to the water storage function of rice field. Unlike the upland field, runoff from the rice field was greatly affected by farmers' water management practices. Overall, event mean concentrations (EMCs) of pollutants in runoff water from the upland field were higher than those from the rice field. Especially, EMCs of TP and SS in runoff water from the upland field were one order of magnitude higher than those from the rice field. This may be because ponding condition and flat geographical features of the rice field greatly reduces the transport of particulate phosphorous associated with soil erosion. The results suggest that the rice field contributes to control particulate pollutants into adjacent water bodies.

  9. Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

    Directory of Open Access Journals (Sweden)

    Andreas A. Ioannides

    2012-01-01

    Full Text Available How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

  10. Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

    Science.gov (United States)

    Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

    2016-02-01

    This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

  11. Recognizing dynamic facial expressions of emotion: Specificity and intensity effects in event-related brain potentials.

    Science.gov (United States)

    Recio, Guillermo; Schacht, Annekathrin; Sommer, Werner

    2014-02-01

    Emotional facial expressions usually arise dynamically from a neutral expression. Yet, most previous research focused on static images. The present study investigated basic aspects of processing dynamic facial expressions. In two experiments, we presented short videos of facial expressions of six basic emotions and non-emotional facial movements emerging at variable and fixed rise times, attaining different intensity levels. In event-related brain potentials (ERP), effects of emotion but also for non-emotional movements appeared as early posterior negativity (EPN) between 200 and 350ms, suggesting an overall facilitation of early visual encoding for all facial movements. These EPN effects were emotion-unspecific. In contrast, relative to happiness and neutral expressions, negative emotional expressions elicited larger late positive ERP components (LPCs), indicating a more elaborate processing. Both EPN and LPC amplitudes increased with expression intensity. Effects of emotion and intensity were additive, indicating that intensity (understood as the degree of motion) increases the impact of emotional expressions but not its quality. These processes can be driven by all basic emotions, and there is little emotion-specificity even when statistical power is considerable (N (Experiment 2)=102). Copyright © 2013 Elsevier B.V. All rights reserved.

  12. Eventful evolution of giant molecular clouds in dynamically evolving spiral arms

    Science.gov (United States)

    Baba, Junichi; Morokuma-Matsui, Kana; Saitoh, Takayuki R.

    2017-01-01

    The formation and evolution of giant molecular clouds (GMCs) in spiral galaxies have been investigated in the traditional framework of the combined quasi-stationary density wave and galactic shock model. In this study, we investigate the structure and evolution of GMCs in a dynamically evolving spiral arm using a three-dimensional N-body/hydrodynamic simulation of a barred spiral galaxy at parsec-scale resolution. This simulation incorporated self-gravity, molecular hydrogen formation, radiative cooling, heating due to interstellar far-ultraviolet radiation, and stellar feedback by both H II regions and Type II supernovae. In contrast to a simple expectation based on the traditional spiral model, the GMCs exhibited no systematic evolutionary sequence across the spiral arm. Our simulation showed that the GMCs behaved as highly dynamic objects with eventful lives involving collisional build-up, collision-induced star formation, and destruction via stellar feedback. The GMC lifetimes were predicted to be short, only a few tens of millions years. We also found that at least at the resolutions and with the feedback models used in this study, most of the GMCs without H II regions were collapsing, but half of the GMCs with H II regions were expanding owing to the H II-region feedback from stars within them. Our results support the dynamic and feedback-regulated GMC evolution scenario. Although the simulated GMCs were converging rather than virial equilibrium, they followed the observed scaling relationship well. We also analysed the effects of galactic tides and external pressure on GMC evolution and suggested that GMCs cannot be regarded as isolated systems since their evolution in disc galaxies is complicated because of these environmental effects.

  13. Eventful Evolution of Giant Molecular Clouds in Dynamically Evolving Spiral Arms

    Science.gov (United States)

    Baba, Junichi; Morokuma-Matsui, Kana; Saitoh, Takayuki R.

    2016-09-01

    The formation and evolution of giant molecular clouds (GMCs) in spiral galaxies have been investigated in the traditional framework of the combined quasi-stationary density wave and galactic shock model. In this study, we investigate the structure and evolution of GMCs in a dynamically evolving spiral arm using a three-dimensional N-body/hydrodynamic simulation of a barred spiral galaxy at parsec-scale resolution. This simulation incorporated self-gravity, molecular hydrogen formation, radiative cooling, heating due to interstellar far-ultraviolet radiation, and stellar feedback by both HII regions and Type-II supernovae. In contrast to a simple expectation based on the traditional spiral model, the GMCs exhibited no systematic evolutionary sequence across the spiral arm. Our simulation showed that the GMCs behaved as highly dynamic objects with eventful lives involving collisional build-up, collision-induced star formation, and destruction via stellar feedback. The GMC lifetimes were predicted to be short, only a few tens of millions years. We also found that, at least at the resolutions and with the feedback models used in this study, most of the GMCs without HII regions were collapsing, but half of the GMCs with HII regions were expanding owing to the HII-region feedback from stars within them. Our results support the dynamic and feedback-regulated GMC evolution scenario. Although the simulated GMCs were converging rather than virial equilibrium, they followed the observed scaling relationship well. We also analysed the effects of galactic tides and external pressure on GMC evolution and suggested that GMCs cannot be regarded as isolated systems since their evolution in disc galaxies is complicated because of these environmental effects.

  14. A data-driven model for spectra: Finding double redshifts in the Sloan Digital Sky Survey

    CERN Document Server

    Tsalmantza, P

    2012-01-01

    We present a data-driven method - heteroscedastic matrix factorization, a kind of probabilistic factor analysis - for modeling or performing dimensionality reduction on observed spectra or other high-dimensional data with known but non-uniform observational uncertainties. The method uses an iterative inverse-variance-weighted least-squares minimization procedure to generate a best set of basis functions. The method is similar to principal components analysis, but with the substantial advantage that it uses measurement uncertainties in a responsible way and accounts naturally for poorly measured and missing data; it models the variance in the noise-deconvolved data space. A regularization can be applied, in the form of a smoothness prior (inspired by Gaussian processes) or a non-negative constraint, without making the method prohibitively slow. Because the method optimizes a justified scalar (related to the likelihood), the basis provides a better fit to the data in a probabilistic sense than any PCA basis. We...

  15. Quarkonium production at the LHC: A data-driven analysis of remarkably simple experimental patterns

    Science.gov (United States)

    Faccioli, Pietro; Lourenço, Carlos; Araújo, Mariana; Knünz, Valentin; Krätschmer, Ilse; Seixas, João

    2017-10-01

    The LHC quarkonium production data reveal a startling observation: the J / ψ, ψ (2S), χc1, χc2 and ϒ (nS)pT-differential cross sections in the central rapidity region are compatible with one universal momentum scaling pattern. Considering also the absence of strong polarizations of directly and indirectly produced S-wave mesons, we conclude that there is currently no evidence of a dependence of the partonic production mechanisms on the quantum numbers and mass of the final state. The experimental observations supporting this universal production scenario are remarkably significant, as shown by a new analysis approach, unbiased by specific theoretical calculations of partonic cross sections, which are only considered a posteriori, in comparisons with the data-driven results.

  16. Combining engineering and data-driven approaches: Development of a generic fire risk model facilitating calibration

    DEFF Research Database (Denmark)

    De Sanctis, G.; Fischer, K.; Kohler, J.

    2014-01-01

    Fire risk models support decision making for engineering problems under the consistent consideration of the associated uncertainties. Empirical approaches can be used for cost-benefit studies when enough data about the decision problem are available. But often the empirical approaches...... a generic risk model that is calibrated to observed fire loss data. Generic risk models assess the risk of buildings based on specific risk indicators and support risk assessment at a portfolio level. After an introduction to the principles of generic risk assessment, the focus of the present paper...... are not detailed enough. Engineering risk models, on the other hand, may be detailed but typically involve assumptions that may result in a biased risk assessment and make a cost-benefit study problematic. In two related papers it is shown how engineering and data-driven modeling can be combined by developing...

  17. Adaptive data-driven parallelization of multi-view video coding on multi-core processor

    Institute of Scientific and Technical Information of China (English)

    PANG Yi; HU WeiDong; SUN LiFeng; YANG ShiQiang

    2009-01-01

    Multi-view video coding (MVC) comprises rich 3D information and is widely used in new visual media, such as 3DTV and free viewpoint TV (FTV). However, even with mainstream computer manufacturers migrating to multi-core processors, the huge computational requirement of MVC currently prohibits its wide use in consumer markets. In this paper, we demonstrate the design and implementation of the first parallel MVC system on Cell Broadband EngineTM processor which is a state-of-the-art multi-core processor. We propose a task-dispatching algorithm which is adaptive data-driven on the frame level for MVC, and implement a parallel multi-view video decoder with modified H.264/AVC codec on real machine. This approach provides scalable speedup (up to 16 times on sixteen cores) through proper local store management, utilization of code locality and SIMD improvement. Decoding speed, speedup and utilization rate of cores are expressed in experimental results.

  18. Automatic translation of MPI source into a latency-tolerant, data-driven form

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Tan; Cicotti, Pietro; Bylaska, Eric; Quinlan, Dan; Baden, Scott

    2017-08-01

    Hiding communication behind useful computation is an important performance programming technique but remains an inscrutable programming exercise even for the expert. We present Bamboo, a code transformation framework that can realize communication overlap in applications written in MPI without the need to intrusively modify the source code. Bamboo reformulates MPI source into the form of a task dependency graph that expresses a partial ordering among tasks, enabling the program to execute in a data-driven fashion under the control of an external runtime system. Experimental results demonstrate that Bamboo significantly reduces communication delays while requiring only modest amounts of programmer annotation for a variety of applications and platforms, including those employing co-processors and accelerators. Moreover, Bamboo's performance meets or exceeds that of labor-intensive hand coding. The translator is more than a means of hiding communication costs automatically; it demonstrates the utility of semantic level optimization against a wellknown library.

  19. Calibrating the pixel-level Kepler imaging data with a causal data-driven model

    CERN Document Server

    Wang, Dun; Hogg, David W; Schölkopf, Bernhard

    2015-01-01

    Astronomical observations are affected by several kinds of noise, each with its own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. The precision of NASA Kepler photometry for exoplanet science---the most precise photometric measurements of stars ever made---appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level so that it can capture very fine-grained information about the variation of the spacecraft. The CPM predicts each target pixel value from a large number of pixels of other stars sharing the instrument variabilities while not containing any information on possible transits in the target star. In addition, we use the target star's future and past (auto-regr...

  20. USACM Thematic Workshop On Uncertainty Quantification And Data-Driven Modeling.

    Energy Technology Data Exchange (ETDEWEB)

    Stewart, James R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    The USACM Thematic Workshop on Uncertainty Quantification and Data-Driven Modeling was held on March 23-24, 2017, in Austin, TX. The organizers of the technical program were James R. Stewart of Sandia National Laboratories and Krishna Garikipati of University of Michigan. The administrative organizer was Ruth Hengst, who serves as Program Coordinator for the USACM. The organization of this workshop was coordinated through the USACM Technical Thrust Area on Uncertainty Quantification and Probabilistic Analysis. The workshop website (http://uqpm2017.usacm.org) includes the presentation agenda as well as links to several of the presentation slides (permission to access the presentations was granted by each of those speakers, respectively). Herein, this final report contains the complete workshop program that includes the presentation agenda, the presentation abstracts, and the list of posters.

  1. Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images

    DEFF Research Database (Denmark)

    Wolffhechel, Karin Marie Brandt; Hahn, Amanda C.; Jarmer, Hanne Østergaard

    2015-01-01

    Several lines of evidence suggest that facial cues of adiposity may be important for human social interaction. However, tests for quantifiable cues of body mass index (BMI) in the face have examined only a small number of facial proportions and these proportions were found to have relatively low...... predictive power. Here we employed a data-driven approach in which statistical models were built using principal components (PCs) derived from objectively defined shape and color characteristics in face images. The predictive power of these models was then compared with models based on previously studied...... facial proportions (perimeter-to-area ratio, width-to-height ratio, and cheek-to-jaw width). Models based on 2D shape-only PCs, color-only PCs, and 2D shape and color PCs combined each performed significantly and substantially better than models based on one or more of the previously studied facial...

  2. A new meta-data driven data-sharing storage model for SaaS

    Directory of Open Access Journals (Sweden)

    Li Heng

    2012-11-01

    Full Text Available A multi-tenant database is the primary characteristic of SaaS, it allows SaaS vendors to run a single instance application which supports multiple tenants on the same hardware and software infrastructure. This application should be highly customizable to meet tenants expectations and business requirements. This paper examined current solutions on multi-tenancy, and proposed a new meta-data driven data-sharing storage model for multi-tenant applications. Our design enables tenants to extend their own database schema during multi-tenant application run-time execution to satisfy their business needs. Experimental results show that our model made a good balance between efficiency and customized.

  3. Automatic sleep classification using a data-driven topic model reveals latent sleep states

    DEFF Research Database (Denmark)

    Koch, Henriette; Christensen, Julie Anja Engelhard; Frandsen, Rune

    2014-01-01

    Background: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. New method: To meet the criticism and reveal the latent...... sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1 s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model...... Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group...

  4. DOE High Performance Computing Operational Review (HPCOR): Enabling Data-Driven Scientific Discovery at HPC Facilities

    Energy Technology Data Exchange (ETDEWEB)

    Gerber, Richard; Allcock, William; Beggio, Chris; Campbell, Stuart; Cherry, Andrew; Cholia, Shreyas; Dart, Eli; England, Clay; Fahey, Tim; Foertter, Fernanda; Goldstone, Robin; Hick, Jason; Karelitz, David; Kelly, Kaki; Monroe, Laura; Prabhat,; Skinner, David; White, Julia

    2014-10-17

    U.S. Department of Energy (DOE) High Performance Computing (HPC) facilities are on the verge of a paradigm shift in the way they deliver systems and services to science and engineering teams. Research projects are producing a wide variety of data at unprecedented scale and level of complexity, with community-specific services that are part of the data collection and analysis workflow. On June 18-19, 2014 representatives from six DOE HPC centers met in Oakland, CA at the DOE High Performance Operational Review (HPCOR) to discuss how they can best provide facilities and services to enable large-scale data-driven scientific discovery at the DOE national laboratories. The report contains findings from that review.

  5. Domain-Oriented Data-Driven Data Mining Based on Rough Sets

    Institute of Scientific and Technical Information of China (English)

    Guoyin Wang

    2006-01-01

    understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.

  6. Developing credible AI - linguistic behaviour, simulations, data-driven input, and Turing's legacy

    CERN Document Server

    Paradowski, Michal B

    2011-01-01

    Barring swarm robotics, a substantial share of current machine-human and machine-machine learning and interaction mechanisms are being developed and fed by results of agent-based computer simulations, game-theoretic models, or robotic experiments based on a dyadic interaction pattern. Yet, in real life, humans no less frequently communicate in groups, and take decisions basing on information cumulatively gleaned from more than one single source. These properties should be taken into consideration in the design of autonomous artificial cognitive systems construed to interact with learn from more than one contact or 'neighbour'. To this end, significant practical import can be gleaned from research applying strict science methodology to phenomena humanistic and social, e.g. to discovery of realistic creativity potential spans, or the 'exposure thresholds' after which new information could be accepted by a cognitive system. Such rigorous data-driven research offers the chance of not only approximating to descrip...

  7. Data-driven Model-independent Searches for Long-lived Particles at the LHC

    CERN Document Server

    Coccaro, Andrea; Lubatti, H J; Russell, Heather; Shelton, Jessie

    2016-01-01

    Neutral long-lived particles (LLPs) are highly motivated by many BSM scenarios, such as theories of supersymmetry, baryogenesis, and neutral naturalness, and present both tremendous discovery opportunities and experimental challenges for the LHC. A major bottleneck for current LLP searches is the prediction of SM backgrounds, which are often impossible to simulate accurately. In this paper, we propose a general strategy for obtaining differential, data-driven background estimates in LLP searches, thereby notably extending the range of LLP masses and lifetimes that can be discovered at the LHC. We focus on LLPs decaying in the ATLAS Muon System, where triggers providing both signal and control samples are available at the LHC Run-2. While many existing searches require two displaced decays, a detailed knowledge of backgrounds will allow for very inclusive searches that require just one detected LLP decay. As we demonstrate for the $h \\to X X$ signal model of LLP pair production in exotic Higgs decays, this res...

  8. Topological obstructions in the way of data-driven collective variables.

    Science.gov (United States)

    Hashemian, Behrooz; Arroyo, Marino

    2015-01-28

    Nonlinear dimensionality reduction (NLDR) techniques are increasingly used to visualize molecular trajectories and to create data-driven collective variables for enhanced sampling simulations. The success of these methods relies on their ability to identify the essential degrees of freedom characterizing conformational changes. Here, we show that NLDR methods face serious obstacles when the underlying collective variables present periodicities, e.g., arising from proper dihedral angles. As a result, NLDR methods collapse very distant configurations, thus leading to misinterpretations and inefficiencies in enhanced sampling. Here, we identify this largely overlooked problem and discuss possible approaches to overcome it. We also characterize the geometry and topology of conformational changes of alanine dipeptide, a benchmark system for testing new methods to identify collective variables.

  9. A new data-driven controllability measure with application in intelligent buildings

    DEFF Research Database (Denmark)

    Shaker, Hamid Reza; Lazarova-Molnar, Sanja

    2017-01-01

    and sensors, and information obtained by data processing. This differs from the traditional model-based approaches that are based on mathematical models of systems. We propose and describe a data-driven controllability measure for discrete-time linear systems. The concept is developed within a data-based......Buildings account for ca. 40% of the total energy consumption and ca. 20% of the total CO2 emissions. More effective and advanced control integrated into Building Management Systems (BMS) represents an opportunity to improve energy efficiency. The ease of availability of sensors technology...... and instrumentation within today's intelligent buildings enable collecting high quality data which could be used directly in data-based analysis and control methods. The area of data-based systems analysis and control is concentrating on developing analysis and control methods that rely on data collected from meters...

  10. Physical Strength as a Cue to Dominance: A Data-Driven Approach.

    Science.gov (United States)

    Toscano, Hugo; Schubert, Thomas W; Dotsch, Ron; Falvello, Virginia; Todorov, Alexander

    2016-12-01

    We investigate both similarities and differences between dominance and strength judgments using a data-driven approach. First, we created statistical face shape models of judgments of both dominance and physical strength. The resulting faces representing dominance and strength were highly similar, and participants were at chance in discriminating faces generated by the two models. Second, although the models are highly correlated, it is possible to create a model that captures their differences. This model generates faces that vary from dominant-yet-physically weak to nondominant-yet-physically strong. Participants were able to identify the difference in strength between the physically strong-yet-nondominant faces and the physically weak-yet-dominant faces. However, this was not the case for identifying dominance. These results suggest that representations of social dominance and physical strength are highly similar, and that strength is used as a cue for dominance more than dominance is used as a cue for strength.

  11. Image Resolution Enhancement via Data-Driven Parametric Models in the Wavelet Space

    Directory of Open Access Journals (Sweden)

    Xin Li

    2007-02-01

    Full Text Available We present a data-driven, project-based algorithm which enhances image resolution by extrapolating high-band wavelet coefficients. High-resolution images are reconstructed by alternating the projections onto two constraint sets: the observation constraint defined by the given low-resolution image and the prior constraint derived from the training data at the high resolution (HR. Two types of prior constraints are considered: spatially homogeneous constraint suitable for texture images and patch-based inhomogeneous one for generic images. A probabilistic fusion strategy is developed for combining reconstructed HR patches when overlapping (redundancy is present. It is argued that objective fidelity measure is important to evaluate the performance of resolution enhancement techniques and the role of antialiasing filter should be properly addressed. Experimental results are reported to show that our projection-based approach can achieve both good subjective and objective performance especially for the class of texture images.

  12. Image Resolution Enhancement via Data-Driven Parametric Models in the Wavelet Space

    Directory of Open Access Journals (Sweden)

    Li Xin

    2007-01-01

    Full Text Available We present a data-driven, project-based algorithm which enhances image resolution by extrapolating high-band wavelet coefficients. High-resolution images are reconstructed by alternating the projections onto two constraint sets: the observation constraint defined by the given low-resolution image and the prior constraint derived from the training data at the high resolution (HR. Two types of prior constraints are considered: spatially homogeneous constraint suitable for texture images and patch-based inhomogeneous one for generic images. A probabilistic fusion strategy is developed for combining reconstructed HR patches when overlapping (redundancy is present. It is argued that objective fidelity measure is important to evaluate the performance of resolution enhancement techniques and the role of antialiasing filter should be properly addressed. Experimental results are reported to show that our projection-based approach can achieve both good subjective and objective performance especially for the class of texture images.

  13. Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics

    Directory of Open Access Journals (Sweden)

    Richard Mark Leggett

    2013-12-01

    Full Text Available The processes of quality assessment and control are an active area of research at The Genome Analysis Centre (TGAC. Unlike other sequencing centres that often concentrate on a certain species or technology, TGAC applies expertise in genomics and bioinformatics to a wide range of projects, often requiring bespoke wet lab and in silico workflows. TGAC is fortunate to have access to a diverse range of sequencing and analysis platforms, and we are at the forefront of investigations into library quality and sequence data assessment. We have developed and implemented a number of algorithms, tools, pipelines and packages to ascertain, store, and expose quality metrics across a number of next-generation sequencing platforms, allowing rapid and in-depth cross-platform QC bioinformatics. In this review, we describe these tools as a vehicle for data-driven informatics, offering the potential to provide richer context for downstream analysis and to inform experimental design.

  14. Secondary Use of Clinical Data to Enable Data-Driven Translational Science with Trustworthy Access Management.

    Science.gov (United States)

    Mosa, Abu Saleh Mohammad; Yoo, Illhoi; Apathy, Nate C; Ko, Kelly J; Parker, Jerry C

    2015-01-01

    University of Missouri (MU) Health Care produces a large amount of digitized clinical data that can be used in clinical and translational research for cohort identification, retrospective data analysis, feasibility study, and hypothesis generation. In this article, the implementation of an integrated clinical research data repository is discussed. We developed trustworthy access-management protocol for providing access to both clinically relevant data and protected health information. As of September 2014, the database contains approximately 400,000 patients and 82 million observations; and is growing daily. The system will facilitate the secondary use of electronic health record (EHR) data at MU to promote data-driven clinical and translational research, in turn enabling better healthcare through research.

  15. Data-driven optimization and knowledge discovery for an enterprise information system

    CERN Document Server

    Duan, Qing; Zeng, Jun

    2015-01-01

    This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-...

  16. Estimation of DSGE Models under Diffuse Priors and Data-Driven Identification Constraints

    DEFF Research Database (Denmark)

    Lanne, Markku; Luoto, Jani

    the properties of the estimation method, and shows how the problem of multimodal posterior distributions caused by parameter redundancy is eliminated by identification constraints. Out-of-sample forecast comparisons as well as Bayes factors lend support to the constrained model.......We propose a sequential Monte Carlo (SMC) method augmented with an importance sampling step for estimation of DSGE models. In addition to being theoretically well motivated, the new method facilitates the assessment of estimation accuracy. Furthermore, in order to alleviate the problem...... of multimodal posterior distributions due to poor identification of DSGE models when uninformative prior distributions are assumed, we recommend imposing data-driven identification constraints and devise a procedure for finding them. An empirical application to the Smets-Wouters (2007) model demonstrates...

  17. First-principles data-driven discovery of transition metal oxides for artificial photosynthesis

    Science.gov (United States)

    Yan, Qimin

    We develop a first-principles data-driven approach for rapid identification of transition metal oxide (TMO) light absorbers and photocatalysts for artificial photosynthesis using the Materials Project. Initially focusing on Cr, V, and Mn-based ternary TMOs in the database, we design a broadly-applicable multiple-layer screening workflow automating density functional theory (DFT) and hybrid functional calculations of bulk and surface electronic and magnetic structures. We further assess the electrochemical stability of TMOs in aqueous environments from computed Pourbaix diagrams. Several promising earth-abundant low band-gap TMO compounds with desirable band edge energies and electrochemical stability are identified by our computational efforts and then synergistically evaluated using high-throughput synthesis and photoelectrochemical screening techniques by our experimental collaborators at Caltech. Our joint theory-experiment effort has successfully identified new earth-abundant copper and manganese vanadate complex oxides that meet highly demanding requirements for photoanodes, substantially expanding the known space of such materials. By integrating theory and experiment, we validate our approach and develop important new insights into structure-property relationships for TMOs for oxygen evolution photocatalysts, paving the way for use of first-principles data-driven techniques in future applications. This work is supported by the Materials Project Predictive Modeling Center and the Joint Center for Artificial Photosynthesis through the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-05CH11231. Computational resources also provided by the Department of Energy through the National Energy Supercomputing Center.

  18. Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial.

    Science.gov (United States)

    Kulin, Merima; Fortuna, Carolina; De Poorter, Eli; Deschrijver, Dirk; Moerman, Ingrid

    2016-06-01

    Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

  19. A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

    Science.gov (United States)

    Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.

    2016-12-01

    Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

  20. Extended temperature-accelerated dynamics: enabling long-time full-scale modeling of large rare-event systems.

    Science.gov (United States)

    Bochenkov, Vladimir; Suetin, Nikolay; Shankar, Sadasivan

    2014-09-07

    A new method, the Extended Temperature-Accelerated Dynamics (XTAD), is introduced for modeling long-timescale evolution of large rare-event systems. The method is based on the Temperature-Accelerated Dynamics approach [M. Sørensen and A. Voter, J. Chem. Phys. 112, 9599 (2000)], but uses full-scale parallel molecular dynamics simulations to probe a potential energy surface of an entire system, combined with the adaptive on-the-fly system decomposition for analyzing the energetics of rare events. The method removes limitations on a feasible system size and enables to handle simultaneous diffusion events, including both large-scale concerted and local transitions. Due to the intrinsically parallel algorithm, XTAD not only allows studies of various diffusion mechanisms in solid state physics, but also opens the avenue for atomistic simulations of a range of technologically relevant processes in material science, such as thin film growth on nano- and microstructured surfaces.

  1. Complex Dynamic Scene Perception: Effects of Attentional Set on Perceiving Single and Multiple Event Types

    Science.gov (United States)

    Sanocki, Thomas; Sulman, Noah

    2013-01-01

    Three experiments measured the efficiency of monitoring complex scenes composed of changing objects, or events. All events lasted about 4 s, but in a given block of trials, could be of a single type (single task) or of multiple types (multitask, with a total of four event types). Overall accuracy of detecting target events amid distractors was…

  2. Tree-ring responses to extreme climate events as benchmarks for terrestrial dynamic vegetation models

    Directory of Open Access Journals (Sweden)

    A. Rammig

    2014-02-01

    Full Text Available Climate extremes can trigger exceptional responses in terrestrial ecosystems, for instance by altering growth or mortality rates. Effects of this kind are often manifested in reductions of the local net primary production (NPP. Investigating a set of European long-term data on annual radial tree growth confirms this pattern: we find that 53% of tree ring width (TRW indices are below one standard deviation, and up to 16% of the TRW values are below two standard deviations in years with extremely high temperatures and low precipitation. Based on these findings we investigate if climate driven patterns in long-term tree growth data may serve as benchmarks for state-of-the-art dynamic vegetation models such as LPJmL. The model simulates NPP but not explicitly the radial tree ring growth, hence requiring a generic method to ensure an objective comparison. Here we propose an analysis scheme that quantifies the coincidence rate of climate extremes with some biotic responses (here TRW or simulated NPP. We find that the reduction in tree-ring width during drought extremes is lower than the corresponding reduction of simulated NPP. We identify ten extreme years during the 20th century in which both, model and measurements indicate high coincidence rates across Europe. However, we detect substantial regional differences in simulated and observed responses to extreme events. One explanation for this discrepancy could be that the tree-ring data have preferentially been sampled at more climatically stressed sites. The model-data difference is amplified by the fact that dynamic vegetation models are designed to simulate mean ecosystem responses at landscape or regional scale. However, we find that both model-data and measurements display carry-over effects from the previous year. We conclude that using radial tree growth is a good basis for generic model-benchmarks if the data are analyzed by scale-free measures such as coincidence analysis. Our study shows

  3. Various sizes of sliding event bursts in the plastic flow of metallic glasses based on a spatiotemporal dynamic model

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Jingli, E-mail: renjl@zzu.edu.cn, E-mail: g.wang@shu.edu.cn; Chen, Cun [School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001 (China); Wang, Gang, E-mail: renjl@zzu.edu.cn, E-mail: g.wang@shu.edu.cn [Laboratory for Microstructures, Shanghai University, Shanghai 200444 (China); Cheung, Wing-Sum [Department of Mathematics, The University of HongKong, HongKong (China); Sun, Baoan; Mattern, Norbert [IFW-dresden, Institute for Complex Materials, P.O. Box 27 01 16, D-01171 Dresden (Germany); Siegmund, Stefan [Department of Mathematics, TU Dresden, D-01062 Dresden (Germany); Eckert, Jürgen [IFW-dresden, Institute for Complex Materials, P.O. Box 27 01 16, D-01171 Dresden (Germany); Institute of Materials Science, TU Dresden, D-01062 Dresden (Germany)

    2014-07-21

    This paper presents a spatiotemporal dynamic model based on the interaction between multiple shear bands in the plastic flow of metallic glasses during compressive deformation. Various sizes of sliding events burst in the plastic deformation as the generation of different scales of shear branches occurred; microscopic creep events and delocalized sliding events were analyzed based on the established model. This paper discusses the spatially uniform solutions and traveling wave solution. The phase space of the spatially uniform system applied in this study reflected the chaotic state of the system at a lower strain rate. Moreover, numerical simulation showed that the microscopic creep events were manifested at a lower strain rate, whereas the delocalized sliding events were manifested at a higher strain rate.

  4. Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM

    KAUST Repository

    Amer, Abdelhalim

    2013-01-01

    Extracting maximum performance of multi-core architectures is a difficult task primarily due to bandwidth limitations of the memory subsystem and its complex hierarchy. In this work, we study the implications of fork-join and data-driven execution models on this type of architecture at the level of task parallelism. For this purpose, we use a highly optimized fork-join based implementation of the FMM and extend it to a data-driven implementation using a distributed task scheduling approach. This study exposes some limitations of the conventional fork-join implementation in terms of synchronization overheads. We find that these are not negligible and their elimination by the data-driven method, with a careful data locality strategy, was beneficial. Experimental evaluation of both methods on state-of-the-art multi-socket multi-core architectures showed up to 22% speed-ups of the data-driven approach compared to the original method. We demonstrate that a data-driven execution of FMM not only improves performance by avoiding global synchronization overheads but also reduces the memory-bandwidth pressure caused by memory-intensive computations. © 2013 Springer-Verlag.

  5. Integration of data-driven and physically-based methods to assess shallow landslides susceptibility

    Science.gov (United States)

    Lajas, Sara; Oliveira, Sérgio C.; Zêzere, José Luis

    2016-04-01

    Approaches used to assess shallow landslides susceptibility at the basin scale are conceptually different depending on the use of statistic or deterministic methods. The data-driven methods are sustained in the assumption that the same causes are likely to produce the same effects and for that reason a present/past landslide inventory and a dataset of factors assumed as predisposing factors are crucial for the landslide susceptibility assessment. The physically-based methods are based on a system controlled by physical laws and soil mechanics, where the forces which tend to promote movement are compared with forces that tend to promote resistance to movement. In this case, the evaluation of susceptibility is supported by the calculation of the Factor of safety (FoS), and dependent of the availability of detailed data related with the slope geometry and hydrological and geotechnical properties of the soils and rocks. Within this framework, this work aims to test two hypothesis: (i) although conceptually distinct and based on contrasting procedures, statistic and deterministic methods generate similar shallow landslides susceptibility results regarding the predictive capacity and spatial agreement; and (ii) the integration of the shallow landslides susceptibility maps obtained with data-driven and physically-based methods, for the same study area, generate a more reliable susceptibility model for shallow landslides occurrence. To evaluate these two hypotheses, we select the Information Value data-driven method and the physically-based Infinite Slope model to evaluate shallow landslides in the study area of Monfalim and Louriceira basins (13.9 km2), which is located in the north of Lisbon region (Portugal). The landslide inventory is composed by 111 shallow landslides and was divide in two independent groups based on temporal criteria (age ≤ 1983 and age > 1983): (i) the modelling group (51 cases) was used to define the weights for each predisposing factor

  6. Parallel data-driven decomposition algorithm for large-scale datasets: with application to transitional boundary layers

    Science.gov (United States)

    Sayadi, Taraneh; Schmid, Peter J.

    2016-10-01

    Many fluid flows of engineering interest, though very complex in appearance, can be approximated by low-order models governed by a few modes, able to capture the dominant behavior (dynamics) of the system. This feature has fueled the development of various methodologies aimed at extracting dominant coherent structures from the flow. Some of the more general techniques are based on data-driven decompositions, most of which rely on performing a singular value decomposition (SVD) on a formulated snapshot (data) matrix. The amount of experimentally or numerically generated data expands as more detailed experimental measurements and increased computational resources become readily available. Consequently, the data matrix to be processed will consist of far more rows than columns, resulting in a so-called tall-and-skinny (TS) matrix. Ultimately, the SVD of such a TS data matrix can no longer be performed on a single processor, and parallel algorithms are necessary. The present study employs the parallel TSQR algorithm of (Demmel et al. in SIAM J Sci Comput 34(1):206-239, 2012), which is further used as a basis of the underlying parallel SVD. This algorithm is shown to scale well on machines with a large number of processors and, therefore, allows the decomposition of very large datasets. In addition, the simplicity of its implementation and the minimum required communication makes it suitable for integration in existing numerical solvers and data decomposition techniques. Examples that demonstrate the capabilities of highly parallel data decomposition algorithms include transitional processes in compressible boundary layers without and with induced flow separation.

  7. Dynamics of the spatial scale of visual attention revealed by brain event-related potentials

    Science.gov (United States)

    Luo, Y. J.; Greenwood, P. M.; Parasuraman, R.

    2001-01-01

    The temporal dynamics of the spatial scaling of attention during visual search were examined by recording event-related potentials (ERPs). A total of 16 young participants performed a search task in which the search array was preceded by valid cues that varied in size and hence in precision of target localization. The effects of cue size on short-latency (P1 and N1) ERP components, and the time course of these effects with variation in cue-target stimulus onset asynchrony (SOA), were examined. Reaction time (RT) to discriminate a target was prolonged as cue size increased. The amplitudes of the posterior P1 and N1 components of the ERP evoked by the search array were affected in opposite ways by the size of the precue: P1 amplitude increased whereas N1 amplitude decreased as cue size increased, particularly following the shortest SOA. The results show that when top-down information about the region to be searched is less precise (larger cues), RT is slowed and the neural generators of P1 become more active, reflecting the additional computations required in changing the spatial scale of attention to the appropriate element size to facilitate target discrimination. In contrast, the decrease in N1 amplitude with cue size may reflect a broadening of the spatial gradient of attention. The results provide electrophysiological evidence that changes in the spatial scale of attention modulate neural activity in early visual cortical areas and activate at least two temporally overlapping component processes during visual search.

  8. Cortical dynamics of semantic processing during sentence comprehension: evidence from event-related optical signals.

    Science.gov (United States)

    Huang, Jian; Wang, Suiping; Jia, Shiwei; Mo, Deyuan; Chen, Hsuan-Chih

    2013-01-01

    Using the event-related optical signal (EROS) technique, this study investigated the dynamics of semantic brain activation during sentence comprehension. Participants read sentences constituent-by-constituent and made a semantic judgment at the end of each sentence. The EROSs were recorded simultaneously with ERPs and time-locked to expected or unexpected sentence-final target words. The unexpected words evoked a larger N400 and a late positivity than the expected ones. Critically, the EROS results revealed activations first in the left posterior middle temporal gyrus (LpMTG) between 128 and 192 ms, then in the left anterior inferior frontal gyrus (LaIFG), the left middle frontal gyrus (LMFG), and the LpMTG in the N400 time window, and finally in the left posterior inferior frontal gyrus (LpIFG) between 832 and 864 ms. Also, expected words elicited greater activation than unexpected words in the left anterior temporal lobe (LATL) between 192 and 256 ms. These results suggest that the early lexical-semantic retrieval reflected by the LpMTG activation is followed by two different semantic integration processes: a relatively rapid and transient integration in the LATL and a relatively slow but enduring integration in the LaIFG/LMFG and the LpMTG. The late activation in the LpIFG, however, may reflect cognitive control.

  9. Assessing polyglutamine conformation in the nucleating event by molecular dynamics simulations.

    Science.gov (United States)

    Miettinen, Markus S; Knecht, Volker; Monticelli, Luca; Ignatova, Zoya

    2012-08-30

    Polyglutamine (polyQ) diseases comprise a group of dominantly inherited pathology caused by an expansion of an unstable polyQ stretch which is presumed to form β-sheets. Similar to other amyloid pathologies, polyQ amyloidogenesis occurs via a nucleated polymerization mechanism, and proceeds through energetically unfavorable nucleus whose existence and structure are difficult to detect. Here, we use atomistic molecular dynamics simulations in explicit solvent to assess the conformation of the polyQ stretch in the nucleus that initiates polyQ fibrillization. Comparison of the kinetic stability of various structures of polyQ peptide with a Q-length in the pathological range (Q40) revealed that steric zipper or nanotube-like structures (β-nanotube or β-pseudohelix) are not kinetically stable enough to serve as a template to initiate polyQ fibrillization as opposed to β-hairpin-based (β-sheet and β-sheetstack) or α-helical conformations. The selection of different structures of the polyQ stretch in the aggregation-initiating event may provide an alternative explanation for polyQ aggregate polymorphism.

  10. Probing nuclear dynamics in jet production with a global event shape

    CERN Document Server

    Kang, Zhong-Bo; Mantry, Sonny; Qiu, Jian-Wei

    2013-01-01

    We study single jet production in electron-nucleus collisions e^- + N_A -> J + X, using the 1-jettiness (\\tau_1) global event shape. It inclusively quantifies the pattern of radiation in the final state, gives enhanced sensitivity to soft radiation at wide angles from the nuclear beam and final-state jet, and facilitates the resummation of large Sudakov logarithms associated with the veto on additional jets. Through their effect on the observed pattern of radiation, 1-jettiness can be a useful probe of nuclear PDFs and power corrections from dynamical effects in the nuclear medium. This formalism allows for the standard jet shape analysis while simultaneously providing sensitivity to soft radiation at wide angles from the jet. We use a factorization framework for cross-sections differential in $\\tau_1$ and the transverse momentum (P_{J_T}) and rapidity (y) of the jet, in the region \\tau_1<< P_{J_T}. The restriction $\\tau_1 << P_{J_T}$ allows only soft radiation between the nuclear beam and jet dir...

  11. Cortical dynamics of semantic processing during sentence comprehension: evidence from event-related optical signals.

    Directory of Open Access Journals (Sweden)

    Jian Huang

    Full Text Available Using the event-related optical signal (EROS technique, this study investigated the dynamics of semantic brain activation during sentence comprehension. Participants read sentences constituent-by-constituent and made a semantic judgment at the end of each sentence. The EROSs were recorded simultaneously with ERPs and time-locked to expected or unexpected sentence-final target words. The unexpected words evoked a larger N400 and a late positivity than the expected ones. Critically, the EROS results revealed activations first in the left posterior middle temporal gyrus (LpMTG between 128 and 192 ms, then in the left anterior inferior frontal gyrus (LaIFG, the left middle frontal gyrus (LMFG, and the LpMTG in the N400 time window, and finally in the left posterior inferior frontal gyrus (LpIFG between 832 and 864 ms. Also, expected words elicited greater activation than unexpected words in the left anterior temporal lobe (LATL between 192 and 256 ms. These results suggest that the early lexical-semantic retrieval reflected by the LpMTG activation is followed by two different semantic integration processes: a relatively rapid and transient integration in the LATL and a relatively slow but enduring integration in the LaIFG/LMFG and the LpMTG. The late activation in the LpIFG, however, may reflect cognitive control.

  12. English- and Mandarin-learning infants' discrimination of actions and objects in dynamic events.

    Science.gov (United States)

    Chen, Jie; Tardif, Twila; Pulverman, Rachel; Casasola, Marianella; Zhu, Liqi; Zheng, Xiaobei; Meng, Xiangzhi

    2015-10-01

    The present studies examined the role of linguistic experience in directing English and Mandarin learners' attention to aspects of a visual scene. Specifically, they asked whether young language learners in these 2 cultures attend to differential aspects of a word-learning situation. Two groups of English and Mandarin learners, 6-8-month-olds (n = 65) and 17-19-month-olds (n = 91), participated in 2 studies, based on a habituation paradigm, designed to test infants' discrimination between actions and objects in dynamic events. In Study 1, these stimuli were presented in silence, whereas in Study 2, a verbal label accompanied videos. Results showed that 6-8-month-olds could discriminate action changes but not object changes, whereas 17-19-month-olds could discriminate both types of changes. However, there were only very subtle cross-linguistic differences in these patterns when the scenes were presented together with a verbal label. These findings show strong evidence for universal developmental trends in attention, with somewhat weaker evidence that the differences in the types of words Mandarin- versus English-learning children produce or are exposed to affect attention to different aspects of a scene in the first 2 years of life. (c) 2015 APA, all rights reserved).

  13. Data-Driven Extraction of a Nested Model of Human Brain Function.

    Science.gov (United States)

    Bolt, Taylor; Nomi, Jason S; Yeo, B T Thomas; Uddin, Lucina Q

    2017-07-26

    Decades of cognitive neuroscience research have revealed two basic facts regarding task-driven brain activation patterns. First, distinct patterns of activation occur in response to different task demands. Second, a superordinate, dichotomous pattern of activation/deactivation, is common across a variety of task demands. We explore the possibility that a hierarchical model incorporates these two observed brain activation phenomena into a unifying framework. We apply a latent variable approach, exploratory bifactor analysis, to a large set of human (both sexes) brain activation maps (n = 108) encompassing cognition, perception, action, and emotion behavioral domains, to determine the potential existence of a nested structure of factors that underlie a variety of commonly observed activation patterns. We find that a general factor, associated with a superordinate brain activation/deactivation pattern, explained the majority of the variance (52.37%) in brain activation patterns. The bifactor analysis also revealed several subfactors that explained an additional 31.02% of variance in brain activation patterns, associated with different manifestations of the superordinate brain activation/deactivation pattern, each emphasizing different contexts in which the task demands occurred. Importantly, this nested factor structure provided better overall fit to the data compared with a non-nested factor structure model. These results point to a domain-general psychological process, representing a "focused awareness" process or "attentional episode" that is variously manifested according to the sensory modality of the stimulus and degree of cognitive processing. This novel model provides the basis for constructing a biologically informed, data-driven taxonomy of psychological processes.SIGNIFICANCE STATEMENT A crucial step in identifying how the brain supports various psychological processes is a well-defined categorization or taxonomy of psychological processes and their

  14. Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting

    Science.gov (United States)

    Reeve, Dominic E.; Karunarathna, Harshinie; Pan, Shunqi; Horrillo-Caraballo, Jose M.; Różyński, Grzegorz; Ranasinghe, Roshanka

    2016-03-01

    It is now common for coastal planning to anticipate changes anywhere from 70 to 100 years into the future. The process models developed and used for scheme design or for large-scale oceanography are currently inadequate for this task. This has prompted the development of a plethora of alternative methods. Some, such as reduced complexity or hybrid models simplify the governing equations retaining processes that are considered to govern observed morphological behaviour. The computational cost of these models is low and they have proven effective in exploring morphodynamic trends and improving our understanding of mesoscale behaviour. One drawback is that there is no generally agreed set of principles on which to make the simplifying assumptions and predictions can vary considerably between models. An alternative approach is data-driven techniques that are based entirely on analysis and extrapolation of observations. Here, we discuss the application of some of the better known and emerging methods in this category to argue that with the increasing availability of observations from coastal monitoring programmes and the development of more sophisticated statistical analysis techniques data-driven models provide a valuable addition to the armoury of methods available for mesoscale prediction. The continuation of established monitoring programmes is paramount, and those that provide contemporaneous records of the driving forces and the shoreline response are the most valuable in this regard. In the second part of the paper we discuss some recent research that combining some of the hybrid techniques with data analysis methods in order to synthesise a more consistent means of predicting mesoscale coastal morphological evolution. While encouraging in certain applications a universally applicable approach has yet to be found. The route to linking different model types is highlighted as a major challenge and requires further research to establish its viability. We argue that

  15. Microenvironment temperature prediction between body and seat interface using autoregressive data-driven model.

    Science.gov (United States)

    Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W

    2015-11-01

    There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of <0.4 °C when used to predict the temperature at the seat and skin interface 15 min ahead, but required 45 min data prior to give this accuracy. Although the 45 min front loading of data appears large (in proportion to the 15 min prediction), a relative strength derives from the fact that the same algorithm could be used on the other 4 sitting datasets created by the same individual, suggesting that the period of 45 min required to train the algorithm is transferable to other data from the same individual. This approach might be developed (along with incorporation of other measures such as movement and humidity) into a system that can give caregivers prior warning to help avoid

  16. Voting-based cancer module identification by combining topological and data-driven properties.

    Science.gov (United States)

    Azad, A K M; Lee, Hyunju

    2013-01-01

    Recently, computational approaches integrating copy number aberrations (CNAs) and gene expression (GE) have been extensively studied to identify cancer-related genes and pathways. In this work, we integrate these two data sets with protein-protein interaction (PPI) information to find cancer-related functional modules. To integrate CNA and GE data, we first built a gene-gene relationship network from a set of seed genes by enumerating all types of pairwise correlations, e.g. GE-GE, CNA-GE, and CNA-CNA, over multiple patients. Next, we propose a voting-based cancer module identification algorithm by combining topological and data-driven properties (VToD algorithm) by using the gene-gene relationship network as a source of data-driven information, and the PPI data as topological information. We applied the VToD algorithm to 266 glioblastoma multiforme (GBM) and 96 ovarian carcinoma (OVC) samples that have both expression and copy number measurements, and identified 22 GBM modules and 23 OVC modules. Among 22 GBM modules, 15, 12, and 20 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Among 23 OVC modules, 19, 18, and 23 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Similarly, we also observed that 9 and 2 GBM modules and 15 and 18 OVC modules were enriched with cancer gene census (CGC) and specific cancer driver genes, respectively. Our proposed module-detection algorithm significantly outperformed other existing methods in terms of both functional and cancer gene set enrichments. Most of the cancer-related pathways from both cancer data sets found in our algorithm contained more than two types of gene-gene relationships, showing strong positive correlations between the number of different types of relationship and CGC enrichment [Formula: see text]-values (0.64 for GBM and 0.49 for OVC). This study suggests that identified modules containing

  17. Dynamics of speckles with a small number of scattering events: specific features of manifestation of the Doppler effect.

    Science.gov (United States)

    Ulyanov, Sergey S

    2014-04-01

    Spectra of intensity fluctuations of dynamic non-Gaussian speckles formed with a small number of scattering events have been studied theoretically and experimentally. A new type of manifestation of the Doppler effect has been observed. The dependence of frequency position of the Doppler peak and the shape of the Doppler spectrum on the number of scatterers has been analyzed.

  18. Moisture Sources and Large-Scale Dynamics Associated with a Flash Flood Event in Portugal

    Science.gov (United States)

    Liberato, Margarida L. R.; Ramos, Alexandre M.; Trigo, Ricardo M.; Trigo, Isabel F.; María Durán-Quesada, Ana; Nieto, Raquel; Gimeno, Luis

    2013-04-01

    through FCT (Fundação para a Ciência e a Tecnologia, Portugal) through project STORMEx FCOMP-01-0124-FEDER-019524 (PTDC/AAC-CLI/121339/2010). Margarida L. R. Liberato was also supported by a FCT grant (SFRH/BPD/45080/2008). Liberato M. L. R., A. M. Ramos, R. M. Trigo, I. F. Trigo, A. M. Durán-Quesada, R. Nieto, and L. Gimeno (2012) Moisture Sources and Large-scale Dynamics Associated with a Flash Flood Event. Lagrangian Modeling of the Atmosphere, Geophysical Monograph Series (in press). Stohl, A., and P. James (2004), A Lagrangian analysis of the atmospheric branch of the global water cycle. Part I: Method description, validation, and demonstration for the August 2002 flooding in central Europe, J. Hydrometeorol., 5, 656-678. Stohl, A., and P. James (2005), A Lagrangian analysis of the atmospheric branch of the global water cycle. Part II: Earth's river catchments, ocean basins, and moisture transports between them, J. Hydrometeorol., 6, 961-984. Zêzere, J. L., R. M. Trigo, and I. F. Trigo (2005), Shallow and deep landslides induced by rainfall in the Lisbon region (Portugal): Assessment of relationships with the North Atlantic Oscillation, Nat. Hazards Earth Syst. Sci., 5, 331-344.

  19. Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

    Directory of Open Access Journals (Sweden)

    Lifeng Wu

    2016-05-01

    Full Text Available Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

  20. Event-driven Dynamic and Intelligent Scheduling for Agile Manufacturing Based on Immune Mechanism and Expert System

    Institute of Scientific and Technical Information of China (English)

    李蓓智; 杨建国; 周亚勤; 邵世煌

    2003-01-01

    Based on the biological immune concept, immune response mechanism and expert system, a dynamic and intelligent scheduling model toward the disturbance of the production such as machine fault,task insert and cancel etc. Is proposed. The antibody generation method based on the sequence constraints and the coding rule of antibody for the machining procedure is also presented. Using the heuristic antibody generation method based on the physiology immune mechanism, the validity of the scheduling optimization is improved, and based on the immune and expert system under the event-driven constraints, not only Job-shop scheduling problem with multi-objective can be solved, but also the disturbance of the production be handled rapidly. A case of the job-shop scheduling is studied and dynamic optimal solutions with multi-objective function for agile manufacturing are obtained in this paper. And the event-driven dynamic rescheduling result is compared with right-shift rescheduling and total rescheduling.

  1. Modification of the SAS4A Safety Analysis Code for Integration with the ADAPT Discrete Dynamic Event Tree Framework.

    Energy Technology Data Exchange (ETDEWEB)

    Jankovsky, Zachary Kyle [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Denman, Matthew R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    It is difficult to assess the consequences of a transient in a sodium-cooled fast reactor (SFR) using traditional probabilistic risk assessment (PRA) methods, as numerous safety-related sys- tems have passive characteristics. Often there is significant dependence on the value of con- tinuous stochastic parameters rather than binary success/failure determinations. One form of dynamic PRA uses a system simulator to represent the progression of a transient, tracking events through time in a discrete dynamic event tree (DDET). In order to function in a DDET environment, a simulator must have characteristics that make it amenable to changing physical parameters midway through the analysis. The SAS4A SFR system analysis code did not have these characteristics as received. This report describes the code modifications made to allow dynamic operation as well as the linking to a Sandia DDET driver code. A test case is briefly described to demonstrate the utility of the changes.

  2. New data-driven method from 3D confocal microscopy for calculating phytoplankton cell biovolume.

    Science.gov (United States)

    Roselli, L; Paparella, F; Stanca, E; Basset, A

    2015-06-01

    Confocal laser scanner microscopy coupled with an image analysis system was used to directly determine the shape and calculate the biovolume of phytoplankton organisms by constructing 3D models of cells. The study was performed on Biceratium furca (Ehrenberg) Vanhoeffen, which is one of the most complex-shaped phytoplankton. Traditionally, biovolume is obtained from a standardized set of geometric models based on linear dimensions measured by light microscopy. However, especially in the case of complex-shaped cells, biovolume is affected by very large errors associated with the numerous manual measurements that this entails. We evaluate the accuracy of these traditional methods by comparing the results obtained using geometric models with direct biovolume measurement by image analysis. Our results show cell biovolume measurement based on decomposition into simple geometrical shapes can be highly inaccurate. Although we assume that the most accurate cell shape is obtained by 3D direct biovolume measurement, which is based on voxel counting, the intrinsic uncertainty of this method is explored and assessed. Finally, we implement a data-driven formula-based approach to the calculation of biovolume of this complex-shaped organism. On one hand, the model is obtained from 3D direct calculation. On the other hand, it is based on just two linear dimensions which can easily be measured by hand. This approach has already been used for investigating the complexities of morphology and for determining the 3D structure of cells. It could also represent a novel way to generalize scaling laws for biovolume calculation.

  3. Data-driven asthma endotypes defined from blood biomarker and gene expression data.

    Directory of Open Access Journals (Sweden)

    Barbara Jane George

    Full Text Available The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.

  4. Data driven models of the performance and repeatability of NIF high foot implosions

    Science.gov (United States)

    Gaffney, Jim; Casey, Dan; Callahan, Debbie; Hartouni, Ed; Ma, Tammy; Spears, Brian

    2015-11-01

    Recent high foot (HF) inertial confinement fusion (ICF) experiments performed at the national ignition facility (NIF) have consisted of enough laser shots that a data-driven analysis of capsule performance is feasible. In this work we use 20-30 individual implosions of similar design, spanning laser drive energies from 1.2 to 1.8 MJ, to quantify our current understanding of the behavior of HF ICF implosions. We develop a probabilistic model for the projected performance of a given implosion and use it to quantify uncertainties in predicted performance including shot-shot variations and observation uncertainties. We investigate the statistical significance of the observed performance differences between different laser pulse shapes, ablator materials, and capsule designs. Finally, using a cross-validation technique, we demonstrate that 5-10 repeated shots of a similar design are required before real trends in the data can be distinguished from shot-shot variations. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-674957.

  5. A data-driven approach for evaluating multi-modal therapy in traumatic brain injury

    Science.gov (United States)

    Haefeli, Jenny; Ferguson, Adam R.; Bingham, Deborah; Orr, Adrienne; Won, Seok Joon; Lam, Tina I.; Shi, Jian; Hawley, Sarah; Liu, Jialing; Swanson, Raymond A.; Massa, Stephen M.

    2017-01-01

    Combination therapies targeting multiple recovery mechanisms have the potential for additive or synergistic effects, but experimental design and analyses of multimodal therapeutic trials are challenging. To address this problem, we developed a data-driven approach to integrate and analyze raw source data from separate pre-clinical studies and evaluated interactions between four treatments following traumatic brain injury. Histologic and behavioral outcomes were measured in 202 rats treated with combinations of an anti-inflammatory agent (minocycline), a neurotrophic agent (LM11A-31), and physical therapy consisting of assisted exercise with or without botulinum toxin-induced limb constraint. Data was curated and analyzed in a linked workflow involving non-linear principal component analysis followed by hypothesis testing with a linear mixed model. Results revealed significant benefits of the neurotrophic agent LM11A-31 on learning and memory outcomes after traumatic brain injury. In addition, modulations of LM11A-31 effects by co-administration of minocycline and by the type of physical therapy applied reached statistical significance. These results suggest a combinatorial effect of drug and physical therapy interventions that was not evident by univariate analysis. The study designs and analytic techniques applied here form a structured, unbiased, internally validated workflow that may be applied to other combinatorial studies, both in animals and humans. PMID:28205533

  6. A priori data-driven multi-clustered reservoir generation algorithm for echo state network.

    Directory of Open Access Journals (Sweden)

    Xiumin Li

    Full Text Available Echo state networks (ESNs with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.

  7. A data-driven method to characterize turbulence-caused uncertainty in wind power generation

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jie; Jain, Rishabh; Hodge, Bri-Mathias

    2016-10-01

    A data-driven methodology is developed to analyze how ambient and wake turbulence affect the power generation of wind turbine(s). Using supervisory control and data acquisition (SCADA) data from a wind plant, we select two sets of wind velocity and power data for turbines on the edge of the plant that resemble (i) an out-of-wake scenario and (ii) an in-wake scenario. For each set of data, two surrogate models are developed to represent the turbine(s) power generation as a function of (i) the wind speed and (ii) the wind speed and turbulence intensity. Three types of uncertainties in turbine(s) power generation are investigated: (i) the uncertainty in power generation with respect to the reported power curve; (ii) the uncertainty in power generation with respect to the estimated power response that accounts for only mean wind speed; and (iii) the uncertainty in power generation with respect to the estimated power response that accounts for both mean wind speed and turbulence intensity. Results show that (i) the turbine(s) generally produce more power under the in-wake scenario than under the out-of-wake scenario with the same wind speed; and (ii) there is relatively more uncertainty in the power generation under the in-wake scenario than under the out-of-wake scenario.

  8. Data-Driven Simulation-Enhanced Optimization of People-Based Print Production Service

    Science.gov (United States)

    Rai, Sudhendu

    This paper describes a systematic six-step data-driven simulation-based methodology for optimizing people-based service systems on a large distributed scale that exhibit high variety and variability. The methodology is exemplified through its application within the printing services industry where it has been successfully deployed by Xerox Corporation across small, mid-sized and large print shops generating over 250 million in profits across the customer value chain. Each step of the methodology consisting of innovative concepts co-development and testing in partnership with customers, development of software and hardware tools to implement the innovative concepts, establishment of work-process and practices for customer-engagement and service implementation, creation of training and infrastructure for large scale deployment, integration of the innovative offering within the framework of existing corporate offerings and lastly the monitoring and deployment of the financial and operational metrics for estimating the return-on-investment and the continual renewal of the offering are described in detail.

  9. A novel data-driven approach to model error estimation in Data Assimilation

    Science.gov (United States)

    Pathiraja, Sahani; Moradkhani, Hamid; Marshall, Lucy; Sharma, Ashish

    2016-04-01

    Error characterisation is a fundamental component of Data Assimilation (DA) studies. Effectively describing model error statistics has been a challenging area, with many traditional methods requiring some level of subjectivity (for instance in defining the error covariance structure). Recent advances have focused on removing the need for tuning of error parameters, although there are still some outstanding issues. Many methods focus only on the first and second moments, and rely on assuming multivariate Gaussian statistics. We propose a non-parametric, data-driven framework to estimate the full distributional form of model error, ie. the transition density p(xt|xt-1). All sources of uncertainty associated with the model simulations are considered, without needing to assign error characteristics/devise stochastic perturbations for individual components of model uncertainty (eg. input, parameter and structural). A training period is used to derive the error distribution of observed variables, conditioned on (potentially hidden) states. Errors in hidden states are estimated from the conditional distribution of observed variables using non-linear optimization. The framework is discussed in detail, and an application to a hydrologic case study with hidden states for one-day ahead streamflow prediction is presented. Results demonstrate improved predictions and more realistic uncertainty bounds compared to a standard tuning approach.

  10. The Cannon 2: A data-driven model of stellar spectra for detailed chemical abundance analyses

    CERN Document Server

    Casey, Andrew R; Ness, Melissa; Rix, Hans-Walter; Ho, Anna Q Y; Gilmore, Gerry

    2016-01-01

    We have shown that data-driven models are effective for inferring physical attributes of stars (labels; Teff, logg, [M/H]) from spectra, even when the signal-to-noise ratio is low. Here we explore whether this is possible when the dimensionality of the label space is large (Teff, logg, and 15 abundances: C, N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe, Ni) and the model is non-linear in its response to abundance and parameter changes. We adopt ideas from compressed sensing to limit overall model complexity while retaining model freedom. The model is trained with a set of 12,681 red-giant stars with high signal-to-noise spectroscopic observations and stellar parameters and abundances taken from the APOGEE Survey. We find that we can successfully train and use a model with 17 stellar labels. Validation shows that the model does a good job of inferring all 17 labels (typical abundance precision is 0.04 dex), even when we degrade the signal-to-noise by discarding ~50% of the observing time. The model dependencie...

  11. The Cannon: A data-driven approach to stellar label determination

    CERN Document Server

    Ness, Melissa; Rix, Hans-Walter; Ho, Anna; Zasowski, Gail

    2015-01-01

    New spectroscopic surveys offer the promise of consistent stellar parameters and abundances ('stellar labels') for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge. In many cases, there is a sub-set of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new data-driven approach for determining stellar labels from spectroscopic data. The Cannon learns from the 'known' labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 543 stars in 19 clusters as reference objects, with Teff, log g and [Fe/H] as the labels, and then applying it to the spectra of 56,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeli...

  12. Quantitative Data-driven Utilization of Hematologic Labs Following Lumbar Fusion.

    Science.gov (United States)

    Yew, Andrew Y; Hoffman, Haydn; Li, Charles; McBride, Duncan Q; Holly, Langston T; Lu, Daniel C

    2015-05-01

    Retrospective case series. Large national inpatient databases estimate that approximately 200,000 lumbar fusions are performed annually in the United States alone. It is common for surgeons to routinely order postoperative hematologic studies to rule out postoperative anemia despite a paucity of data to support routine laboratory utilization. To describe quantitative criteria to guide postoperative utilization of hematologic laboratory assessments. A retrospective analysis of 490 consecutive lumbar fusion procedures performed at a single institution by 3 spine surgeons was performed. Inclusion criteria included instrumented and noninstrumented lumbar fusions performed for any etiology. Data were acquired on preoperative and postoperative hematocrit, platelets, and international normalized ratio as well as age, sex, number of levels undergoing operation, indication for surgery, and intraoperative blood loss. Multivariate logistic regression was performed to determine correlation to postoperative transfusion requirement. A total of 490 patients undergoing lumbar fusion were identified. Twenty-five patients (5.1%) required postoperative transfusion. No patients required readmission for anemia or transfusion. Multivariate logistic regression analysis demonstrated that reduced preoperative hematocrit and increased intraoperative blood loss were independent predictors of postoperative transfusion requirement. Intraoperative blood loss >1000 mL had an odds ratio of 8.9 (P=0.013), and preoperative hematocrit quantitative preoperative and intraoperative criteria to guide data-driven utilization of postoperative hematologic studies following lumbar fusion.

  13. Data-driven modeling based on volterra series for multidimensional blast furnace system.

    Science.gov (United States)

    Gao, Chuanhou; Jian, Ling; Liu, Xueyi; Chen, Jiming; Sun, Youxian

    2011-12-01

    The multidimensional blast furnace system is one of the most complex industrial systems and, as such, there are still many unsolved theoretical and experimental difficulties, such as silicon prediction and blast furnace automation. For this reason, this paper is concerned with developing data-driven models based on the Volterra series for this complex system. Three kinds of different low-order Volterra filters are designed to predict the hot metal silicon content collected from a pint-sized blast furnace, in which a sliding window technique is used to update the filter kernels timely. The predictive results indicate that the linear Volterra predictor can describe the evolvement of the studied silicon sequence effectively with the high percentage of hitting the target, very low root mean square error and satisfactory confidence level about the reliability of the future prediction. These advantages and the low computational complexity reveal that the sliding-window linear Volterra filter is full of potential for multidimensional blast furnace system. Also, the lack of the constructed Volterra models is analyzed and the possible direction of future investigation is pointed out.

  14. Data-driven research: open data opportunities for growing knowledge, and ethical issues that arise

    Directory of Open Access Journals (Sweden)

    Aleksandra K Krotoski

    2012-03-01

    Full Text Available The Open Data Initiative in the UK offers incredible opportunities for researchers who seek to gain insight from the wealth of public and institutional data that is increasingly available from government sources – like NHS prescription and GP referral information – or the information we freely offer online. Coupled with digital technologies that can help teams generate connections and collaborations, these data sets can support large-scale innovation and insight. However, by looking at a comparable explosion in data-driven journalism, this article hopes to highlight some of the ethical questions that may arise from big data. The popularity of the social networking service Twitter to share information during the riots in London in August 2011 produced a real-time record of sense-making of enormous interest to academics, reporters and to Twitter users themselves; however, when analysed and published, academic and journalistic interpretations of aggregate content was transformed and individualized, with potential implications for a user-base that was unaware it was being observed. Similar issues arise in academic research with human subjects. Here, the questions of reflexivity in data design and research ethics are considered through a popular media frame.

  15. Data-driven dissection of emission-line regions in Seyfert galaxies

    CERN Document Server

    Villarroel, Beatriz

    2016-01-01

    Indirectly resolving the line-emitting gas regions in distant Active Galactic Nuclei (AGN) requires both high-resolution photometry and spectroscopy (i.e. through reverberation mapping). Emission in AGN originates on widely different scales; the broad-line region (BLR) has a typical radius less than a few parsec, the narrow-line region (NLR) extends out to hundreds of parsecs. But emission also appears on large scales from heated nebulae in the host galaxies (tenths of kpc). We propose a novel, data-driven method based on correlations between emission-line fluxes to identify which of the emission lines are produced in the same kind of emission-line regions. We test the method on Seyfert galaxies from the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) and Galaxy Zoo project. We demonstrate the usefulness of the method on Seyfert-1s and Seyfert-2 objects, showing similar narrow-line regions (NLRs). Preliminary results from comparing Seyfert-2s in spiral and elliptical galaxy hosts suggest that the presenc...

  16. Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool.

    Science.gov (United States)

    Pironti, Alejandro; Pfeifer, Nico; Walter, Hauke; Jensen, Björn-Erik O; Zazzi, Maurizio; Gomes, Perpétua; Kaiser, Rolf; Lengauer, Thomas

    2017-01-01

    Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs.

  17. Combining knowledge- and data-driven methods for de-identification of clinical narratives.

    Science.gov (United States)

    Dehghan, Azad; Kovacevic, Aleksandar; Karystianis, George; Keane, John A; Nenadic, Goran

    2015-12-01

    A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Information (PHI). We describe the methods developed and evaluated as part of the i2b2/UTHealth 2014 challenge to identify PHI defined by 25 entity types in longitudinal clinical narratives. Our approach combines knowledge-driven (dictionaries and rules) and data-driven (machine learning) methods with a large range of features to address de-identification of specific named entities. In addition, we have devised a two-pass recognition approach that creates a patient-specific run-time dictionary from the PHI entities identified in the first step with high confidence, which is then used in the second pass to identify mentions that lack specific clues. The proposed method achieved the overall micro F1-measures of 91% on strict and 95% on token-level evaluation on the test dataset (514 narratives). Whilst most PHI entities can be reliably identified, particularly challenging were mentions of Organizations and Professions. Still, the overall results suggest that automated text mining methods can be used to reliably process clinical notes to identify personal information and thus providing a crucial step in large-scale de-identification of unstructured data for further clinical and epidemiological studies.

  18. Data-driven model-independent searches for long-lived particles at the LHC

    Science.gov (United States)

    Coccaro, Andrea; Curtin, David; Lubatti, H. J.; Russell, Heather; Shelton, Jessie

    2016-12-01

    Neutral long-lived particles (LLPs) are highly motivated by many beyond the Standard Model scenarios, such as theories of supersymmetry, baryogenesis, and neutral naturalness, and present both tremendous discovery opportunities and experimental challenges for the LHC. A major bottleneck for current LLP searches is the prediction of Standard Model backgrounds, which are often impossible to simulate accurately. In this paper, we propose a general strategy for obtaining differential, data-driven background estimates in LLP searches, thereby notably extending the range of LLP masses and lifetimes that can be discovered at the LHC. We focus on LLPs decaying in the ATLAS muon system, where triggers providing both signal and control samples are available at LHC run 2. While many existing searches require two displaced decays, a detailed knowledge of backgrounds will allow for very inclusive searches that require just one detected LLP decay. As we demonstrate for the h →X X signal model of LLP pair production in exotic Higgs decays, this results in dramatic sensitivity improvements for proper lifetimes ≳10 m . In theories of neutral naturalness, this extends reach to glueball masses far below the b ¯b threshold. Our strategy readily generalizes to other signal models and other detector subsystems. This framework therefore lends itself to the development of a systematic, model-independent LLP search program, in analogy to the highly successful simplified-model framework of prompt searches.

  19. Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach

    CERN Document Server

    Ji, Yongnan; Aickelin, Uwe; Pitiot, Alain

    2010-01-01

    Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM)and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. F...

  20. Data-driven quantification of the robustness and sensitivity of cell signaling networks

    Science.gov (United States)

    Mukherjee, Sayak; Seok, Sang-Cheol; Vieland, Veronica J.; Das, Jayajit

    2013-12-01

    Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems.