WorldWideScience

Sample records for dynamic data-driven event

  1. Dynamic Data-Driven UAV Network for Plume Characterization

    Science.gov (United States)

    2016-05-23

    AFRL-AFOSR-VA-TR-2016-0203 Dynamic Data-Driven UAV Network for Plume Characterization Kamran Mohseni UNIVERSITY OF FLORIDA Final Report 05/23/2016...AND SUBTITLE Dynamic Data-Driven UAV Network for Plume Characterization 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-13-1-0090 5c.  PROGRAM ELEMENT...studied a dynamic data driven (DDD) approach to operation of a heterogeneous team of unmanned aerial vehicles ( UAVs ) or micro/miniature aerial

  2. Dynamically adaptive data-driven simulation of extreme hydrological flows

    KAUST Repository

    Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim; Knio, Omar; Dawson, Clint

    2017-01-01

    evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses

  3. Dynamic Data Driven Applications Systems (DDDAS)

    Science.gov (United States)

    2013-03-06

    detected Level 1 (L1) sensors: PIR & Piezoelectric Level 2 (L2) sensor: Overhead camera (UAV) Level 1.1 sensor: LIDAR Dynamic Influence Diagram ID1...Effects of Porous Shape Memory Alloys • Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring • Fluid SLAM and the Robotic...Structural Health Monitoring – PI: Thomas Henderson, U. of Utah • Fluid SLAM and the Robotic Reconstruction of Localized Atmospheric Phenomena – PI

  4. Dynamically adaptive data-driven simulation of extreme hydrological flows

    Science.gov (United States)

    Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim; Knio, Omar; Dawson, Clint

    2018-02-01

    Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

  5. Dynamically adaptive data-driven simulation of extreme hydrological flows

    KAUST Repository

    Kumar Jain, Pushkar

    2017-12-27

    Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

  6. 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. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  7. 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.

  8. 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....

  9. Estimating the Probability of Wind Ramping Events: A Data-driven Approach

    OpenAIRE

    Wang, Cheng; Wei, Wei; Wang, Jianhui; Qiu, Feng

    2016-01-01

    This letter proposes a data-driven method for estimating the probability of wind ramping events without exploiting the exact probability distribution function (PDF) of wind power. Actual wind data validates the proposed method.

  10. Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models

    DEFF Research Database (Denmark)

    Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin

    2017-01-01

    In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21...

  11. NOvA Event Building, Buffering and Data-Driven Triggering From Within the DAQ System

    Energy Technology Data Exchange (ETDEWEB)

    Fischler, M. [Fermilab; Green, C. [Fermilab; Kowalkowski, J. [Fermilab; Norman, A. [Fermilab; Paterno, M. [Fermilab; Rechenmacher, R. [Fermilab

    2012-06-22

    To make its core measurements, the NOvA experiment needs to make real-time data-driven decisions involving beam-spill time correlation and other triggering issues. NOvA-DDT is a prototype Data-Driven Triggering system, built using the Fermilab artdaq generic DAQ/Event-building tools set. This provides the advantages of sharing online software infrastructure with other Intensity Frontier experiments, and of being able to use any offline analysis module--unchanged--as a component of the online triggering decisions. The NOvA-artdaq architecture chosen has significant advantages, including graceful degradation if the triggering decision software fails or cannot be done quickly enough for some fraction of the time-slice ``events.'' We have tested and measured the performance and overhead of NOvA-DDT using an actual Hough transform based trigger decision module taken from the NOvA offline software. The results of these tests--98 ms mean time per event on only 1/16 of th e available processing power of a node, and overheads of about 2 ms per event--provide a proof of concept: NOvA-DDT is a viable strategy for data acquisition, event building, and trigger processing at the NOvA far detector.

  12. NOvA Event Building, Buffering and Data-Driven Triggering From Within the DAQ System

    International Nuclear Information System (INIS)

    Fischler, M; Rechenmacher, R; Green, C; Kowalkowski, J; Norman, A; Paterno, M

    2012-01-01

    The NOvA experiment is a long baseline neutrino experiment design to make precision probes of the structure of neutrino mixing. The experiment features a unique deadtimeless data acquisition system that is capable acquiring and building an event data stream from the continuous readout of the more than 360,000 far detector channels. In order to achieve its physics goals the experiment must be able to buffer, correlate and extract the data in this stream with the beam-spills that occur that Fermilab. In addition the NOvA experiment seeks to enhance its data collection efficiencies for rare class of event topologies that are valuable for calibration through the use of data driven triggering. The NOvA-DDT is a prototype Data-Driven Triggering system. NOvA-DDT has been developed using the Fermilab artdaq generic DAQ/Event-building toolkit. This toolkit provides the advantages of sharing online software infrastructure with other Intensity Frontier experiments, and of being able to use any offline analysis module-unchanged-as a component of the online triggering decisions. We have measured the performance and overhead of NOvA-DDT framework using a Hough transform based trigger decision module developed for the NOvA detector to identify cosmic rays. The results of these tests which were run on the NOvA prototype near detector, yielded a mean processing time of 98 ms per event, while consuming only 1/16th of the available processing capacity. These results provide a proof of concept that a NOvA-DDT based processing system is a viable strategy for data acquisition and triggering for the NOvA far detector.

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

    KAUST Repository

    Douglas, Craig

    2014-01-01

    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.

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

    KAUST Repository

    Douglas, Craig

    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.

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

    DEFF Research Database (Denmark)

    Knudsen, Torben; Bak, Thomas

    2012-01-01

    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......Wind turbines in a wind farm, influence each other through the wind flow. Downwind turbines are in the wake of upwind turbines and the wind speed experienced at downwind turbines is hence a function of the wind speeds at upwind turbines but also the momentum extracted from the wind by the upwind....... 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...

  18. Data-driven Inference and Investigation of Thermosphere Dynamics and Variations

    Science.gov (United States)

    Mehta, P. M.; Linares, R.

    2017-12-01

    This paper presents a methodology for data-driven inference and investigation of thermosphere dynamics and variations. The approach uses data-driven modal analysis to extract the most energetic modes of variations for neutral thermospheric species using proper orthogonal decomposition, where the time-independent modes or basis represent the dynamics and the time-depedent coefficients or amplitudes represent the model parameters. The data-driven modal analysis approach combined with sparse, discrete observations is used to infer amplitues for the dynamic modes and to calibrate the energy content of the system. In this work, two different data-types, namely the number density measurements from TIMED/GUVI and the mass density measurements from CHAMP/GRACE are simultaneously ingested for an accurate and self-consistent specification of the thermosphere. The assimilation process is achieved with a non-linear least squares solver and allows estimation/tuning of the model parameters or amplitudes rather than the driver. In this work, we use the Naval Research Lab's MSIS model to derive the most energetic modes for six different species, He, O, N2, O2, H, and N. We examine the dominant drivers of variations for helium in MSIS and observe that seasonal latitudinal variation accounts for about 80% of the dynamic energy with a strong preference of helium for the winter hemisphere. We also observe enhanced helium presence near the poles at GRACE altitudes during periods of low solar activity (Feb 2007) as previously deduced. We will also examine the storm-time response of helium derived from observations. The results are expected to be useful in tuning/calibration of the physics-based models.

  19. 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.

  20. 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.

  1. 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. PMID:26771830

  2. Linear dynamical modes as new variables for data-driven ENSO forecast

    Science.gov (United States)

    Gavrilov, Andrey; Seleznev, Aleksei; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander; Kurths, Juergen

    2018-05-01

    A new data-driven model for analysis and prediction of spatially distributed time series is proposed. The model is based on a linear dynamical mode (LDM) decomposition of the observed data which is derived from a recently developed nonlinear dimensionality reduction approach. The key point of this approach is its ability to take into account simple dynamical properties of the observed system by means of revealing the system's dominant time scales. The LDMs are used as new variables for empirical construction of a nonlinear stochastic evolution operator. The method is applied to the sea surface temperature anomaly field in the tropical belt where the El Nino Southern Oscillation (ENSO) is the main mode of variability. The advantage of LDMs versus traditionally used empirical orthogonal function decomposition is demonstrated for this data. Specifically, it is shown that the new model has a competitive ENSO forecast skill in comparison with the other existing ENSO models.

  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 Modeling of Complex Systems by means of a Dynamical ANN

    Science.gov (United States)

    Seleznev, A.; Mukhin, D.; Gavrilov, A.; Loskutov, E.; Feigin, A.

    2017-12-01

    The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: (i) determining the phase subspace of the system, or embedding, from available time series and (ii) constructing an evolution operator acting in this reduced subspace. In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network (ANN) with special topology. The proposed ANN-based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold. Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on. The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed. The study was supported by the Russian Science Foundation (grant #16-12-10198).

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

    Directory of Open Access Journals (Sweden)

    Thomas Nowotny

    2008-07-01

    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. BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

    Science.gov (United States)

    Zhao, Ming; Rattanatamrong, Prapaporn; DiGiovanna, Jack; Mahmoudi, Babak; Figueiredo, Renato J; Sanchez, Justin C; Príncipe, José C; Fortes, José A B

    2008-01-01

    Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

  8. Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.

    Directory of Open Access Journals (Sweden)

    David Henriques

    2017-02-01

    Full Text Available Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models, which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks: it builds dynamic (based on ordinary differential equation models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training. For this task, SELDOM's ensemble prediction is not only consistently better

  9. Dynamic Data-Driven Prediction of Lean Blowout in a Swirl-Stabilized Combustor

    Directory of Open Access Journals (Sweden)

    Soumalya Sarkar

    2015-09-01

    Full Text Available This paper addresses dynamic data-driven prediction of lean blowout (LBO phenomena in confined combustion processes, which are prevalent in many physical applications (e.g., land-based and aircraft gas-turbine engines. The underlying concept is built upon pattern classification and is validated for LBO prediction with time series of chemiluminescence sensor data from a laboratory-scale swirl-stabilized dump combustor. The proposed method of LBO prediction makes use of the theory of symbolic dynamics, where (finite-length time series data are partitioned to produce symbol strings that, in turn, generate a special class of probabilistic finite state automata (PFSA. These PFSA, called D-Markov machines, have a deterministic algebraic structure and their states are represented by symbol blocks of length D or less, where D is a positive integer. The D-Markov machines are constructed in two steps: (i state splitting, i.e., the states are split based on their information contents, and (ii state merging, i.e., two or more states (of possibly different lengths are merged together to form a new state without any significant loss of the embedded information. The modeling complexity (e.g., number of states of a D-Markov machine model is observed to be drastically reduced as the combustor approaches LBO. An anomaly measure, based on Kullback-Leibler divergence, is constructed to predict the proximity of LBO. The problem of LBO prediction is posed in a pattern classification setting and the underlying algorithms have been tested on experimental data at different extents of fuel-air premixing and fuel/air ratio. It is shown that, over a wide range of fuel-air premixing, D-Markov machines with D > 1 perform better as predictors of LBO than those with D = 1.

  10. Component-Based Data-Driven Predictive Maintenance to Reduce Unscheduled Maintenance Events

    NARCIS (Netherlands)

    Verhagen, W.J.C.; Curran, R.; de Boer, L.W.M.; Chen, C.H.; Trappey, A.C.; Peruzzini, M.; Stjepandić, J.; Wognum, N.

    2017-01-01

    Costs associated with unscheduled and preventive maintenance can contribute significantly to an airline's expenditure. Reliability analysis can help to identify and plan for maintenance events. Reliability analysis in industry is often limited to statistically based

  11. An asynchronous data-driven event-building scheme based on ATM switching fabrics

    International Nuclear Information System (INIS)

    Letheren, M.; Christiansen, J.; Mandjavidze, I.; Verhille, H.; De Prycker, M.; Pauwels, B.; Petit, G.; Wright, S.; Lumley, J.

    1994-01-01

    The very high data rates expected in experiments at the next generation of high luminosity hadron colliders will be handled by pipelined front-end readout electronics and multiple levels (2 or 3) of triggering. A variety of data acquisition architectures have been proposed for use downstream of the first level trigger. Depending on the architecture, the aggregate bandwidths required for event building are expected to be of the order 10--100 Gbit/s. Here, an Asynchronous Transfer Mode (ATM) packet-switching network technology is proposed as the interconnect for building high-performance, scalable data acquisition architectures. This paper introduces the relevant characteristics of ATM and describes components for the construction of an ATM-based event builder: (1) a multi-path, self-routing, scalable ATM switching fabric, (2) an experimental high performance workstation ATM-interface, and (3) a VMEbus ATM-interface. The requirement for traffic shaping in ATM-based event-builders is discussed and an analysis of the performance of several such schemes is presented

  12. 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 ...

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

    Science.gov (United States)

    Abler, Daniel; Kanellopoulos, Vassiliki; 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 and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results. PMID:23824126

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

    International Nuclear Information System (INIS)

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

    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 and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results. (author)

  15. 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...

  16. 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.

  17. Dynamic data-driven integrated flare model based on self-organized criticality

    Science.gov (United States)

    Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M. K.

    2013-05-01

    Context. We interpret solar flares as events originating in active regions that have reached the self-organized critical state. We describe them with a dynamic integrated flare model whose initial conditions and driving mechanism are derived from observations. Aims: We investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. Methods: To investigate whether the distribution functions of total energy, peak energy, and event duration follow the expected scaling laws, we first applied the previously reported static cellular automaton model to a time series of seven solar vector magnetograms of the NOAA active region 8210 recorded by the Imaging Vector Magnetograph on May 1 1998 between 18:59 UT and 23:16 UT until the self-organized critical state was reached. We then evolved the magnetic field between these processed snapshots through spline interpolation, mimicking a natural driver in our dynamic model. We identified magnetic discontinuities that exceeded a threshold in the Laplacian of the magnetic field after each interpolation step. These discontinuities were relaxed in local diffusion events, implemented in the form of cellular automaton evolution rules. Subsequent interpolation and relaxation steps covered all transitions until the end of the processed magnetograms' sequence. We additionally advanced each magnetic configuration that has reached the self-organized critical state (SOC configuration) by the static model until 50 more flares were triggered, applied the dynamic model again to the new sequence, and repeated the same process sufficiently often to generate adequate statistics. Physical requirements, such as the divergence-free condition for the magnetic field, were approximately imposed. Results: We obtain robust power laws in the distribution functions of the modeled flaring events with scaling indices that agree well

  18. Enhanced dynamic data-driven fault detection approach: Application to a two-tank heater system

    KAUST Repository

    Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying; Kammammettu, Sanjula

    2018-01-01

    on PCA approach a challenging task. Accounting for the dynamic nature of data can also reflect the performance of the designed fault detection approaches. In PCA-based methods, this dynamic characteristic of the data can be accounted for by using dynamic

  19. Transition Manifolds of Complex Metastable Systems: Theory and Data-Driven Computation of Effective Dynamics.

    Science.gov (United States)

    Bittracher, Andreas; Koltai, Péter; Klus, Stefan; Banisch, Ralf; Dellnitz, Michael; Schütte, Christof

    2018-01-01

    We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

  20. SPECT acquisition using dynamic projections: a novel approach for data-driven respiratory gating

    International Nuclear Information System (INIS)

    Hutton, B.F.; Hatton, R.L.; Yip, N.

    2002-01-01

    Full text: Movement of the heart due to respiration has been previously demonstrated to produce potentially serious artefacts. On-line respiratory gating is difficult, as it requires a high level of patient cooperation. We demonstrate that use of dynamic acquisition of projections permits identification of the respiratory dynamics, allowing retrospective selection of data corresponding to a fixed point in the respiratory cycle. To demonstrate the feasibility of the technique a dynamic study was acquired just prior to myocardial per-fusion SPECT acquisition, using 5 frames/sec for 20 seconds (64*64 matrix) in anterior and lateral projections (using a dual-head right-angled configuration). The dynamic was processed a) by compressing frames in the transverse direction so as to illustrate time dependence, b) by plotting the centre of mass in the axial direction as a function of time. Respiratory motion was enhanced by use of temporal smoothing and intensity thresholding. In ten patients studied the cyclic pattern of motion due to respiratory dynamics was clearly visible in nine. Respiration typically resulted in around 1cm axial translation but in some individuals, movements as large as 3 cm were identified. The respiration rate ranged from 12-18 /min in agreement with independent observation of the patient's breathing pattern. These results suggest that retrospective respiratory gating is feasible without the need for any external respiratory monitoring device, provide that dynamic acquisition of SPECT projections is implemented. Correction for respiratory motion may also be feasible using this technique. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

  1. Enhanced dynamic data-driven fault detection approach: Application to a two-tank heater system

    KAUST Repository

    Harrou, Fouzi

    2018-02-12

    Principal components analysis (PCA) has been intensively studied and used in monitoring industrial systems. However, data generated from chemical processes are usually correlated in time due to process dynamics, which makes the fault detection based on PCA approach a challenging task. Accounting for the dynamic nature of data can also reflect the performance of the designed fault detection approaches. In PCA-based methods, this dynamic characteristic of the data can be accounted for by using dynamic PCA (DPCA), in which lagged variables are used in the PCA model to capture the time evolution of the process. This paper presents a new approach that combines the DPCA to account for autocorrelation in data and generalized likelihood ratio (GLR) test to detect faults. A DPCA model is applied to perform dimension reduction while appropriately considering the temporal relationships in the data. Specifically, the proposed approach uses the DPCA to generate residuals, and then apply GLR test to reveal any abnormality. The performances of the proposed method are evaluated through a continuous stirred tank heater system.

  2. Data driven analysis of dynamic contrast-enhanced magnetic resonance imaging data in breast cancer diagnosis

    NARCIS (Netherlands)

    Twellmann, T.

    2005-01-01

    In the European Union, breast cancer is the most common type of cancer affecting women. If diagnosed in an early stage, breast cancer has an encouraging cure rate. Thus, early detection of breast cancer continues to be the key for an effective treatment. Recently, Dynamic Contrast-Enhanced Magnetic

  3. Low-back electromyography (EMG data-driven load classification for dynamic lifting tasks.

    Directory of Open Access Journals (Sweden)

    Deema Totah

    Full Text Available Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs, while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset.Regions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10% to 81% (±7%. The average recall for each class ranged from 69-92%.These inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications.Active assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.

  4. Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator.

    Science.gov (United States)

    Li, Qianxiao; Dietrich, Felix; Bollt, Erik M; Kevrekidis, Ioannis G

    2017-10-01

    Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD) 51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.

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

    KAUST Repository

    Yu, Han; Douglas, Craig C.; Ogden, Fred L.

    2012-01-01

    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.

  6. 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.

  7. 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.)

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

    International Nuclear Information System (INIS)

    Mehlhase, Sascha

    2010-01-01

    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 -1 at √(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.)

  9. Dynamics of Charged Events

    International Nuclear Information System (INIS)

    Bachas, Constantin; Bunster, Claudio; Henneaux, Marc

    2009-01-01

    In three spacetime dimensions the world volume of a magnetic source is a single point, an event. We make the event dynamical by regarding it as the imprint of a flux-carrying particle impinging from an extra dimension. This can be generalized to higher spacetime dimensions and to extended events. We exhibit universal observable consequences of the existence of events and argue that events are as important as particles or branes. We explain how events arise on the world volume of membranes in M theory, and in a Josephson junction in superconductivity.

  10. Satellite data driven modeling system for predicting air quality and visibility during wildfire and prescribed burn events

    Science.gov (United States)

    Nair, U. S.; Keiser, K.; Wu, Y.; Maskey, M.; Berendes, D.; Glass, P.; Dhakal, A.; Christopher, S. A.

    2012-12-01

    The Alabama Forestry Commission (AFC) is responsible for wildfire control and also prescribed burn management in the state of Alabama. Visibility and air quality degradation resulting from smoke are two pieces of information that are crucial for this activity. Currently the tools available to AFC are the dispersion index available from the National Weather Service and also surface smoke concentrations. The former provides broad guidance for prescribed burning activities but does not provide specific information regarding smoke transport, areas affected and quantification of air quality and visibility degradation. While the NOAA operational air quality guidance includes surface smoke concentrations from existing fire events, it does not account for contributions from background aerosols, which are important for the southeastern region including Alabama. Also lacking is the quantification of visibility. The University of Alabama in Huntsville has developed a state-of-the-art integrated modeling system to address these concerns. This system based on the Community Air Quality Modeling System (CMAQ) that ingests satellite derived smoke emissions and also assimilates NASA MODIS derived aerosol optical thickness. In addition, this operational modeling system also simulates the impact of potential prescribed burn events based on location information derived from the AFC prescribed burn permit database. A lagrangian model is used to simulate smoke plumes for the prescribed burns requests. The combined air quality and visibility degradation resulting from these smoke plumes and background aerosols is computed and the information is made available through a web based decision support system utilizing open source GIS components. This system provides information regarding intersections between highways and other critical facilities such as old age homes, hospitals and schools. The system also includes satellite detected fire locations and other satellite derived datasets

  11. Data-driven storytelling

    CERN Document Server

    Hurter, Christophe; Diakopoulos, Nicholas ed.; Carpendale, Sheelagh

    2018-01-01

    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.

  12. Initial Results from an Energy-Aware Airborne Dynamic, Data-Driven Application System Performing Sampling in Coherent Boundary-Layer Structures

    Science.gov (United States)

    Frew, E.; Argrow, B. M.; Houston, A. L.; Weiss, C.

    2014-12-01

    The energy-aware airborne dynamic, data-driven application system (EA-DDDAS) performs persistent sampling in complex atmospheric conditions by exploiting wind energy using the dynamic data-driven application system paradigm. The main challenge for future airborne sampling missions is operation with tight integration of physical and computational resources over wireless communication networks, in complex atmospheric conditions. The physical resources considered here include sensor platforms, particularly mobile Doppler radar and unmanned aircraft, the complex conditions in which they operate, and the region of interest. Autonomous operation requires distributed computational effort connected by layered wireless communication. Onboard decision-making and coordination algorithms can be enhanced by atmospheric models that assimilate input from physics-based models and wind fields derived from multiple sources. These models are generally too complex to be run onboard the aircraft, so they need to be executed in ground vehicles in the field, and connected over broadband or other wireless links back to the field. Finally, the wind field environment drives strong interaction between the computational and physical systems, both as a challenge to autonomous path planning algorithms and as a novel energy source that can be exploited to improve system range and endurance. Implementation details of a complete EA-DDDAS will be provided, along with preliminary flight test results targeting coherent boundary-layer structures.

  13. 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...

  14. 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

  15. Dynamic Data-Driven Reduced-Order Models of Macroscale Quantities for the Prediction of Equilibrium System State for Multiphase Porous Medium Systems

    Science.gov (United States)

    Talbot, C.; McClure, J. E.; Armstrong, R. T.; Mostaghimi, P.; Hu, Y.; Miller, C. T.

    2017-12-01

    Microscale simulation of multiphase flow in realistic, highly-resolved porous medium systems of a sufficient size to support macroscale evaluation is computationally demanding. Such approaches can, however, reveal the dynamic, steady, and equilibrium states of a system. We evaluate methods to utilize dynamic data to reduce the cost associated with modeling a steady or equilibrium state. We construct data-driven models using extensions to dynamic mode decomposition (DMD) and its connections to Koopman Operator Theory. DMD and its variants comprise a class of equation-free methods for dimensionality reduction of time-dependent nonlinear dynamical systems. DMD furnishes an explicit reduced representation of system states in terms of spatiotemporally varying modes with time-dependent oscillation frequencies and amplitudes. We use DMD to predict the steady and equilibrium macroscale state of a realistic two-fluid porous medium system imaged using micro-computed tomography (µCT) and simulated using the lattice Boltzmann method (LBM). We apply Koopman DMD to direct numerical simulation data resulting from simulations of multiphase fluid flow through a 1440x1440x4320 section of a full 1600x1600x5280 realization of imaged sandstone. We determine a representative set of system observables via dimensionality reduction techniques including linear and kernel principal component analysis. We demonstrate how this subset of macroscale quantities furnishes a representation of the time-evolution of the system in terms of dynamic modes, and discuss the selection of a subset of DMD modes yielding the optimal reduced model, as well as the time-dependence of the error in the predicted equilibrium value of each macroscale quantity. Finally, we describe how the above procedure, modified to incorporate methods from compressed sensing and random projection techniques, may be used in an online fashion to facilitate adaptive time-stepping and parsimonious storage of system states over time.

  16. InfoSymbiotics/DDDAS - The power of Dynamic Data Driven Applications Systems for New Capabilities in Environmental -, Geo-, and Space- Sciences

    Science.gov (United States)

    Darema, F.

    2016-12-01

    InfoSymbiotics/DDDAS embodies the power of Dynamic Data Driven Applications Systems (DDDAS), a concept whereby an executing application model is dynamically integrated, in a feed-back loop, with the real-time data-acquisition and control components, as well as other data sources of the application system. Advanced capabilities can be created through such new computational approaches in modeling and simulations, and in instrumentation methods, and include: enhancing the accuracy of the application model; speeding-up the computation to allow faster and more comprehensive models of a system, and create decision support systems with the accuracy of full-scale simulations; in addition, the notion of controlling instrumentation processes by the executing application results in more efficient management of application-data and addresses challenges of how to architect and dynamically manage large sets of heterogeneous sensors and controllers, an advance over the static and ad-hoc ways of today - with DDDAS these sets of resources can be managed adaptively and in optimized ways. Large-Scale-Dynamic-Data encompasses the next wave of Big Data, and namely dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems, through multitudes of heterogeneous sensors and controllers instrumenting these systems, and where opportunities and challenges at these "large-scales" relate not only to data size but the heterogeneity in data, data collection modalities, fidelities, and timescales, ranging from real-time data to archival data. In tandem with this important dimension of dynamic data, there is an extended view of Big Computing, which includes the collective computing by networked assemblies of multitudes of sensors and controllers, this range from the high-end to the real-time seamlessly integrated and unified, and comprising the Large-Scale-Big-Computing. InfoSymbiotics/DDDAS engenders transformative impact in many application domains

  17. 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. Assum...

  18. Data-driven robust control of the plasma rotational transform profile and normalized beta dynamics for advanced tokamak scenarios in DIII-D

    Energy Technology Data Exchange (ETDEWEB)

    Shi, W.; Wehner, W.P.; Barton, J.E.; Boyer, M.D. [Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA 18015 (United States); Schuster, E., E-mail: schuster@lehigh.edu [Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA 18015 (United States); Moreau, D. [CEA, IRFM, F-13018 St Paul lez Durance (France); Walker, M.L.; Ferron, J.R.; Luce, T.C.; Humphreys, D.A.; Penaflor, B.G.; Johnson, R.D. [General Atomics, San Diego, CA 92121 (United States)

    2017-04-15

    A control-oriented, two-timescale, linear, dynamic, response model of the rotational transform ι profile and the normalized beta β{sub N} is proposed based on experimental data from the DIII-D tokamak. Dedicated system-identification experiments without feedback control have been carried out to generate data for the development of this model. The data-driven dynamic model, which is both device-specific and scenario-specific, represents the response of the ι profile and β{sub N} to the electric field due to induction as well as to the heating and current drive (H&CD) systems during the flat-top phase of an H-mode discharge in DIII-D. The control goal is to use both induction and the H&CD systems to locally regulate the plasma ι profile and β{sub N} around particular target values close to the reference state used for system identification. A singular value decomposition (SVD) of the plasma model at steady state is carried out to decouple the system and identify the most relevant control channels. A mixed-sensitivity robust control design problem is formulated based on the dynamic model to synthesize a stabilizing feedback controller without input constraints that minimizes the reference tracking error and rejects external disturbances with minimal control energy. The feedback controller is then augmented with an anti-windup compensator, which keeps the given controller well-behaved in the presence of magnitude constraints in the actuators and leaves the nominal closed-loop system unmodified when no saturation is present. The proposed controller represents one of the first feedback profile controllers integrating magnetic and kinetic variables ever implemented and experimentally tested in DIII-D. The preliminary experimental results presented in this work, although limited in number and constrained by actuator problems and design limitations, as it will be reported, show good progress towards routine current profile control in DIII-D and leave valuable lessons

  19. Implementation and integration in the L3 experimentation of a level-2 trigger with event building, based on C104 data driven cross-bar switches and on T9000 transputers

    International Nuclear Information System (INIS)

    Masserot, A.

    1995-01-01

    This thesis describes the new level-2 trigger system. It has been developed to fit the L3 requirements induced by the LEP phase 2 conditions. At each beam crossing, the system memorizes the trigger data, builds-up the events selected by the level-1 hard-wired processors and finally rejects on-line the background identified by algorithms coded in Fortran. Based on T9000 Transputers and on C104 data driven cross-bar switches, the system uses prototypes designed by INMOS/SGS THOMSON for parallel processing applications. Emphasis is set on a new event building technic, on its integration in L3 and on performance. (author). 38 refs., 68 figs., 36 tabs

  20. Dynamic SEP event probability forecasts

    Science.gov (United States)

    Kahler, S. W.; Ling, A.

    2015-10-01

    The forecasting of solar energetic particle (SEP) event probabilities at Earth has been based primarily on the estimates of magnetic free energy in active regions and on the observations of peak fluxes and fluences of large (≥ M2) solar X-ray flares. These forecasts are typically issued for the next 24 h or with no definite expiration time, which can be deficient for time-critical operations when no SEP event appears following a large X-ray flare. It is therefore important to decrease the event probability forecast with time as a SEP event fails to appear. We use the NOAA listing of major (≥10 pfu) SEP events from 1976 to 2014 to plot the delay times from X-ray peaks to SEP threshold onsets as a function of solar source longitude. An algorithm is derived to decrease the SEP event probabilities with time when no event is observed to reach the 10 pfu threshold. In addition, we use known SEP event size distributions to modify probability forecasts when SEP intensity increases occur below the 10 pfu event threshold. An algorithm to provide a dynamic SEP event forecast, Pd, for both situations of SEP intensities following a large flare is derived.

  1. 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...

  2. 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%.

  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. 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

  5. Data Driven Economic Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Masoud Kheradmandi

    2018-04-01

    Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

  6. Data-driven modelling of LTI systems using symbolic regression

    NARCIS (Netherlands)

    Khandelwal, D.; Toth, R.; Van den Hof, P.M.J.

    2017-01-01

    The aim of this project is to automate the task of data-driven identification of dynamical systems. The underlying goal is to develop an identification tool that models a physical system without distinguishing between classes of systems such as linear, nonlinear or possibly even hybrid systems. Such

  7. Dynamic Data Driven Applications Systems (DDDAS)

    Science.gov (United States)

    2012-05-03

    response) – Earthquakes, hurricanes, tornados, wildfires, floods, landslides, tsunamis, … • Critical Infrastructure systems – Electric-powergrid...Multiphase Flow Weather and Climate Structural Mechanics Seismic Processing Aerodynamics Geophysical Fluids Quantum Chemistry Actinide Chemistry...Alloys • Approach and Objectives:  Consider porous SMAs:  similar macroscopic behavior but mass /weight is less, and thus attractive for

  8. Combining engineering and data-driven approaches

    DEFF Research Database (Denmark)

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

    2015-01-01

    Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many assump...... to the calibration of a generic fire risk model for single family houses to Swiss insurance data. The example demonstrates that the bias in the risk estimation can be strongly reduced by model calibration.......Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many...... 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...

  9. Data-Driven Problems in Elasticity

    Science.gov (United States)

    Conti, S.; Müller, S.; Ortiz, M.

    2018-01-01

    We consider a new class of problems in elasticity, referred to as Data-Driven problems, defined on the space of strain-stress field pairs, or phase space. The problem consists of minimizing the distance between a given material data set and the subspace of compatible strain fields and stress fields in equilibrium. We find that the classical solutions are recovered in the case of linear elasticity. We identify conditions for convergence of Data-Driven solutions corresponding to sequences of approximating material data sets. Specialization to constant material data set sequences in turn establishes an appropriate notion of relaxation. We find that relaxation within this Data-Driven framework is fundamentally different from the classical relaxation of energy functions. For instance, we show that in the Data-Driven framework the relaxation of a bistable material leads to material data sets that are not graphs.

  10. Data-Driven Model Reduction and Transfer Operator Approximation

    Science.gov (United States)

    Klus, Stefan; Nüske, Feliks; Koltai, Péter; Wu, Hao; Kevrekidis, Ioannis; Schütte, Christof; Noé, Frank

    2018-06-01

    In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis, dynamic mode decomposition, and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods.

  11. Consistent data-driven computational mechanics

    Science.gov (United States)

    González, D.; Chinesta, F.; Cueto, E.

    2018-05-01

    We present a novel method, within the realm of data-driven computational mechanics, to obtain reliable and thermodynamically sound simulation from experimental data. We thus avoid the need to fit any phenomenological model in the construction of the simulation model. This kind of techniques opens unprecedented possibilities in the framework of data-driven application systems and, particularly, in the paradigm of industry 4.0.

  12. 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

  13. Data-assisted reduced-order modeling of extreme events in complex dynamical systems.

    Directory of Open Access Journals (Sweden)

    Zhong Yi Wan

    Full Text Available The prediction of extreme events, from avalanches and droughts to tsunamis and epidemics, depends on the formulation and analysis of relevant, complex dynamical systems. Such dynamical systems are characterized by high intrinsic dimensionality with extreme events having the form of rare transitions that are several standard deviations away from the mean. Such systems are not amenable to classical order-reduction methods through projection of the governing equations due to the large intrinsic dimensionality of the underlying attractor as well as the complexity of the transient events. Alternatively, data-driven techniques aim to quantify the dynamics of specific, critical modes by utilizing data-streams and by expanding the dimensionality of the reduced-order model using delayed coordinates. In turn, these methods have major limitations in regions of the phase space with sparse data, which is the case for extreme events. In this work, we develop a novel hybrid framework that complements an imperfect reduced order model, with data-streams that are integrated though a recurrent neural network (RNN architecture. The reduced order model has the form of projected equations into a low-dimensional subspace that still contains important dynamical information about the system and it is expanded by a long short-term memory (LSTM regularization. The LSTM-RNN is trained by analyzing the mismatch between the imperfect model and the data-streams, projected to the reduced-order space. The data-driven model assists the imperfect model in regions where data is available, while for locations where data is sparse the imperfect model still provides a baseline for the prediction of the system state. We assess the developed framework on two challenging prototype systems exhibiting extreme events. We show that the blended approach has improved performance compared with methods that use either data streams or the imperfect model alone. Notably the improvement is more

  14. Data-assisted reduced-order modeling of extreme events in complex dynamical systems.

    Science.gov (United States)

    Wan, Zhong Yi; Vlachas, Pantelis; Koumoutsakos, Petros; Sapsis, Themistoklis

    2018-01-01

    The prediction of extreme events, from avalanches and droughts to tsunamis and epidemics, depends on the formulation and analysis of relevant, complex dynamical systems. Such dynamical systems are characterized by high intrinsic dimensionality with extreme events having the form of rare transitions that are several standard deviations away from the mean. Such systems are not amenable to classical order-reduction methods through projection of the governing equations due to the large intrinsic dimensionality of the underlying attractor as well as the complexity of the transient events. Alternatively, data-driven techniques aim to quantify the dynamics of specific, critical modes by utilizing data-streams and by expanding the dimensionality of the reduced-order model using delayed coordinates. In turn, these methods have major limitations in regions of the phase space with sparse data, which is the case for extreme events. In this work, we develop a novel hybrid framework that complements an imperfect reduced order model, with data-streams that are integrated though a recurrent neural network (RNN) architecture. The reduced order model has the form of projected equations into a low-dimensional subspace that still contains important dynamical information about the system and it is expanded by a long short-term memory (LSTM) regularization. The LSTM-RNN is trained by analyzing the mismatch between the imperfect model and the data-streams, projected to the reduced-order space. The data-driven model assists the imperfect model in regions where data is available, while for locations where data is sparse the imperfect model still provides a baseline for the prediction of the system state. We assess the developed framework on two challenging prototype systems exhibiting extreme events. We show that the blended approach has improved performance compared with methods that use either data streams or the imperfect model alone. Notably the improvement is more significant in

  15. 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.

  16. 基于数据驱动的动态 Web 模板技术设计与实现%Design and implementation of data-driven dynamic Web template technology

    Institute of Scientific and Technical Information of China (English)

    刘放; 陈和平

    2017-01-01

    随着互联网产品规模的不断扩大,前端脚本代码规模大、重用性低、维护困难、扩展性差等问题不断涌现。为此,本文分析了目前主流的 Web 模板技术及各自的优缺点,并结合国内浏览器的兼容性,在 Living template 技术思想的基础上,提出一种基于数据驱动的动态 Web 模板技术方案(DWTT),它能有效提高前端开发效率和代码复用性,降低程序扩展和维护的复杂度。%With the continuous expansion of Internet Web products,such problems as large scale,low reusability,difficulty in organizing and maintaining,poor expansibility and so on are emerging in the front-end script codes.So this paper studies the current mainstream Web template technologies and analyzes their merits and demerits.In light of the compatibility of domestic browsers,a data-driven dynamic Web template technology named as DWTT is put forward based on the thoughts of Living template technology.DWTT can improve the front-end development efficiency and code reusability, and reduce the complexity of program extension and maintenance.

  17. 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

  18. Challenges of Data-driven Healthcare Management

    DEFF Research Database (Denmark)

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

    This paper describes the new kind of data-work involved in developing data-driven healthcare based on two cases from Denmark: The first case concerns a governance infrastructure based on Diagnose-Related Groups (DRG), which was introduced in Denmark in the 1990s. The DRG-system links healthcare...... 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....

  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 Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning

    OpenAIRE

    Akkaya, Ilge

    2016-01-01

    Emerging distributed cyber-physical systems (CPSs) integrate a wide range of heterogeneous components that need to be orchestrated in a dynamic environment. While model-based techniques are commonly used in CPS design, they be- come inadequate in capturing the complexity as systems become larger and extremely dynamic. The adaptive nature of the systems makes data-driven approaches highly desirable, if not necessary.Traditionally, data-driven systems utilize large volumes of static data sets t...

  1. Data-driven discovery of Koopman eigenfunctions using deep learning

    Science.gov (United States)

    Lusch, Bethany; Brunton, Steven L.; Kutz, J. Nathan

    2017-11-01

    Koopman operator theory transforms any autonomous non-linear dynamical system into an infinite-dimensional linear system. Since linear systems are well-understood, a mapping of non-linear dynamics to linear dynamics provides a powerful approach to understanding and controlling fluid flows. However, finding the correct change of variables remains an open challenge. We present a strategy to discover an approximate mapping using deep learning. Our neural networks find this change of variables, its inverse, and a finite-dimensional linear dynamical system defined on the new variables. Our method is completely data-driven and only requires measurements of the system, i.e. it does not require derivatives or knowledge of the governing equations. We find a minimal set of approximate Koopman eigenfunctions that are sufficient to reconstruct and advance the system to future states. We demonstrate the method on several dynamical systems.

  2. Constructing Dynamic Event Trees from Markov Models

    International Nuclear Information System (INIS)

    Paolo Bucci; Jason Kirschenbaum; Tunc Aldemir; Curtis Smith; Ted Wood

    2006-01-01

    In the probabilistic risk assessment (PRA) of process plants, Markov models can be used to model accurately the complex dynamic interactions between plant physical process variables (e.g., temperature, pressure, etc.) and the instrumentation and control system that monitors and manages the process. One limitation of this approach that has prevented its use in nuclear power plant PRAs is the difficulty of integrating the results of a Markov analysis into an existing PRA. In this paper, we explore a new approach to the generation of failure scenarios and their compilation into dynamic event trees from a Markov model of the system. These event trees can be integrated into an existing PRA using software tools such as SAPHIRE. To implement our approach, we first construct a discrete-time Markov chain modeling the system of interest by: (a) partitioning the process variable state space into magnitude intervals (cells), (b) using analytical equations or a system simulator to determine the transition probabilities between the cells through the cell-to-cell mapping technique, and, (c) using given failure/repair data for all the components of interest. The Markov transition matrix thus generated can be thought of as a process model describing the stochastic dynamic behavior of the finite-state system. We can therefore search the state space starting from a set of initial states to explore all possible paths to failure (scenarios) with associated probabilities. We can also construct event trees of arbitrary depth by tracing paths from a chosen initiating event and recording the following events while keeping track of the probabilities associated with each branch in the tree. As an example of our approach, we use the simple level control system often used as benchmark in the literature with one process variable (liquid level in a tank), and three control units: a drain unit and two supply units. Each unit includes a separate level sensor to observe the liquid level in the tank

  3. 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.

  4. Dynamical Networks Characterization of Space Weather Events

    Science.gov (United States)

    Orr, L.; Chapman, S. C.; Dods, J.; Gjerloev, J. W.

    2017-12-01

    Space weather can cause disturbances to satellite systems, impacting navigation technology and telecommunications; it can cause power loss and aviation disruption. A central aspect of the earth's magnetospheric response to space weather events are large scale and rapid changes in ionospheric current patterns. Space weather is highly dynamic and there are still many controversies about how the current system evolves in time. The recent SuperMAG initiative, collates ground-based vector magnetic field time series from over 200 magnetometers with 1-minute temporal resolution. In principle this combined dataset is an ideal candidate for quantification using dynamical networks. Network properties and parameters allow us to characterize the time dynamics of the full spatiotemporal pattern of the ionospheric current system. However, applying network methodologies to physical data presents new challenges. We establish whether a given pair of magnetometers are connected in the network by calculating their canonical cross correlation. The magnetometers are connected if their cross correlation exceeds a threshold. In our physical time series this threshold needs to be both station specific, as it varies with (non-linear) individual station sensitivity and location, and able to vary with season, which affects ground conductivity. Additionally, the earth rotates and therefore the ground stations move significantly on the timescales of geomagnetic disturbances. The magnetometers are non-uniformly spatially distributed. We will present new methodology which addresses these problems and in particular achieves dynamic normalization of the physical time series in order to form the network. Correlated disturbances across the magnetometers capture transient currents. Once the dynamical network has been obtained [1][2] from the full magnetometer data set it can be used to directly identify detailed inferred transient ionospheric current patterns and track their dynamics. We will show

  5. 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...

  6. Data-Driven Security-Constrained OPF

    DEFF Research Database (Denmark)

    Thams, Florian; Halilbasic, Lejla; Pinson, Pierre

    2017-01-01

    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......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...... in market clearing algorithms. Our goal is to minimize redispatching actions, and instead allow the market to determine the most cost-efficient dispatch while considering all security constraints. To maintain tractability of our approach we perform our security assessment offline, examining large datasets...

  7. Data-driven sensor placement from coherent fluid structures

    Science.gov (United States)

    Manohar, Krithika; Kaiser, Eurika; Brunton, Bingni W.; Kutz, J. Nathan; Brunton, Steven L.

    2017-11-01

    Optimal sensor placement is a central challenge in the prediction, estimation and control of fluid flows. We reinterpret sensor placement as optimizing discrete samples of coherent fluid structures for full state reconstruction. This permits a drastic reduction in the number of sensors required for faithful reconstruction, since complex fluid interactions can often be described by a small number of coherent structures. Our work optimizes point sensors using the pivoted matrix QR factorization to sample coherent structures directly computed from flow data. We apply this sampling technique in conjunction with various data-driven modal identification methods, including the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). In contrast to POD-based sensors, DMD demonstrably enables the optimization of sensors for prediction in systems exhibiting multiple scales of dynamics. Finally, reconstruction accuracy from pivot sensors is shown to be competitive with sensors obtained using traditional computationally prohibitive optimization methods.

  8. Data driven modelling of vertical atmospheric radiation

    International Nuclear Information System (INIS)

    Antoch, Jaromir; Hlubinka, Daniel

    2011-01-01

    In the Czech Hydrometeorological Institute (CHMI) there exists a unique set of meteorological measurements consisting of the values of vertical atmospheric levels of beta and gamma radiation. In this paper a stochastic data-driven model based on nonlinear regression and on nonhomogeneous Poisson process is suggested. In the first part of the paper, growth curves were used to establish an appropriate nonlinear regression model. For comparison we considered a nonhomogeneous Poisson process with its intensity based on growth curves. In the second part both approaches were applied to the real data and compared. Computational aspects are briefly discussed as well. The primary goal of this paper is to present an improved understanding of the distribution of environmental radiation as obtained from the measurements of the vertical radioactivity profiles by the radioactivity sonde system. - Highlights: → We model vertical atmospheric levels of beta and gamma radiation. → We suggest appropriate nonlinear regression model based on growth curves. → We compare nonlinear regression modelling with Poisson process based modeling. → We apply both models to the real data.

  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. Perceiving Event Dynamics and Parsing Hollywood Films

    Science.gov (United States)

    Cutting, James E.; Brunick, Kaitlin L.; Candan, Ayse

    2012-01-01

    We selected 24 Hollywood movies released from 1940 through 2010 to serve as a film corpus. Eight viewers, three per film, parsed them into events, which are best termed subscenes. While watching a film a second time, viewers scrolled through frames and recorded the frame number where each event began. Viewers agreed about 90% of the time. We then…

  11. Dynamics and predictions in the co-event interpretation

    International Nuclear Information System (INIS)

    Ghazi-Tabatabai, Yousef; Wallden, Petros

    2009-01-01

    Sorkin has introduced a new, observer independent, interpretation of quantum mechanics that can give a successful realist account of the 'quantum micro-world' as well as explaining how classicality emerges at the level of observable events for a range of systems including single time 'Copenhagen measurements'. This 'co-event interpretation' presents us with a new ontology, in which a single 'co-event' is real. A new ontology necessitates a review of the dynamical and predictive mechanism of a theory, and in this paper we begin the process by exploring means of expressing the dynamical and predictive content of histories theories in terms of co-events

  12. Data driven mathematical models for policy making

    OpenAIRE

    Nannyonga, Betty

    2011-01-01

    This thesis consists of two papers. 1. Betty Nannyonga, D.J.T. Sumpter, J.Y.T. Mugisha and L.S. Luboobi: The Dynamics,causes and possible prevention of Hepaititis E outbreaks. 2. Betty Nannyonga, D.J.T. Sumpter, andStam Nicolis: A dynamical systems approach tosocial and economic development. The first paper deals with a deterministic approach of modelling a Hepatitis E outbreak whenmalaria is endemic in a population. We design three models based on the epidemiology ofHepatitis E, malaria, and...

  13. Data-driven architectural design to production and operation

    NARCIS (Netherlands)

    Bier, H.H.; Mostafavi, S.

    2015-01-01

    Data-driven architectural production and operation 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 established bi-directionally so that data-driven architecture is not only produced

  14. Data-Driven Methods to Diversify Knowledge of Human Psychology

    OpenAIRE

    Jack, Rachael E.; Crivelli, Carlos; Wheatley, Thalia

    2017-01-01

    open access article Psychology aims to understand real human behavior. However, cultural biases in the scientific process can constrain knowledge. We describe here how data-driven methods can relax these constraints to reveal new insights that theories can overlook. To advance knowledge we advocate a symbiotic approach that better combines data-driven methods with theory.

  15. Selection of the Sample for Data-Driven $Z \\to \

    CERN Document Server

    Krauss, Martin

    2009-01-01

    The topic of this study was to improve the selection of the sample for data-driven Z → ν ν background estimation, which is a major contribution in supersymmetric searches in ̄ a no-lepton search mode. The data is based on Z → + − samples using data created with ATLAS simulation software. This method works if two leptons are reconstructed, but using cuts that are typical for SUSY searches reconstruction efficiency for electrons and muons is rather low. For this reason it was tried to enhance the data sample. Therefore events were considered, where only one electron was reconstructed. In this case the invariant mass for the electron and each jet was computed to select the jet with the best match for the Z boson mass as not reconstructed electron. This way the sample can be extended but significantly looses purity because of also reconstructed background events. To improve this method other variables have to be considered which were not available for this study. Applying a similar method to muons using ...

  16. 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.

  17. 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...

  18. Observer and data-driven model based fault detection in Power Plant Coal Mills

    DEFF Research Database (Denmark)

    Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

    2008-01-01

    model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault......This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles...

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

    Science.gov (United States)

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

    2018-01-01

    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.

  20. Non-Lipschitz Dynamics Approach to Discrete Event Systems

    Science.gov (United States)

    Zak, M.; Meyers, R.

    1995-01-01

    This paper presents and discusses a mathematical formalism for simulation of discrete event dynamics (DED) - a special type of 'man- made' system designed to aid specific areas of information processing. A main objective is to demonstrate that the mathematical formalism for DED can be based upon the terminal model of Newtonian dynamics which allows one to relax Lipschitz conditions at some discrete points.

  1. Data driven processor 'Vertex Trigger' for B experiments

    International Nuclear Information System (INIS)

    Hartouni, E.P.

    1993-01-01

    Data Driven Processors (DDP's) are specialized computation engines configured to solve specific numerical problems, such as vertex reconstruction. The architecture of the DDP which is the subject of this talk was designed and implemented by W. Sippach and B.C. Knapp at Nevis Lab. in the early 1980's. This particular implementation allows multiple parallel streams of data to provide input to a heterogenous collection of simple operators whose interconnection form an algorithm. The local data flow control allows this device to execute algorithms extremely quickly provided that care is taken in the layout of the algorithm. I/O rates of several hundred megabytes/second are routinely achieved thus making DDP's attractive candidates for complex online calculations. The original question was open-quote can a DDP reconstruct tracks in a Silicon Vertex Detector, find events with a separated vertex and do it fast enough to be used as an online trigger?close-quote Restating this inquiry as three questions and describing the answers to the questions will be the subject of this talk. The three specific questions are: (1) Can an algorithm be found which reconstructs tracks in a planar geometry and no magnetic field; (2) Can separated vertices be recognized in some way; (3) Can the algorithm be implemented in the Nevis-UMass and DDP and execute in 10-20 μs?

  2. Thermodynamically consistent data-driven computational mechanics

    Science.gov (United States)

    González, David; Chinesta, Francisco; Cueto, Elías

    2018-05-01

    In the paradigm of data-intensive science, automated, unsupervised discovering of governing equations for a given physical phenomenon has attracted a lot of attention in several branches of applied sciences. In this work, we propose a method able to avoid the identification of the constitutive equations of complex systems and rather work in a purely numerical manner by employing experimental data. In sharp contrast to most existing techniques, this method does not rely on the assumption on any particular form for the model (other than some fundamental restrictions placed by classical physics such as the second law of thermodynamics, for instance) nor forces the algorithm to find among a predefined set of operators those whose predictions fit best to the available data. Instead, the method is able to identify both the Hamiltonian (conservative) and dissipative parts of the dynamics while satisfying fundamental laws such as energy conservation or positive production of entropy, for instance. The proposed method is tested against some examples of discrete as well as continuum mechanics, whose accurate results demonstrate the validity of the proposed approach.

  3. 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.

  4. Causal relations among events and states in dynamic geographical phenomena

    Science.gov (United States)

    Huang, Zhaoqiang; Feng, Xuezhi; Xuan, Wenling; Chen, Xiuwan

    2007-06-01

    There is only a static state of the real world to be recorded in conventional geographical information systems. However, there is not only static information but also dynamic information in geographical phenomena. So that how to record the dynamic information and reveal the relations among dynamic information is an important issue in a spatio-temporal information system. From an ontological perspective, we can initially divide the spatio-temporal entities in the world into continuants and occurrents. Continuant entities endure through some extended (although possibly very short) interval of time (e.g., houses, roads, cities, and real-estate). Occurrent entities happen and are then gone (e.g., a house repair job, road construction project, urban expansion, real-estate transition). From an information system perspective, continuants and occurrents that have a unique identity in the system are referred to as objects and events, respectively. And the change is represented implicitly by static snapshots in current spatial temporal information systems. In the previous models, the objects can be considered as the fundamental components of the system, and the change is modeled by considering time-varying attributes of these objects. In the spatio-temporal database, the temporal information that is either interval or instant is involved and the underlying data structures and indexes for temporal are considerable investigated. However, there is the absence of explicit ways of considering events, which affect the attributes of objects or the state. So the research issue of this paper focuses on how to model events in conceptual models of dynamic geographical phenomena and how to represent the causal relations among events and the objects or states. Firstly, the paper reviews the conceptual modeling in a temporal GIS by researchers. Secondly, this paper discusses the spatio-temporal entities: objects and events. Thirdly, this paper investigates the causal relations amongst

  5. 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

  6. Data-Driven Exercises for Chemistry: A New Digital Collection

    Science.gov (United States)

    Grubbs, W. Tandy

    2007-01-01

    The analysis presents a new digital collection for various data-driven exercises that are used for teaching chemistry to the students. Such methods are expected to help the students to think in a more scientific manner.

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

    KAUST Repository

    Cui, Tiangang; Youssef, Marzouk; Willcox, Karen

    2014-01-01

    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

  8. Dynamics and predictions in the co-event interpretation

    Energy Technology Data Exchange (ETDEWEB)

    Ghazi-Tabatabai, Yousef [Blackett Laboratory, Imperial College, London, SW7 2AZ (United Kingdom); Wallden, Petros [Raman Research Institute, Bangalore 560 080 (India)

    2009-06-12

    Sorkin has introduced a new, observer independent, interpretation of quantum mechanics that can give a successful realist account of the 'quantum micro-world' as well as explaining how classicality emerges at the level of observable events for a range of systems including single time 'Copenhagen measurements'. This 'co-event interpretation' presents us with a new ontology, in which a single 'co-event' is real. A new ontology necessitates a review of the dynamical and predictive mechanism of a theory, and in this paper we begin the process by exploring means of expressing the dynamical and predictive content of histories theories in terms of co-events.

  9. Dynamic Event Tree advancements and control logic improvements

    International Nuclear Information System (INIS)

    Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Sen, Ramazan Sonat; Cogliati, Joshua Joseph

    2015-01-01

    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

  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. Using Shape Memory Alloys: A Dynamic Data Driven Approach

    KAUST Repository

    Douglas, Craig C.; Calo, Victor M.; Cerwinsky, Derrick; Deng, Li; Efendiev, Yalchin R.

    2013-01-01

    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

  12. Dynamic Data Driven Operator Error Early Warning System

    Science.gov (United States)

    2015-08-13

    data sources , gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this...after each cognitive activity is captured in an android device which doubles as the server for a wireless body area network (WBAN). Thus the operator...MindWave Mobile (NeuroSky, Inc., USA) will be a possible solution. NeuroSky is a non-invasive EEG that connects the user to iOS and Android platforms

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

    Science.gov (United States)

    2015-01-01

    growth in technologies for sensing and computation has contributed to large increases in the volume of data that must be managed and analyzed in many...recognition, speaker identification, pattern recognition) and wireless communication (e.g., GSM, digital radio, NFC , Bluetooth), as well as control...systems for performance and energy consumption. In Proceedings of the IEEE Real-Time Technology and Applications Symposium, pages 124–132, 2003. [49

  14. Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

    Science.gov (United States)

    Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

    2017-11-01

    Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

  15. Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

    Science.gov (United States)

    Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.

    2018-02-01

    The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.

  16. 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.

  17. 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.

  18. 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.

  19. DYNAMIC AUTHORIZATION BASED ON THE HISTORY OF EVENTS

    Directory of Open Access Journals (Sweden)

    Maxim V. Baklanovsky

    2016-11-01

    Full Text Available The new paradigm in the field of access control systems with fuzzy authorization is proposed. Let there is a set of objects in a single data transmissionnetwork. The goal is to develop dynamic authorization protocol based on correctness of presentation of events (news occurred earlier in the network. We propose mathematical method that keeps compactly the history of events, neglects more distant and least-significant events, composes and verifies authorization data. The history of events is represented as vectors of numbers. Each vector is multiplied by several stochastic vectors. The result is known that if vectors of events are sparse, then by solving the problem of -optimization they can be restored with high accuracy. Results of experiments for vectors restoring have shown that the greater the number of stochastic vectors is, the better accuracy of restored vectors is observed. It has been established that the largest absolute components are restored earlier. Access control system with the proposed dynamic authorization method enables to compute fuzzy confidence coefficients in networks with frequently changing set of participants, mesh-networks, multi-agent systems.

  20. 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…

  1. Data-driven Regulation and Governance in Smart Cities

    NARCIS (Netherlands)

    Ranchordás, Sofia; Klop, Abram; Mak, Vanessa; Berlee, Anna; Tjong Tjin Tai, Eric

    2018-01-01

    This chapter discusses the concept of data-driven regulation and governance in the context of smart cities by describing how these urban centres harness these technologies to collect and process information about citizens, traffic, urban planning or waste production. It describes how several smart

  2. 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…

  3. 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.

  4. 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

  5. Scalable data-driven short-term traffic prediction

    NARCIS (Netherlands)

    Friso, K.; Wismans, L. J.J.; Tijink, M. B.

    2017-01-01

    Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models-which do not scale very well to large networks, computationally-or on data-driven methods for freeways, leaving out urban arterials completely. Urban

  6. Data-driven analysis of blood glucose management effectiveness

    NARCIS (Netherlands)

    Nannings, B.; Abu-Hanna, A.; Bosman, R. J.

    2005-01-01

    The blood-glucose-level (BGL) of Intensive Care (IC) patients requires close monitoring and control. In this paper we describe a general data-driven analytical method for studying the effectiveness of BGL management. The method is based on developing and studying a clinical outcome reflecting the

  7. 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…

  8. Knowledge-Driven Versus Data-Driven Logics

    Czech Academy of Sciences Publication Activity Database

    Dubois, D.; Hájek, Petr; Prade, H.

    2000-01-01

    Roč. 9, č. 1 (2000), s. 65-89 ISSN 0925-8531 R&D Projects: GA AV ČR IAA1030601 Grant - others:CNRS(FR) 4008 Institutional research plan: AV0Z1030915 Keywords : epistemic logic * possibility theory * data-driven reasoning * deontic logic Subject RIV: BA - General Mathematics

  9. Developing Annotation Solutions for Online Data Driven Learning

    Science.gov (United States)

    Perez-Paredes, Pascual; Alcaraz-Calero, Jose M.

    2009-01-01

    Although "annotation" is a widely-researched topic in Corpus Linguistics (CL), its potential role in Data Driven Learning (DDL) has not been addressed in depth by Foreign Language Teaching (FLT) practitioners. Furthermore, most of the research in the use of DDL methods pays little attention to annotation in the design and implementation…

  10. Modelling machine ensembles with discrete event dynamical system theory

    Science.gov (United States)

    Hunter, Dan

    1990-01-01

    Discrete Event Dynamical System (DEDS) theory can be utilized as a control strategy for future complex machine ensembles that will be required for in-space construction. The control strategy involves orchestrating a set of interactive submachines to perform a set of tasks for a given set of constraints such as minimum time, minimum energy, or maximum machine utilization. Machine ensembles can be hierarchically modeled as a global model that combines the operations of the individual submachines. These submachines are represented in the global model as local models. Local models, from the perspective of DEDS theory , are described by the following: a set of system and transition states, an event alphabet that portrays actions that takes a submachine from one state to another, an initial system state, a partial function that maps the current state and event alphabet to the next state, and the time required for the event to occur. Each submachine in the machine ensemble is presented by a unique local model. The global model combines the local models such that the local models can operate in parallel under the additional logistic and physical constraints due to submachine interactions. The global model is constructed from the states, events, event functions, and timing requirements of the local models. Supervisory control can be implemented in the global model by various methods such as task scheduling (open-loop control) or implementing a feedback DEDS controller (closed-loop control).

  11. X-ray transmission movies of spontaneous dynamic events

    International Nuclear Information System (INIS)

    Smilowitz, L.; Henson, B. F.; Holmes, M.; Novak, A.; Oschwald, D.; Dolgonos, P.; Qualls, B.

    2014-01-01

    We describe a new x-ray radiographic imaging system which allows for continuous x-ray transmission imaging of spontaneous dynamic events. We demonstrate this method on thermal explosions in three plastic bonded formulations of the energetic material octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine. We describe the x-ray imaging system and triggering developed to enable the continuous imaging of a thermal explosion

  12. Weather models as virtual sensors to data-driven rainfall predictions in urban watersheds

    Science.gov (United States)

    Cozzi, Lorenzo; Galelli, Stefano; Pascal, Samuel Jolivet De Marc; Castelletti, Andrea

    2013-04-01

    Weather and climate predictions are a key element of urban hydrology where they are used to inform water management and assist in flood warning delivering. Indeed, the modelling of the very fast dynamics of urbanized catchments can be substantially improved by the use of weather/rainfall predictions. For example, in Singapore Marina Reservoir catchment runoff processes have a very short time of concentration (roughly one hour) and observational data are thus nearly useless for runoff predictions and weather prediction are required. Unfortunately, radar nowcasting methods do not allow to carrying out long - term weather predictions, whereas numerical models are limited by their coarse spatial scale. Moreover, numerical models are usually poorly reliable because of the fast motion and limited spatial extension of rainfall events. In this study we investigate the combined use of data-driven modelling techniques and weather variables observed/simulated with a numerical model as a way to improve rainfall prediction accuracy and lead time in the Singapore metropolitan area. To explore the feasibility of the approach, we use a Weather Research and Forecast (WRF) model as a virtual sensor network for the input variables (the states of the WRF model) to a machine learning rainfall prediction model. More precisely, we combine an input variable selection method and a non-parametric tree-based model to characterize the empirical relation between the rainfall measured at the catchment level and all possible weather input variables provided by WRF model. We explore different lead time to evaluate the model reliability for different long - term predictions, as well as different time lags to see how past information could improve results. Results show that the proposed approach allow a significant improvement of the prediction accuracy of the WRF model on the Singapore urban area.

  13. 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...

  14. 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 le...... filter, where they improve both accuracy and efficiency of the tracker. In fact, they triple the effective number of samples compared to the most commonly used importance distribution at little extra computational cost....

  15. 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 ...

  16. Satellite-Enhanced Dynamical Downscaling of Extreme Events

    Science.gov (United States)

    Nunes, A.

    2015-12-01

    Severe weather events can be the triggers of environmental disasters in regions particularly susceptible to changes in hydrometeorological conditions. In that regard, the reconstruction of past extreme weather events can help in the assessment of vulnerability and risk mitigation actions. Using novel modeling approaches, dynamical downscaling of long-term integrations from global circulation models can be useful for risk analysis, providing more accurate climate information at regional scales. Originally developed at the National Centers for Environmental Prediction (NCEP), the Regional Spectral Model (RSM) is being used in the dynamical downscaling of global reanalysis, within the South American Hydroclimate Reconstruction Project. Here, RSM combines scale-selective bias correction with assimilation of satellite-based precipitation estimates to downscale extreme weather occurrences. Scale-selective bias correction is a method employed in the downscaling, similar to the spectral nudging technique, in which the downscaled solution develops in agreement with its coarse boundaries. Precipitation assimilation acts on modeled deep-convection, drives the land-surface variables, and therefore the hydrological cycle. During the downscaling of extreme events that took place in Brazil in recent years, RSM continuously assimilated NCEP Climate Prediction Center morphing technique precipitation rates. As a result, RSM performed better than its global (reanalysis) forcing, showing more consistent hydrometeorological fields compared with more sophisticated global reanalyses. Ultimately, RSM analyses might provide better-quality initial conditions for high-resolution numerical predictions in metropolitan areas, leading to more reliable short-term forecasting of severe local storms.

  17. Retrospective data-driven respiratory gating for PET/CT

    International Nuclear Information System (INIS)

    Schleyer, Paul J; O'Doherty, Michael J; Barrington, Sally F; Marsden, Paul K

    2009-01-01

    Respiratory motion can adversely affect both PET and CT acquisitions. Respiratory gating allows an acquisition to be divided into a series of motion-reduced bins according to the respiratory signal, which is typically hardware acquired. In order that the effects of motion can potentially be corrected for, we have developed a novel, automatic, data-driven gating method which retrospectively derives the respiratory signal from the acquired PET and CT data. PET data are acquired in listmode and analysed in sinogram space, and CT data are acquired in cine mode and analysed in image space. Spectral analysis is used to identify regions within the CT and PET data which are subject to respiratory motion, and the variation of counts within these regions is used to estimate the respiratory signal. Amplitude binning is then used to create motion-reduced PET and CT frames. The method was demonstrated with four patient datasets acquired on a 4-slice PET/CT system. To assess the accuracy of the data-derived respiratory signal, a hardware-based signal was acquired for comparison. Data-driven gating was successfully performed on PET and CT datasets for all four patients. Gated images demonstrated respiratory motion throughout the bin sequences for all PET and CT series, and image analysis and direct comparison of the traces derived from the data-driven method with the hardware-acquired traces indicated accurate recovery of the respiratory signal.

  18. Authoring Data-Driven Videos with DataClips.

    Science.gov (United States)

    Amini, Fereshteh; Riche, Nathalie Henry; Lee, Bongshin; Monroy-Hernandez, Andres; Irani, Pourang

    2017-01-01

    Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven "clips" together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.

  19. 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.

  20. 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.

  1. High speed motion neutron radiography of dynamic events

    International Nuclear Information System (INIS)

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

    1983-01-01

    The development of a technique that permits neutron radiographic analysis of dynamic processes over a period lasting from one to ten milliseconds is described. The key to the technique is the use of a neutron pulse broad enough to span the duration of a brief event and intense enough to allow recording of the results on a high-speed movie film at frame rates of 10,000 frames/sec. Some typical application results in ballistic studies and two-phase flow are shown and discussed. The use of scintillator screens in the high-speed motion neutron radiography system is summarized and the statistical limitations of the technique are discussed

  2. Event-by-Event Continuous Respiratory Motion Correction for Dynamic PET Imaging.

    Science.gov (United States)

    Yu, Yunhan; Chan, Chung; Ma, Tianyu; Liu, Yaqiang; Gallezot, Jean-Dominique; Naganawa, Mika; Kelada, Olivia J; Germino, Mary; Sinusas, Albert J; Carson, Richard E; Liu, Chi

    2016-07-01

    Existing respiratory motion-correction methods are applied only to static PET imaging. We have previously developed an event-by-event respiratory motion-correction method with correlations between internal organ motion and external respiratory signals (INTEX). This method is uniquely appropriate for dynamic imaging because it corrects motion for each time point. In this study, we applied INTEX to human dynamic PET studies with various tracers and investigated the impact on kinetic parameter estimation. The use of 3 tracers-a myocardial perfusion tracer, (82)Rb (n = 7); a pancreatic β-cell tracer, (18)F-FP(+)DTBZ (n = 4); and a tumor hypoxia tracer, (18)F-fluoromisonidazole ((18)F-FMISO) (n = 1)-was investigated in a study of 12 human subjects. Both rest and stress studies were performed for (82)Rb. The Anzai belt system was used to record respiratory motion. Three-dimensional internal organ motion in high temporal resolution was calculated by INTEX to guide event-by-event respiratory motion correction of target organs in each dynamic frame. Time-activity curves of regions of interest drawn based on end-expiration PET images were obtained. For (82)Rb studies, K1 was obtained with a 1-tissue model using a left-ventricle input function. Rest-stress myocardial blood flow (MBF) and coronary flow reserve (CFR) were determined. For (18)F-FP(+)DTBZ studies, the total volume of distribution was estimated with arterial input functions using the multilinear analysis 1 method. For the (18)F-FMISO study, the net uptake rate Ki was obtained with a 2-tissue irreversible model using a left-ventricle input function. All parameters were compared with the values derived without motion correction. With INTEX, K1 and MBF increased by 10% ± 12% and 15% ± 19%, respectively, for (82)Rb stress studies. CFR increased by 19% ± 21%. For studies with motion amplitudes greater than 8 mm (n = 3), K1, MBF, and CFR increased by 20% ± 12%, 30% ± 20%, and 34% ± 23%, respectively. For (82)Rb

  3. 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

    , autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality...... with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al., 2000) physiological denoising algorithm, which uses externally...

  4. Discrete event dynamic system (DES)-based modeling for dynamic material flow in the pyroprocess

    International Nuclear Information System (INIS)

    Lee, Hyo Jik; Kim, Kiho; Kim, Ho Dong; Lee, Han Soo

    2011-01-01

    A modeling and simulation methodology was proposed in order to implement the dynamic material flow of the pyroprocess. Since the static mass balance provides the limited information on the material flow, it is hard to predict dynamic behavior according to event. Therefore, a discrete event system (DES)-based model named, PyroFlow, was developed at the Korea Atomic Energy Research Institute (KAERI). PyroFlow is able to calculate dynamic mass balance and also show various dynamic operational results in real time. By using PyroFlow, it is easy to rapidly predict unforeseeable results, such as throughput in unit process, accumulated product in buffer and operation status. As preliminary simulations, bottleneck analyses in the pyroprocess were carried out and consequently it was presented that operation strategy had influence on the productivity of the pyroprocess.

  5. 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.

  6. Parallel Stochastic discrete event simulation of calcium dynamics in neuron.

    Science.gov (United States)

    Ishlam Patoary, Mohammad Nazrul; Tropper, Carl; McDougal, Robert A; Zhongwei, Lin; Lytton, William W

    2017-09-26

    The intra-cellular calcium signaling pathways of a neuron depends on both biochemical reactions and diffusions. Some quasi-isolated compartments (e.g. spines) are so small and calcium concentrations are so low that one extra molecule diffusing in by chance can make a nontrivial difference in its concentration (percentage-wise). These rare events can affect dynamics discretely in such way that they cannot be evaluated by a deterministic simulation. Stochastic models of such a system provide a more detailed understanding of these systems than existing deterministic models because they capture their behavior at a molecular level. Our research focuses on the development of a high performance parallel discrete event simulation environment, Neuron Time Warp (NTW), which is intended for use in the parallel simulation of stochastic reaction-diffusion systems such as intra-calcium signaling. NTW is integrated with NEURON, a simulator which is widely used within the neuroscience community. We simulate two models, a calcium buffer and a calcium wave model. The calcium buffer model is employed in order to verify the correctness and performance of NTW by comparing it to a serial deterministic simulation in NEURON. We also derived a discrete event calcium wave model from a deterministic model using the stochastic IP3R structure.

  7. ALADDIN: a neural model for event classification in dynamic processes

    International Nuclear Information System (INIS)

    Roverso, Davide

    1998-02-01

    ALADDIN is a prototype system which combines fuzzy clustering techniques and artificial neural network (ANN) models in a novel approach to the problem of classifying events in dynamic processes. The main motivation for the development of such a system derived originally from the problem of finding new principled methods to perform alarm structuring/suppression in a nuclear power plant (NPP) alarm system. One such method consists in basing the alarm structuring/suppression on a fast recognition of the event generating the alarms, so that a subset of alarms sufficient to efficiently handle the current fault can be selected to be presented to the operator, minimizing in this way the operator's workload in a potentially stressful situation. The scope of application of a system like ALADDIN goes however beyond alarm handling, to include diagnostic tasks in general. The eventual application of the system to domains other than NPPs was also taken into special consideration during the design phase. In this document we report on the first phase of the ALADDIN project which consisted mainly in a comparative study of a series of ANN-based approaches to event classification, and on the proposal of a first system prototype which is to undergo further tests and, eventually, be integrated in existing alarm, diagnosis, and accident management systems such as CASH, IDS, and CAMS. (author)

  8. General Purpose Data-Driven Monitoring for Space Operations

    Science.gov (United States)

    Iverson, David L.; Martin, Rodney A.; Schwabacher, Mark A.; Spirkovska, Liljana; Taylor, William McCaa; Castle, Joseph P.; Mackey, Ryan M.

    2009-01-01

    As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. 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 or failures. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault

  9. 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....

  10. 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.

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

    OpenAIRE

    Conggang Yu; Lusha Zhu

    2016-01-01

    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 exper...

  12. 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.

  13. Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization.

    Science.gov (United States)

    Gotz, David; Borland, David

    2016-01-01

    The healthcare industry's widespread digitization efforts are reshaping one of the largest sectors of the world's economy. This transformation is enabling systems that promise to use ever-improving data-driven evidence to help doctors make more precise diagnoses, institutions identify at risk patients for intervention, clinicians develop more personalized treatment plans, and researchers better understand medical outcomes within complex patient populations. Given the scale and complexity of the data required to achieve these goals, advanced data visualization tools have the potential to play a critical role. This article reviews a number of visualization challenges unique to the healthcare discipline.

  14. 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.

  15. 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.

  16. 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.

  17. Modeling energy market dynamics using discrete event system simulation

    International Nuclear Information System (INIS)

    Gutierrez-Alcaraz, G.; Sheble, G.B.

    2009-01-01

    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)

  18. Dynamic vegetation modeling of tropical biomes during Heinrich events

    Science.gov (United States)

    Handiani, Dian Noor; Paul, André; Dupont, Lydie M.

    2010-05-01

    Heinrich events are thought to be associated with a slowdown of the Atlantic Meridional Overturning Circulation (AMOC), which in turn would lead to a cooling of the North Atlantic Ocean and a warming of the South Atlantic Ocean (the "bipolar seesaw" hypothesis). The accompanying abrupt climate changes occurred not only in the ocean but also on the continents. Changes were strongest in the Northern Hemisphere but were registered in the tropics as well. Pollen data from Angola and Brazil showed that climate changes during Heinrich events affected vegetation patterns very differently in eastern South America and western Africa. To understand the differential response in the terrestrial tropics, we studied the vegetation changes during Heinrich events by using a dynamic global vegetation model (TRIFFID) as part of the University of Victoria (UVic) Earth System-Climate Model (ESCM). The model results show a bipolar seesaw pattern in temperature and precipitation during a near-collapse of the AMOC. The succession in plant-functional types (PFTs) showed changes from forest to shrubs to desert, including spreading desert in northwest Africa, retreating broadleaf trees in West Africa and northern South America, but advancing broadleaf trees in Brazil. The pattern is explained by a southward shift of the tropical rainbelt resulting in a strong decrease in precipitation over northwest and West Africa as well as in northern South America, but an increase in precipitation in eastern Brazil. To facilitate the comparison between modeled vegetation results with pollen data, we diagnosed the distribution of biomes from the PFT coverage and the simulated model climate. The biome distribution was computed for Heinrich event 1 and the Last Glacial Maximum as well as for pre-industrial conditions. We used a classification of biomes in terms of "mega-biomes", which were defined following a scheme originally proposed by BIOME 6000 (v 4.2). The biome distribution of the Sahel region

  19. 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.

  20. Dynamics of a Puelche foehn event in the Andes

    Directory of Open Access Journals (Sweden)

    Lea Beusch

    2018-01-01

    Full Text Available In this numerical modelling study, we investigate a Puelche foehn event (25–26 March 2014 in the southern Andes – a region with sparse observations. The synoptic environment as well as the mesoscale structure and the dynamics of the easterly wind are examined with European Centre for Medium-Range Weather Forecasts (ECMWF analyses and a simulation with the mesoscale non-hydrostatic limited-area weather prediction model COSMO with a grid spacing of 2.2 km.The large-scale synoptic situation leading to this Puelche event is characterized by a mid-tropospheric cut-off low above the mountain range, the formation of a coastal surface low, as well as high pressure extending over the southern Andes. Easterly winds extend throughout the entire troposphere, indicative of a deep foehn flow. In the free troposphere, the easterlies are geostrophically balanced and develop in association with increasing pressure to the south. In contrast, within the planetary boundary layer, the easterly winds occur predominantly due to an increasing cross-range large-scale pressure gradient with only a weak geostrophic component. Kinematic trajectories indicate that a significant part of the Puelche air mass originates from above an inversion on the upstream side of the Andes. Some air parcels, however, ascend on the upstream side to crest height as the boundary layer deepens during daytime and/or flow through gaps across the mountain range. Hence, this Puelche event shares characteristics of both a blocked and a non-blocked foehn type.

  1. 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.

  2. Data driven approaches for diagnostics and optimization of NPP operation

    International Nuclear Information System (INIS)

    Pliska, J.; Machat, Z.

    2014-01-01

    The efficiency and heat rate is an important indicator of both the health of the power plant equipment and the quality of power plant operation. To achieve this challenges powerful tool is a statistical data processing of large data sets which are stored in data historians. These large data sets contain useful information about process quality and equipment and sensor health. The paper discusses data-driven approaches for model building of main power plant equipment such as condenser, cooling tower and the overall thermal cycle as well using multivariate regression techniques based on so called a regression triplet - data, model and method. Regression models comprise a base for diagnostics and optimization tasks. Diagnostics and optimization tasks are demonstrated on practical cases - diagnostics of main power plant equipment to early identify equipment fault, and optimization task of cooling circuit by cooling water flow control to achieve for a given boundary conditions the highest power output. (authors)

  3. 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...

  4. 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...

  5. 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...... against real data. The performance improvement results in a 22% reduction in the energy consumption. A comparative simulation is accomplished showing the superiority of the method over the existing approaches in terms of the load following performance....

  6. Data-Driven Assistance Functions for Industrial Automation Systems

    International Nuclear Information System (INIS)

    Windmann, Stefan; Niggemann, Oliver

    2015-01-01

    The increasing amount of data in industrial automation systems overburdens the user in process control and diagnosis tasks. One possibility to cope with these challenges consists of using smart assistance systems that automatically monitor and optimize processes. This article deals with aspects of data-driven assistance systems such as assistance functions, process models and data acquisition. The paper describes novel approaches for self-diagnosis and self-optimization, and shows how these assistance functions can be integrated in different industrial environments. The considered assistance functions are based on process models that are automatically learned from process data. Fault detection and isolation is based on the comparison of observations of the real system with predictions obtained by application of the process models. The process models are further employed for energy efficiency optimization of industrial processes. Experimental results are presented for fault detection and energy efficiency optimization of a drive system. (paper)

  7. 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

  8. Objective, Quantitative, Data-Driven Assessment of Chemical Probes.

    Science.gov (United States)

    Antolin, Albert A; Tym, Joseph E; Komianou, Angeliki; Collins, Ian; Workman, Paul; Al-Lazikani, Bissan

    2018-02-15

    Chemical probes are essential tools for understanding biological systems and for target validation, yet selecting probes for biomedical research is rarely based on objective assessment of all potential compounds. Here, we describe the Probe Miner: Chemical Probes Objective Assessment resource, capitalizing on the plethora of public medicinal chemistry data to empower quantitative, objective, data-driven evaluation of chemical probes. We assess >1.8 million compounds for their suitability as chemical tools against 2,220 human targets and dissect the biases and limitations encountered. Probe Miner represents a valuable resource to aid the identification of potential chemical probes, particularly when used alongside expert curation. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. 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

  10. 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

    . The businesses interdisciplinary capabilities come into play in the BMI process, where knowledge from the facilitation strategy and knowledge from phases of the BMI process needs to be present to create new knowledge, hence new BMs and innovations. Depending on the environment and shareholders, this also exposes......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......, 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...

  11. A Transition Towards a Data-Driven Business Model (DDBM)

    DEFF Research Database (Denmark)

    Zaki, Mohamed; Bøe-Lillegraven, Tor; Neely, Andy

    2016-01-01

    Nettavisen is a Norwegian online start-up that experienced a boost after the financial crisis of 2009. Since then, the firm has been able to increase its market share and profitability through the use of highly disruptive business models, allowing the relatively small staff to outcompete powerhouse...... legacy-publishing companies and new media players such as Facebook and Google. These disruptive business models have been successful, as Nettavisen captured a large market share in Norway early on, and was consistently one of the top-three online news sites in Norway. Capitalising on media data explosion...... and the recent acquisition of blogger network ‘Blog.no’, Nettavisen is moving towards a data-driven business model (DDBM). In particular, the firm aims to analyse huge volumes of user Web browsing and purchasing habits....

  12. Helioseismic and neutrino data-driven reconstruction of solar properties

    Science.gov (United States)

    Song, Ningqiang; Gonzalez-Garcia, M. C.; Villante, Francesco L.; Vinyoles, Nuria; Serenelli, Aldo

    2018-06-01

    In this work, we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. In particular, we use a Gaussian process to model the functional shape of the opacity uncertainty to gain flexibility and become as free as possible from prejudice in this regard. With these tools we first readdress the statistical significance of the solar composition problem. Furthermore, starting from a composition unbiased set of standard solar models (SSMs) we are able to statistically select those with solar chemical composition and other solar inputs which better describe the helioseismic and neutrino observations. In particular, we are able to reconstruct the solar opacity profile in a data-driven fashion, independently of any reference opacity tables, obtaining a 4 per cent uncertainty at the base of the convective envelope and 0.8 per cent at the solar core. When systematic uncertainties are included, results are 7.5 per cent and 2 per cent, respectively. In addition, we find that the values of most of the other inputs of the SSMs required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor S11 and the microscopic diffusion rates, for which data suggests a 1 per cent and 30 per cent reduction, respectively. As an output of the study we derive the corresponding data-driven predictions for the solar neutrino fluxes.

  13. 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

    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...

  14. A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

    CERN Document Server

    AUTHOR|(CDS)2092751; Martino, Michele

    The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. Albeit a model may be available; however, such a model may be too complex to consider for an appropriate controller design. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs...

  15. High frame-rate neutron radiography of dynamic events

    International Nuclear Information System (INIS)

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

    1981-01-01

    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 11 n/cm 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

  16. High frame-rate neutron radiography of dynamic events

    International Nuclear Information System (INIS)

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

    1983-01-01

    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 11 n/cm 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. (Auth.)

  17. A Data-Driven Approach to Realistic Shape Morphing

    KAUST Repository

    Gao, Lin; Lai, Yu-Kun; Huang, Qi-Xing; Hu, Shi-Min

    2013-01-01

    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.

  18. Data driven parallelism in experimental high energy physics applications

    International Nuclear Information System (INIS)

    Pohl, M.

    1987-01-01

    I present global design principles for the implementation of high energy physics data analysis code on sequential and parallel processors with mixed shared and local memory. Potential parallelism in the structure of high energy physics tasks is identified with granularity varying from a few times 10 8 instructions all the way down to a few times 10 4 instructions. It follows the hierarchical structure of detector and data acquisition systems. To take advantage of this - yet preserving the necessary portability of the code - I propose a computational model with purely data driven concurrency in Single Program Multiple Data (SPMD) mode. The task granularity is defined by varying the granularity of the central data structure manipulated. Concurrent processes coordiate themselves asynchroneously using simple lock constructs on parts of the data structure. Load balancing among processes occurs naturally. The scheme allows to map the internal layout of the data structure closely onto the layout of local and shared memory in a parallel architecture. It thus allows to optimize the application with respect to synchronization as well as data transport overheads. I present a coarse top level design for a portable implementation of this scheme on sequential machines, multiprocessor mainframes (e.g. IBM 3090), tightly coupled multiprocessors (e.g. RP-3) and loosely coupled processor arrays (e.g. LCAP, Emulating Processor Farms). (orig.)

  19. A data-driven approach to quality risk management.

    Science.gov (United States)

    Alemayehu, Demissie; Alvir, Jose; Levenstein, Marcia; Nickerson, David

    2013-10-01

    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. 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. 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. Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.

  20. 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.

  1. ATLAS job transforms: a data driven workflow engine

    International Nuclear Information System (INIS)

    Stewart, G A; Breaden-Madden, W B; Maddocks, H J; Harenberg, T; Sandhoff, M; Sarrazin, B

    2014-01-01

    The need to run complex workflows for a high energy physics experiment such as ATLAS has always been present. However, as computing resources have become even more constrained, compared to the wealth of data generated by the LHC, the need to use resources efficiently and manage complex workflows within a single grid job have increased. In ATLAS, a new Job Transform framework has been developed that we describe in this paper. This framework manages the multiple execution steps needed to 'transform' one data type into another (e.g., RAW data to ESD to AOD to final ntuple) and also provides a consistent interface for the ATLAS production system. The new framework uses a data driven workflow definition which is both easy to manage and powerful. After a transform is defined, jobs are expressed simply by specifying the input data and the desired output data. The transform infrastructure then executes only the necessary substeps to produce the final data products. The global execution cost of running the job is minimised and the transform can adapt to scenarios where data can be produced along different execution paths. Transforms for specific physics tasks which support up to 60 individual substeps have been successfully run. As the new transforms infrastructure has been deployed in production many features have been added to the framework which improve reliability, quality of error reporting and also provide support for multi-process jobs.

  2. Data-driven classification of patients with primary progressive aphasia.

    Science.gov (United States)

    Hoffman, Paul; Sajjadi, Seyed Ahmad; Patterson, Karalyn; Nestor, Peter J

    2017-11-01

    Current diagnostic criteria classify primary progressive aphasia into three variants-semantic (sv), nonfluent (nfv) and logopenic (lv) PPA-though the adequacy of this scheme is debated. This study took a data-driven approach, applying k-means clustering to data from 43 PPA patients. The algorithm grouped patients based on similarities in language, semantic and non-linguistic cognitive scores. The optimum solution consisted of three groups. One group, almost exclusively those diagnosed as svPPA, displayed a selective semantic impairment. A second cluster, with impairments to speech production, repetition and syntactic processing, contained a majority of patients with nfvPPA but also some lvPPA patients. The final group exhibited more severe deficits to speech, repetition and syntax as well as semantic and other cognitive deficits. These results suggest that, amongst cases of non-semantic PPA, differentiation mainly reflects overall degree of language/cognitive impairment. The observed patterns were scarcely affected by inclusion/exclusion of non-linguistic cognitive scores. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  3. 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.

  4. Data driven parallelism in experimental high energy physics applications

    Science.gov (United States)

    Pohl, Martin

    1987-08-01

    I present global design principles for the implementation of High Energy Physics data analysis code on sequential and parallel processors with mixed shared and local memory. Potential parallelism in the structure of High Energy Physics tasks is identified with granularity varying from a few times 10 8 instructions all the way down to a few times 10 4 instructions. It follows the hierarchical structure of detector and data acquisition systems. To take advantage of this - yet preserving the necessary portability of the code - I propose a computational model with purely data driven concurrency in Single Program Multiple Data (SPMD) mode. The Task granularity is defined by varying the granularity of the central data structure manipulated. Concurrent processes coordinate themselves asynchroneously using simple lock constructs on parts of the data structure. Load balancing among processes occurs naturally. The scheme allows to map the internal layout of the data structure closely onto the layout of local and shared memory in a parallel architecture. It thus allows to optimize the application with respect to synchronization as well as data transport overheads. I present a coarse top level design for a portable implementation of this scheme on sequential machines, multiprocessor mainframes (e.g. IBM 3090), tightly coupled multiprocessors (e.g. RP-3) and loosely coupled processor arrays (e.g. LCAP, Emulating Processor Farms).

  5. Data driven profiting from your most important business asset

    CERN Document Server

    Redman, Thomas C

    2008-01-01

    Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professi...

  6. EXPLORING DATA-DRIVEN SPECTRAL MODELS FOR APOGEE M DWARFS

    Science.gov (United States)

    Lua Birky, Jessica; Hogg, David; Burgasser, Adam J.; Jessica Birky

    2018-01-01

    The Cannon (Ness et al. 2015; Casey et al. 2016) is a flexible, data-driven spectral modeling and parameter inference framework, demonstrated on high-resolution Apache Point Galactic Evolution Experiment (APOGEE; λ/Δλ~22,500, 1.5-1.7µm) spectra of giant stars to estimate stellar labels (Teff, logg, [Fe/H], and chemical abundances) to precisions higher than the model-grid pipeline. The lack of reliable stellar parameters reported by the APOGEE pipeline for temperatures less than ~3550K, motivates extension of this approach to M dwarf stars. Using a training set of 51 M dwarfs with spectral types ranging M0-M9 obtained from SDSS optical spectra, we demonstrate that the Cannon can infer spectral types to a precision of +/-0.6 types, making it an effective tool for classifying high-resolution near-infrared spectra. We discuss the potential for extending this work to determine the physical stellar labels Teff, logg, and [Fe/H].This work is supported by the SDSS Faculty and Student (FAST) initiative.

  7. 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. 

  8. 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.

  9. Data-driven outbreak forecasting with a simple nonlinear growth model.

    Science.gov (United States)

    Lega, Joceline; Brown, Heidi E

    2016-12-01

    Recent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  10. 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...

  11. 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...

  12. Data-Driven Based Asynchronous Motor Control for Printing Servo Systems

    Science.gov (United States)

    Bian, Min; Guo, Qingyun

    Modern digital printing equipment aims to the environmental-friendly industry with high dynamic performances and control precision and low vibration and abrasion. High performance motion control system of printing servo systems was required. Control system of asynchronous motor based on data acquisition was proposed. Iterative learning control (ILC) algorithm was studied. PID control was widely used in the motion control. However, it was sensitive to the disturbances and model parameters variation. The ILC applied the history error data and present control signals to approximate the control signal directly in order to fully track the expect trajectory without the system models and structures. The motor control algorithm based on the ILC and PID was constructed and simulation results were given. The results show that data-driven control method is effective dealing with bounded disturbances for the motion control of printing servo systems.

  13. Data-driven approach for creating synthetic electronic medical records.

    Science.gov (United States)

    Buczak, Anna L; Babin, Steven; Moniz, Linda

    2010-10-14

    New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed. This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population. We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified. A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4

  14. 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.

  15. Data-driven motion correction in brain SPECT

    International Nuclear Information System (INIS)

    Kyme, A.Z.; Hutton, B.F.; Hatton, R.L.; Skerrett, D.W.

    2002-01-01

    Patient motion can cause image artifacts in SPECT despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward-projections. By optimising the orientation of the reconstruction, parameters can be obtained for each misaligned projection and applied to update this volume using a 3D reconstruction algorithm. Digital and physical phantom validation was performed to investigate this approach. Noisy projection data simulating at least one fully 3D patient head movement during acquisition were constructed by projecting the digital Huffman brain phantom at various orientations. Motion correction was applied to the reconstructed studies. The importance of including attenuation effects in the estimation of motion and the need for implementing an iterated correction were assessed in the process. Correction success was assessed visually for artifact reduction, and quantitatively using a mean square difference (MSD) measure. Physical Huffman phantom studies with deliberate movements introduced during the acquisition were also acquired and motion corrected. Effective artifact reduction in the simulated corrupt studies was achieved by motion correction. Typically the MSD ratio between the corrected and reference studies compared to the corrupted and reference studies was > 2. Motion correction could be achieved without inclusion of attenuation effects in the motion estimation stage, providing simpler implementation and greater efficiency. Moreover the additional improvement with multiple iterations of the approach was small. Improvement was also observed in the physical phantom data, though the technique appeared limited here by an object symmetry. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

  16. Architectural Strategies for Enabling Data-Driven Science at Scale

    Science.gov (United States)

    Crichton, D. J.; Law, E. S.; Doyle, R. J.; Little, M. M.

    2017-12-01

    architectural strategies, including a 2015-2016 NASA AIST Study on Big Data, for evolving scientific research towards massively distributed data-driven discovery. It will include example use cases across earth science, planetary science, and other disciplines.

  17. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Leake, James E.; Linton, Mark G. [U.S. Naval Research Laboratory, 4555 Overlook Avenue, SW, Washington, DC 20375 (United States); Schuck, Peter W., E-mail: james.e.leake@nasa.gov [NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771 (United States)

    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.

  19. DEVS representation of dynamical systems - Event-based intelligent control. [Discrete Event System Specification

    Science.gov (United States)

    Zeigler, Bernard P.

    1989-01-01

    It is shown how systems can be advantageously represented as discrete-event models by using DEVS (discrete-event system specification), a set-theoretic formalism. Such DEVS models provide a basis for the design of event-based logic control. In this control paradigm, the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by its DEVS model of the system under control. The event-based contral paradigm is applied in advanced robotic and intelligent automation, showing how classical process control can be readily interfaced with rule-based symbolic reasoning systems.

  20. Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

    Science.gov (United States)

    Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

    2018-05-01

    We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

  1. Data-driven approach for creating synthetic electronic medical records

    Directory of Open Access Journals (Sweden)

    Moniz Linda

    2010-10-01

    Full Text Available Abstract Background New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed. Methods This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia and for background records. The method developed has three major steps: 1 synthetic patient identity and basic information generation; 2 identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3 adaptation of these care patterns to the synthetic patient population. Results We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified. Conclusions A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders. The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious

  2. 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 number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state...

  3. 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

  4. 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

  5. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  6. 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...

  7. 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.

  8. Simple Data-Driven Control for Simulated Bipeds

    NARCIS (Netherlands)

    Geijtenbeek, T.; Pronost, N.G.; van der Stappen, A.F.

    2012-01-01

    We present a framework for controlling physics-based bipeds in a simulated environment, based on a variety of reference motions. Unlike existing methods for control based on reference motions, our framework does not require preprocessing of the reference motion, nor does it rely on inverse dynamics

  9. Data driven model generation based on computational intelligence

    Science.gov (United States)

    Gemmar, Peter; Gronz, Oliver; Faust, Christophe; Casper, Markus

    2010-05-01

    The simulation of discharges at a local gauge or the modeling of large scale river catchments are effectively involved in estimation and decision tasks of hydrological research and practical applications like flood prediction or water resource management. However, modeling such processes using analytical or conceptual approaches is made difficult by both complexity of process relations and heterogeneity of processes. It was shown manifold that unknown or assumed process relations can principally be described by computational methods, and that system models can automatically be derived from observed behavior or measured process data. This study describes the development of hydrological process models using computational methods including Fuzzy logic and artificial neural networks (ANN) in a comprehensive and automated manner. Methods We consider a closed concept for data driven development of hydrological models based on measured (experimental) data. The concept is centered on a Fuzzy system using rules of Takagi-Sugeno-Kang type which formulate the input-output relation in a generic structure like Ri : IFq(t) = lowAND...THENq(t+Δt) = ai0 +ai1q(t)+ai2p(t-Δti1)+ai3p(t+Δti2)+.... The rule's premise part (IF) describes process states involving available process information, e.g. actual outlet q(t) is low where low is one of several Fuzzy sets defined over variable q(t). The rule's conclusion (THEN) estimates expected outlet q(t + Δt) by a linear function over selected system variables, e.g. actual outlet q(t), previous and/or forecasted precipitation p(t ?Δtik). In case of river catchment modeling we use head gauges, tributary and upriver gauges in the conclusion part as well. In addition, we consider temperature and temporal (season) information in the premise part. By creating a set of rules R = {Ri|(i = 1,...,N)} the space of process states can be covered as concise as necessary. Model adaptation is achieved by finding on optimal set A = (aij) of conclusion

  10. 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

  11. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

    Science.gov (United States)

    Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

    2017-01-01

    Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

  12. Using data-driven agent-based models for forecasting emerging infectious diseases

    Directory of Open Access Journals (Sweden)

    Srinivasan Venkatramanan

    2018-03-01

    Full Text Available Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models provide a comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper, we describe one such agent-based model framework developed for forecasting the 2014–2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refined and adapted for future epidemics, and share the lessons learned over the course of the challenge. Keywords: Emerging infectious diseases, Agent-based models, Simulation optimization, Bayesian calibration, Ebola

  13. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

    Science.gov (United States)

    Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

    2017-12-12

    Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

  14. Offline analysis of HEP events by ''dynamic perceptron'' neural network

    International Nuclear Information System (INIS)

    Perrone, A.L.; Basti, G.; Messi, R.; Pasqualucci, E.; Paoluzi, L.

    1997-01-01

    In this paper we start from a critical analysis of the fundamental problems of the parallel calculus in linear structures and of their extension to the partial solutions obtained with non-linear architectures. Then, we present shortly a new dynamic architecture able to solve the limitations of the previous architectures through an automatic re-definition of the topology. This architecture is applied to real-time recognition of particle tracks in high-energy accelerators. (orig.)

  15. 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.

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

    KAUST Repository

    Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

    2016-01-01

    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

  17. 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.

  18. 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

  19. Cosmic and terrestrial single-event radiation effects in dynamic random access memories

    International Nuclear Information System (INIS)

    Massengill, L.W.

    1996-01-01

    A review of the literature on single-event radiation effects (SEE) on MOS integrated-circuit dynamic random access memories (DRAM's) is presented. The sources of single-event (SE) radiation particles, causes of circuit information loss, experimental observations of SE information upset, technological developments for error mitigation, and relationships of developmental trends to SE vulnerability are discussed

  20. Towards Data-Driven Simulations of Wildfire Spread using Ensemble-based Data Assimilation

    Science.gov (United States)

    Rochoux, M. C.; Bart, J.; Ricci, S. M.; Cuenot, B.; Trouvé, A.; Duchaine, F.; Morel, T.

    2012-12-01

    Real-time predictions of a propagating wildfire remain a challenging task because the problem involves both multi-physics and multi-scales. The propagation speed of wildfires, also called the rate of spread (ROS), is indeed determined by complex interactions between pyrolysis, combustion and flow dynamics, atmospheric dynamics occurring at vegetation, topographical and meteorological scales. Current operational fire spread models are mainly based on a semi-empirical parameterization of the ROS in terms of vegetation, topographical and meteorological properties. For the fire spread simulation to be predictive and compatible with operational applications, the uncertainty on the ROS model should be reduced. As recent progress made in remote sensing technology provides new ways to monitor the fire front position, a promising approach to overcome the difficulties found in wildfire spread simulations is to integrate fire modeling and fire sensing technologies using data assimilation (DA). For this purpose we have developed a prototype data-driven wildfire spread simulator in order to provide optimal estimates of poorly known model parameters [*]. The data-driven simulation capability is adapted for more realistic wildfire spread : it considers a regional-scale fire spread model that is informed by observations of the fire front location. An Ensemble Kalman Filter algorithm (EnKF) based on a parallel computing platform (OpenPALM) was implemented in order to perform a multi-parameter sequential estimation where wind magnitude and direction are in addition to vegetation properties (see attached figure). The EnKF algorithm shows its good ability to track a small-scale grassland fire experiment and ensures a good accounting for the sensitivity of the simulation outcomes to the control parameters. As a conclusion, it was shown that data assimilation is a promising approach to more accurately forecast time-varying wildfire spread conditions as new airborne-like observations of

  1. Data-driven discovery of partial differential equations.

    Science.gov (United States)

    Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

    2017-04-01

    We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

  2. The Future of Data-Driven Wound Care.

    Science.gov (United States)

    Woods, Jon S; Saxena, Mayur; Nagamine, Tasha; Howell, Raelina S; Criscitelli, Theresa; Gorenstein, Scott; M Gillette, Brian

    2018-04-01

    Care for patients with chronic wounds can be complex, and the chances of poor outcomes are high if wound care is not optimized through evidence-based protocols. Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound-specific electronic medical records. Harnessing the power of big data analytics to help nurses and physicians provide optimized care based on the care provided to millions of patients can result in better outcomes. Numerous applications of machine learning toward workflow improvements, inpatient monitoring, outpatient communication, and hospital operations can improve overall efficiency and efficacy of care delivery in and out of the hospital, while reducing adverse events and complications. This article provides an overview of the application of big data analytics and machine learning in health care, highlights important recent advances, and discusses how these technologies may revolutionize advanced wound care. © AORN, Inc, 2018.

  3. Asynchronous data-driven classification of weapon systems

    International Nuclear Information System (INIS)

    Jin, Xin; Mukherjee, Kushal; Gupta, Shalabh; Ray, Asok; Phoha, Shashi; Damarla, Thyagaraju

    2009-01-01

    This communication addresses real-time weapon classification by analysis of asynchronous acoustic data, collected from microphones on a sensor network. The weapon classification algorithm consists of two parts: (i) feature extraction from time-series data using symbolic dynamic filtering (SDF), and (ii) pattern classification based on the extracted features using the language measure (LM) and support vector machine (SVM). The proposed algorithm has been tested on field data, generated by firing of two types of rifles. The results of analysis demonstrate high accuracy and fast execution of the pattern classification algorithm with low memory requirements. Potential applications include simultaneous shooter localization and weapon classification with soldier-wearable networked sensors. (rapid communication)

  4. A comparison of different measures for dynamical event mean transverse momentum fluctuation

    International Nuclear Information System (INIS)

    Liu Lianshou; Fu Jinghua

    2004-01-01

    Various measures for the dynamical event mean transverse momentum fluctuation are compared with the real dynamical fluctuation using a Monte Carlo model. The variance calculated from the G-moments can reproduce the dynamical variance well, while those obtained by subtraction procedures are approximate measures for not very low multiplicity. Φ pt , proposed by Gazdzicki M and Mrowczynski S, can also serve as an approximate measure after being divided by the square root of mean multiplicity

  5. Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

    Directory of Open Access Journals (Sweden)

    Jimyung Kang

    2017-10-01

    Full Text Available Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider’s perspective.

  6. 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.

  7. 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

    2017-07-01

    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.

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

    Science.gov (United States)

    Jin, M.; Petrosian, V.; Liu, W.; Nitta, N.; Omodei, N.; Rubio da Costa, F.; Effenberger, F.; Li, G.; Pesce-Rollins, M.

    2017-12-01

    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 site 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.

  9. Event-based scenario manager for multibody dynamics simulation of heavy load lifting operations in shipyards

    Directory of Open Access Journals (Sweden)

    Sol Ha

    2016-01-01

    Full Text Available This paper suggests an event-based scenario manager capable of creating and editing a scenario for shipbuilding process simulation based on multibody dynamics. To configure various situation in shipyards and easily connect with multibody dynamics, the proposed method has two main concepts: an Actor and an Action List. The Actor represents the anatomic unit of action in the multibody dynamics and can be connected to a specific component of the dynamics kernel such as the body and joint. The user can make a scenario up by combining the actors. The Action List contains information for arranging and executing the actors. Since the shipbuilding process is a kind of event-based sequence, all simulation models were configured using Discrete EVent System Specification (DEVS formalism. The proposed method was applied to simulations of various operations in shipyards such as lifting and erection of a block and heavy load lifting operation using multiple cranes.

  10. Data-driven models of dominantly-inherited Alzheimer's disease progression.

    Science.gov (United States)

    Oxtoby, Neil P; Young, Alexandra L; Cash, David M; Benzinger, Tammie L S; Fagan, Anne M; Morris, John C; Bateman, Randall J; Fox, Nick C; Schott, Jonathan M; Alexander, Daniel C

    2018-03-22

    Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. 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.

  16. Data-driven classification of ventilated lung tissues using electrical impedance tomography

    International Nuclear Information System (INIS)

    Gómez-Laberge, Camille; Hogan, Matthew J; Elke, Gunnar; Weiler, Norbert; Frerichs, Inéz; Adler, Andy

    2011-01-01

    Current methods for identifying ventilated lung regions utilizing electrical impedance tomography images rely on dividing the image into arbitrary regions of interest (ROI), manually delineating ROI, or forming ROI with pixels whose signal properties surpass an arbitrary threshold. In this paper, we propose a novel application of a data-driven classification method to identify ventilated lung ROI based on forming k clusters from pixels with correlated signals. A standard first-order model for lung mechanics is then applied to determine which ROI correspond to ventilated lung tissue. We applied the method in an experimental study of 16 mechanically ventilated swine in the supine position, which underwent changes in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (F I O 2 ). In each stage of the experimental protocol, the method performed best with k = 4 and consistently identified 3 lung tissue ROI and 1 boundary tissue ROI in 15 of the 16 subjects. When testing for changes from baseline in lung position, tidal volume, and respiratory system compliance, we found that PEEP displaced the ventilated lung region dorsally by 2 cm, decreased tidal volume by 1.3%, and increased the respiratory system compliance time constant by 0.3 s. F I O 2 decreased tidal volume by 0.7%. All effects were tested at p < 0.05 with n = 16. These findings suggest that the proposed ROI detection method is robust and sensitive to ventilation dynamics in the experimental setting

  17. 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.

  18. A data-driven, mathematical model of mammalian cell cycle regulation.

    Directory of Open Access Journals (Sweden)

    Michael C Weis

    Full Text Available Few of >150 published cell cycle modeling efforts use significant levels of data for tuning and validation. This reflects the difficultly to generate correlated quantitative data, and it points out a critical uncertainty in modeling efforts. To develop a data-driven model of cell cycle regulation, we used contiguous, dynamic measurements over two time scales (minutes and hours calculated from static multiparametric cytometry data. The approach provided expression profiles of cyclin A2, cyclin B1, and phospho-S10-histone H3. The model was built by integrating and modifying two previously published models such that the model outputs for cyclins A and B fit cyclin expression measurements and the activation of B cyclin/Cdk1 coincided with phosphorylation of histone H3. The model depends on Cdh1-regulated cyclin degradation during G1, regulation of B cyclin/Cdk1 activity by cyclin A/Cdk via Wee1, and transcriptional control of the mitotic cyclins that reflects some of the current literature. We introduced autocatalytic transcription of E2F, E2F regulated transcription of cyclin B, Cdc20/Cdh1 mediated E2F degradation, enhanced transcription of mitotic cyclins during late S/early G2 phase, and the sustained synthesis of cyclin B during mitosis. These features produced a model with good correlation between state variable output and real measurements. Since the method of data generation is extensible, this model can be continually modified based on new correlated, quantitative data.

  19. Pengembangan Data Warehouse Menggunakan Pendekatan Data-Driven untuk Membantu Pengelolaan SDM

    Directory of Open Access Journals (Sweden)

    Mujiono Mujiono

    2016-01-01

    Full Text Available The basis of bureaucratic reform is the reform of human resources management. One supporting factor is the development of an employee database. To support the management of human resources required including data warehouse and business intelligent tools. The data warehouse is an integrated concept of reliable data storage to provide support to all the needs of the data analysis. In this study developed a data warehouse using the data-driven approach to the source data comes from SIMPEG, SAPK and electronic presence. Data warehouses are designed using the nine steps methodology and unified modeling language (UML notation. Extract transform load (ETL is done by using Pentaho Data Integration by applying transformation maps. Furthermore, to help human resource management, the system is built to perform online analytical processing (OLAP to facilitate web-based information. In this study generated BI application development framework with Model-View-Controller (MVC architecture and OLAP operations are built using the dynamic query generation, PivotTable, and HighChart to present information about PNS, CPNS, Retirement, Kenpa and Presence

  20. The Orion GN and C Data-Driven Flight Software Architecture for Automated Sequencing and Fault Recovery

    Science.gov (United States)

    King, Ellis; Hart, Jeremy; Odegard, Ryan

    2010-01-01

    The Orion Crew Exploration Vehicle (CET) is being designed to include significantly more automation capability than either the Space Shuttle or the International Space Station (ISS). In particular, the vehicle flight software has requirements to accommodate increasingly automated missions throughout all phases of flight. A data-driven flight software architecture will provide an evolvable automation capability to sequence through Guidance, Navigation & Control (GN&C) flight software modes and configurations while maintaining the required flexibility and human control over the automation. This flexibility is a key aspect needed to address the maturation of operational concepts, to permit ground and crew operators to gain trust in the system and mitigate unpredictability in human spaceflight. To allow for mission flexibility and reconfrgurability, a data driven approach is being taken to load the mission event plan as well cis the flight software artifacts associated with the GN&C subsystem. A database of GN&C level sequencing data is presented which manages and tracks the mission specific and algorithm parameters to provide a capability to schedule GN&C events within mission segments. The flight software data schema for performing automated mission sequencing is presented with a concept of operations for interactions with ground and onboard crew members. A prototype architecture for fault identification, isolation and recovery interactions with the automation software is presented and discussed as a forward work item.

  1. 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,

  2. Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection

    Science.gov (United States)

    Raimalwala, K.; Faragalli, M.; Reid, E.

    2018-04-01

    The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.

  3. 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...

  4. Short-term stream flow forecasting at Australian river sites using data-driven regression techniques

    CSIR Research Space (South Africa)

    Steyn, Melise

    2017-09-01

    Full Text Available This study proposes a computationally efficient solution to stream flow forecasting for river basins where historical time series data are available. Two data-driven modeling techniques are investigated, namely support vector regression...

  5. 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

  6. 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....

  7. 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.

  8. Dynamic ultraslow optical-matter wave analog of an event horizon.

    Science.gov (United States)

    Zhu, C J; Deng, L; Hagley, E W; Ge, Mo-Lin

    2014-08-29

    We investigate theoretically the effects of a dynamically increasing medium index on optical-wave propagation in a rubidium condensate. A long pulsed pump laser coupling a D2 line transition produces a rapidly growing internally generated field. This results in a significant optical self-focusing effect and creates a dynamically growing medium index anomaly that propagates ultraslowly with the internally generated field. When a fast probe pulse injected after a delay catches up with the dynamically increasing index anomaly, it is forced to slow down and is prohibited from crossing the anomaly, thereby realizing an ultraslow optical-matter wave analog of a dynamic white-hole event horizon.

  9. Co-Design of Event Generator and Dynamic Output Feedback Controller for LTI Systems

    Directory of Open Access Journals (Sweden)

    Dan Ma

    2015-01-01

    Full Text Available This paper presents a co-design method of the event generator and the dynamic output feedback controller for a linear time-invariant (LIT system. The event-triggered condition on the sensor-to-controller and the controller-to-actuator depends on the plant output and the controller output, respectively. A sufficient condition on the existence of the event generator and the dynamic output feedback controller is proposed and the co-design problem can be converted into the feasibility of linear matrix inequalities (LMIs. The LTI system is asymptotically stable under the proposed event-triggered controller and also reduces the computing resources with respect to the time-triggered one. In the end, a numerical example is given to illustrate the effectiveness of the proposed approach.

  10. 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 repr...... 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....

  11. 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.

  12. LCP method for a planar passive dynamic walker based on an event-driven scheme

    Science.gov (United States)

    Zheng, Xu-Dong; Wang, Qi

    2018-06-01

    The main purpose of this paper is to present a linear complementarity problem (LCP) method for a planar passive dynamic walker with round feet based on an event-driven scheme. The passive dynamic walker is treated as a planar multi-rigid-body system. The dynamic equations of the passive dynamic walker are obtained by using Lagrange's equations of the second kind. The normal forces and frictional forces acting on the feet of the passive walker are described based on a modified Hertz contact model and Coulomb's law of dry friction. The state transition problem of stick-slip between feet and floor is formulated as an LCP, which is solved with an event-driven scheme. Finally, to validate the methodology, four gaits of the walker are simulated: the stance leg neither slips nor bounces; the stance leg slips without bouncing; the stance leg bounces without slipping; the walker stands after walking several steps.

  13. Access Control with Delegated Authorization Policy Evaluation for Data-Driven Microservice Workflows

    Directory of Open Access Journals (Sweden)

    Davy Preuveneers

    2017-09-01

    Full Text Available Microservices offer a compelling competitive advantage for building data flow systems as a choreography of self-contained data endpoints that each implement a specific data processing functionality. Such a ‘single responsibility principle’ design makes them well suited for constructing scalable and flexible data integration and real-time data flow applications. In this paper, we investigate microservice based data processing workflows from a security point of view, i.e., (1 how to constrain data processing workflows with respect to dynamic authorization policies granting or denying access to certain microservice results depending on the flow of the data; (2 how to let multiple microservices contribute to a collective data-driven authorization decision and (3 how to put adequate measures in place such that the data within each individual microservice is protected against illegitimate access from unauthorized users or other microservices. Due to this multifold objective, enforcing access control on the data endpoints to prevent information leakage or preserve one’s privacy becomes far more challenging, as authorization policies can have dependencies and decision outcomes cross-cutting data in multiple microservices. To address this challenge, we present and evaluate a workflow-oriented authorization framework that enforces authorization policies in a decentralized manner and where the delegated policy evaluation leverages feature toggles that are managed at runtime by software circuit breakers to secure the distributed data processing workflows. The benefit of our solution is that, on the one hand, authorization policies restrict access to the data endpoints of the microservices, and on the other hand, microservices can safely rely on other data endpoints to collectively evaluate cross-cutting access control decisions without having to rely on a shared storage backend holding all the necessary information for the policy evaluation.

  14. Alaska/Yukon Geoid Improvement by a Data-Driven Stokes's Kernel Modification Approach

    Science.gov (United States)

    Li, Xiaopeng; Roman, Daniel R.

    2015-04-01

    Geoid modeling over Alaska of USA and Yukon Canada being a trans-national issue faces a great challenge primarily due to the inhomogeneous surface gravity data (Saleh et al, 2013) and the dynamic geology (Freymueller et al, 2008) as well as its complex geological rheology. Previous study (Roman and Li 2014) used updated satellite models (Bruinsma et al 2013) and newly acquired aerogravity data from the GRAV-D project (Smith 2007) to capture the gravity field changes in the targeting areas primarily in the middle-to-long wavelength. In CONUS, the geoid model was largely improved. However, the precision of the resulted geoid model in Alaska was still in the decimeter level, 19cm at the 32 tide bench marks and 24cm on the 202 GPS/Leveling bench marks that gives a total of 23.8cm at all of these calibrated surface control points, where the datum bias was removed. Conventional kernel modification methods in this area (Li and Wang 2011) had limited effects on improving the precision of the geoid models. To compensate the geoid miss fits, a new Stokes's kernel modification method based on a data-driven technique is presented in this study. First, the method was tested on simulated data sets (Fig. 1), where the geoid errors have been reduced by 2 orders of magnitude (Fig 2). For the real data sets, some iteration steps are required to overcome the rank deficiency problem caused by the limited control data that are irregularly distributed in the target area. For instance, after 3 iterations, the standard deviation dropped about 2.7cm (Fig 3). Modification at other critical degrees can further minimize the geoid model miss fits caused either by the gravity error or the remaining datum error in the control points.

  15. 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 (LEMs) are

  16. 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

  17. Energy-Efficient Fault-Tolerant Dynamic Event Region Detection in Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Enemark, Hans-Jacob; Zhang, Yue; Dragoni, Nicola

    2015-01-01

    to a hybrid algorithm for dynamic event region detection, such as real-time tracking of chemical leakage regions. Considering the characteristics of the moving away dynamic events, we propose a return back condition for the hybrid algorithm from distributed neighborhood collaboration, in which a node makes......Fault-tolerant event detection is fundamental to wireless sensor network applications. Existing approaches usually adopt neighborhood collaboration for better detection accuracy, while need more energy consumption due to communication. Focusing on energy efficiency, this paper makes an improvement...... its detection decision based on decisions received from its spatial and temporal neighbors, to local non-communicative decision making. The simulation results demonstrate that the improved algorithm does not degrade the detection accuracy of the original algorithm, while it has better energy...

  18. 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...

  19. 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.

  20. Automated reasoning with dynamic event trees: a real-time, knowledge-based decision aide

    International Nuclear Information System (INIS)

    Touchton, R.A.; Gunter, A.D.; Subramanyan, N.

    1988-01-01

    The models and data contained in a probabilistic risk assessment (PRA) Event Sequence Analysis represent a wealth of information that can be used for dynamic calculation of event sequence likelihood. In this paper we report a new and unique computerization methodology which utilizes these data. This sub-system (referred to as PREDICTOR) has been developed and tested as part of a larger system. PREDICTOR performs a real-time (re)calculation of the estimated likelihood of core-melt as a function of plant status. This methodology uses object-oriented programming techniques from the artificial intelligence discipline that enable one to codify event tree and fault tree logic models and associated probabilities developed in a PRA study. Existence of off-normal conditions is reported to PREDICTOR, which then updates the relevant failure probabilities throughout the event tree and fault tree models by dynamically replacing the off-the-shelf (or prior) probabilities with new probabilities based on the current situation. The new event probabilities are immediately propagated through the models (using 'demons') and an updated core-melt probability is calculated. Along the way, the dominant non-success path of each event tree is determined and highlighted. (author)

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

    OpenAIRE

    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 ideas, values, and practices associated with the United States in public discourse remained relatively steady over time, which might explain the country’s longevity as a reference culture and its po...

  2. A data-driven emulation framework for representing water-food nexus in a changing cold region

    Science.gov (United States)

    Nazemi, A.; Zandmoghaddam, S.; Hatami, S.

    2017-12-01

    Water resource systems are under increasing pressure globally. Growing population along with competition between water demands and emerging effects of climate change have caused enormous vulnerabilities in water resource management across many regions. Diagnosing such vulnerabilities and provision of effective adaptation strategies requires the availability of simulation tools that can adequately represent the interactions between competing water demands for limiting water resources and inform decision makers about the critical vulnerability thresholds under a range of potential natural and anthropogenic conditions. Despite a significant progress in integrated modeling of water resource systems, regional models are often unable to fully represent the contemplating dynamics within the key elements of water resource systems locally. Here we propose a data-driven approach to emulate a complex regional water resource system model developed for Oldman River Basin in southern Alberta, Canada. The aim of the emulation is to provide a detailed understanding of the trade-offs and interaction at the Oldman Reservoir, which is the key to flood control and irrigated agriculture in this over-allocated semi-arid cold region. Different surrogate models are developed to represent the dynamic of irrigation demand and withdrawal as well as reservoir evaporation and release individually. The nan-falsified offline models are then integrated through the water balance equation at the reservoir location to provide a coupled model for representing the dynamic of reservoir operation and water allocation at the local scale. The performance of individual and integrated models are rigorously examined and sources of uncertainty are highlighted. To demonstrate the practical utility of such surrogate modeling approach, we use the integrated data-driven model for examining the trade-off in irrigation water supply, reservoir storage and release under a range of changing climate, upstream

  3. An Interactive Platform to Visualize Data-Driven Clinical Pathways for the Management of Multiple Chronic Conditions.

    Science.gov (United States)

    Zhang, Yiye; Padman, Rema

    2017-01-01

    Patients with multiple chronic conditions (MCC) pose an increasingly complex health management challenge worldwide, particularly due to the significant gap in our understanding of how to provide coordinated care. Drawing on our prior research on learning data-driven clinical pathways from actual practice data, this paper describes a prototype, interactive platform for visualizing the pathways of MCC to support shared decision making. Created using Python web framework, JavaScript library and our clinical pathway learning algorithm, the visualization platform allows clinicians and patients to learn the dominant patterns of co-progression of multiple clinical events from their own data, and interactively explore and interpret the pathways. We demonstrate functionalities of the platform using a cluster of 36 patients, identified from a dataset of 1,084 patients, who are diagnosed with at least chronic kidney disease, hypertension, and diabetes. Future evaluation studies will explore the use of this platform to better understand and manage MCC.

  4. Ultrafast table-top dynamic radiography of spontaneous or stimulated events

    Science.gov (United States)

    Smilowitz, Laura; Henson, Bryan

    2018-01-16

    Disclosed herein are representative embodiments of methods, apparatus, and systems for performing radiography. For example, certain embodiments concern X-ray radiography of spontaneous events. Particular embodiments of the disclosed technology provide continuous high-speed x-ray imaging of spontaneous dynamic events, such as explosions, reaction-front propagation, and even material failure. Further, in certain embodiments, x-ray activation and data collection activation are triggered by the object itself that is under observation (e.g., triggered by a change of state detected by one or more sensors monitoring the object itself).

  5. 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...

  6. 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…

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

    NARCIS (Netherlands)

    Wink, Alle Meije; Hoogduin, Hans; Roerdink, Jos B.T.M.

    2008-01-01

    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

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

    NARCIS (Netherlands)

    Wink, Alle Meije; Hoogduin, Hans; Roerdink, Jos B.T.M.

    2010-01-01

    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

  9. Perspectives of data-driven LPV modeling of high-purity distillation columns

    NARCIS (Netherlands)

    Bachnas, A.A.; Toth, R.; Mesbah, A.; Ludlage, J.H.A.

    2013-01-01

    Abstract—This paper investigates data-driven, Linear- Parameter-Varying (LPV) modeling of a high-purity distillation column. Two LPV modeling approaches are studied: a local approach, corresponding to the interpolation of Linear Time- Invariant (LTI) models identified at steady-state purity levels,

  10. The Role of Guided Induction in Paper-Based Data-Driven Learning

    Science.gov (United States)

    Smart, Jonathan

    2014-01-01

    This study examines the role of guided induction as an instructional approach in paper-based data-driven learning (DDL) in the context of an ESL grammar course during an intensive English program at an American public university. Specifically, it examines whether corpus-informed grammar instruction is more effective through inductive, data-driven…

  11. Design and evaluation of a data-driven scenario generation framework for game-based training

    NARCIS (Netherlands)

    Luo, L.; Yin, H.; Cai, W.; Zhong, J.; Lees, M.

    Generating suitable game scenarios that can cater for individual players has become an emerging challenge in procedural content generation. In this paper, we propose a data-driven scenario generation framework for game-based training. An evolutionary scenario generation process is designed with a

  12. Data-driven Development of ROTEM and TEG Algorithms for the Management of Trauma Hemorrhage

    DEFF Research Database (Denmark)

    Baksaas-Aasen, Kjersti; Van Dieren, Susan; Balvers, Kirsten

    2018-01-01

    for ROTEM, TEG, and CCTs to be used in addition to ratio driven transfusion and tranexamic acid. CONCLUSIONS: We describe a systematic approach to define threshold parameters for ROTEM and TEG. These parameters were incorporated into algorithms to support data-driven adjustments of resuscitation...

  13. Teacher Talk about Student Ability and Achievement in the Era of Data-Driven Decision Making

    Science.gov (United States)

    Datnow, Amanda; Choi, Bailey; Park, Vicki; St. John, Elise

    2018-01-01

    Background: Data-driven decision making continues to be a common feature of educational reform agendas across the globe. In many U.S. schools, the teacher team meeting is a key setting in which data use is intended to take place, with the aim of planning instruction to address students' needs. However, most prior research has not examined how the…

  14. 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 E

    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. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. 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…

  16. 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...

  17. Writing through Big Data: New Challenges and Possibilities for Data-Driven Arguments

    Science.gov (United States)

    Beveridge, Aaron

    2017-01-01

    As multimodal writing continues to shift and expand in the era of Big Data, writing studies must confront the new challenges and possibilities emerging from data mining, data visualization, and data-driven arguments. Often collected under the broad banner of "data literacy," students' experiences of data visualization and data-driven…

  18. Data-driven directions for effective footwear provision for the high-risk diabetic foot

    NARCIS (Netherlands)

    Arts, M. L. J.; de Haart, M.; Waaijman, R.; Dahmen, R.; Berendsen, H.; Nollet, F.; Bus, S. A.

    2015-01-01

    Custom-made footwear is used to offload the diabetic foot to prevent plantar foot ulcers. This prospective study evaluates the offloading effects of modifying custom-made footwear and aims to provide data-driven directions for the provision of effectively offloading footwear in clinical practice.

  19. Toward Data-Driven Design of Educational Courses: A Feasibility Study

    Science.gov (United States)

    Agrawal, Rakesh; Golshan, Behzad; Papalexakis, Evangelos

    2016-01-01

    A study plan is the choice of concepts and the organization and sequencing of the concepts to be covered in an educational course. While a good study plan is essential for the success of any course offering, the design of study plans currently remains largely a manual task. We present a novel data-driven method, which given a list of concepts can…

  20. Retesting the Limits of Data-Driven Learning: Feedback and Error Correction

    Science.gov (United States)

    Crosthwaite, Peter

    2017-01-01

    An increasing number of studies have looked at the value of corpus-based data-driven learning (DDL) for second language (L2) written error correction, with generally positive results. However, a potential conundrum for language teachers involved in the process is how to provide feedback on students' written production for DDL. The study looks at…

  1. Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach

    Science.gov (United States)

    Wang, Jun; Green, Jordan R.; Samal, Ashok; Yunusova, Yana

    2013-01-01

    Purpose: To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach. Method: Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were

  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. Automated Testing Techniques for Event-Driven and Dynamically Typed Software Applications

    DEFF Research Database (Denmark)

    Adamsen, Christoffer Quist

    techniques to address each of the challenges. We present a new methodology that extends the error detection capabilities of existing, manually written Android test suites. In the context of JavaScript web applications, we present practical race detectors for detecting AJAX and initialization races......, and a technique that can prevent event race errors by restricting the nondeterminism. Finally, we present a notion of test completeness for dynamic languages, along with a hybrid static/dynamic analysis framework that approximates test completeness, and demonstrate the usefulness of test completeness facts...

  4. 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

  5. Action-Derived Molecular Dynamics in the Study of Rare Events

    Energy Technology Data Exchange (ETDEWEB)

    Passerone, Daniele; Parrinello, Michele

    2001-09-03

    We present a practical method to generate classical trajectories with fixed initial and final boundary conditions. Our method is based on the minimization of a suitably defined discretized action. The method finds its most natural application in the study of rare events. Its capabilities are illustrated by nontrivial examples. The algorithm lends itself to straightforward parallelization, and when combined with ab initio molecular dynamics it promises to offer a powerful tool for the study of chemical reactions.

  6. Action-Derived Molecular Dynamics in the Study of Rare Events

    International Nuclear Information System (INIS)

    Passerone, Daniele; Parrinello, Michele

    2001-01-01

    We present a practical method to generate classical trajectories with fixed initial and final boundary conditions. Our method is based on the minimization of a suitably defined discretized action. The method finds its most natural application in the study of rare events. Its capabilities are illustrated by nontrivial examples. The algorithm lends itself to straightforward parallelization, and when combined with ab initio molecular dynamics it promises to offer a powerful tool for the study of chemical reactions

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

    Science.gov (United States)

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

    2016-01-01

    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.

  8. Monte Carlo study for the dynamical fluctuations inside a single jet in 2-jet events

    International Nuclear Information System (INIS)

    Zhang Kunshi; Liu Lianshou; Yin Jianwu; Chen Gang; Liu Chao

    2002-01-01

    The dynamical fluctuations inside a single jet in the 2-jet events produced in e + e - collisions at 91.2 GeV have been studied using Monte Carlo method. The results show that, the anisotropy of dynamical fluctuations inside a single jet changes remarkably with the variation of the cut parameter y cut . A transition point (γ p t = γ ψ ≠γ y ) exists, where the dynamical fluctuations are anisotropic in the longitudinal-transverse plan and isotropic in the transverse planes. It indicates that the y cut corresponding to the transition point is a physically reasonable cutting parameter for selecting jets and, meanwhile, the relative transverse momentum k t at the transition point is the scale for the determination of physical jets. This conclusion is in good agreement with the experimental fact that the third jet (gluon jet) was historically first discovered in the energy region 17-30 GeV in e + e - collisions

  9. Dynamic Data Driven Experiment Control Coordinated with Anisotropic Elastic Material Characterization

    Science.gov (United States)

    John G. Michopoulos; Tomonari Furukawa; John C. Hermanson; Samuel G. Lambrakos

    2011-01-01

    The goal of this paper is to propose and demonstrate a multi level design optimization approach for the coordinated determination of a material constitutive model synchronously to the design of the experimental procedure needed to acquire the necessary data. The methodology achieves both online (real-time) and offline design of optimum experiments required for...

  10. 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.

  11. 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...

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

    KAUST Repository

    Douglas, Craig C.; Efendiev, Yalchin; Popov, Peter; Calo, Victor M.

    2012-01-01

    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

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

    Science.gov (United States)

    2016-09-27

    a. REPORT b. ABSTRACT c. THIS PAGE 17. LIMITATION OF ABSTRACT Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18 Adobe Professional 7.0...in the domain, as further illustrated in [58, 53]. Traveling as fast as possible may not be the best solu- tion, however, for a nonstationary field...Application I: Wake Estimation and Formation Control 3.1 Aerodynamic Model This section illustrates the DDDAS concept using an observability-based sensor

  14. 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.

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

    KAUST Repository

    Douglas, C.; Efendiev, Y.; Ewing, R.; Ginting, V.; Lazarov, R.; Cole, M.; Jones, G.

    2010-01-01

    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

  16. 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.

  17. Probabilistic Dynamics for Integrated Analysis of Accident Sequences considering Uncertain Events

    Directory of Open Access Journals (Sweden)

    Robertas Alzbutas

    2015-01-01

    Full Text Available The analytical/deterministic modelling and simulation/probabilistic methods are used separately as a rule in order to analyse the physical processes and random or uncertain events. However, in the currently used probabilistic safety assessment this is an issue. The lack of treatment of dynamic interactions between the physical processes on one hand and random events on the other hand causes the limited assessment. In general, there are a lot of mathematical modelling theories, which can be used separately or integrated in order to extend possibilities of modelling and analysis. The Theory of Probabilistic Dynamics (TPD and its augmented version based on the concept of stimulus and delay are introduced for the dynamic reliability modelling and the simulation of accidents in hybrid (continuous-discrete systems considering uncertain events. An approach of non-Markovian simulation and uncertainty analysis is discussed in order to adapt the Stimulus-Driven TPD for practical applications. The developed approach and related methods are used as a basis for a test case simulation in view of various methods applications for severe accident scenario simulation and uncertainty analysis. For this and for wider analysis of accident sequences the initial test case specification is then extended and discussed. Finally, it is concluded that enhancing the modelling of stimulated dynamics with uncertainty and sensitivity analysis allows the detailed simulation of complex system characteristics and representation of their uncertainty. The developed approach of accident modelling and analysis can be efficiently used to estimate the reliability of hybrid systems and at the same time to analyze and possibly decrease the uncertainty of this estimate.

  18. Heinrich event 1: an example of dynamical ice-sheet reaction to oceanic changes

    Directory of Open Access Journals (Sweden)

    J. Álvarez-Solas

    2011-11-01

    Full Text Available Heinrich events, identified as enhanced ice-rafted detritus (IRD in North Atlantic deep sea sediments (Heinrich, 1988; Hemming, 2004 have classically been attributed to Laurentide ice-sheet (LIS instabilities (MacAyeal, 1993; Calov et al., 2002; Hulbe et al., 2004 and assumed to lead to important disruptions of the Atlantic meridional overturning circulation (AMOC and North Atlantic deep water (NADW formation. However, recent paleoclimate data have revealed that most of these events probably occurred after the AMOC had already slowed down or/and NADW largely collapsed, within about a thousand years (Hall et al., 2006; Hemming, 2004; Jonkers et al., 2010; Roche et al., 2004, implying that the initial AMOC reduction could not have been caused by the Heinrich events themselves.

    Here we propose an alternative driving mechanism, specifically for Heinrich event 1 (H1; 18 to 15 ka BP, by which North Atlantic ocean circulation changes are found to have strong impacts on LIS dynamics. By combining simulations with a coupled climate model and a three-dimensional ice sheet model, our study illustrates how reduced NADW and AMOC weakening lead to a subsurface warming in the Nordic and Labrador Seas resulting in rapid melting of the Hudson Strait and Labrador ice shelves. Lack of buttressing by the ice shelves implies a substantial ice-stream acceleration, enhanced ice-discharge and sea level rise, with peak values 500–1500 yr after the initial AMOC reduction. Our scenario modifies the previous paradigm of H1 by solving the paradox of its occurrence during a cold surface period, and highlights the importance of taking into account the effects of oceanic circulation on ice-sheets dynamics in order to elucidate the triggering mechanism of Heinrich events.

  19. 3-D Dynamic rupture simulation for the 2016 Kumamoto, Japan, earthquake sequence: Foreshocks and M6 dynamically triggered event

    Science.gov (United States)

    Ando, R.; Aoki, Y.; Uchide, T.; Imanishi, K.; Matsumoto, S.; Nishimura, T.

    2016-12-01

    A couple of interesting earthquake rupture phenomena were observed associated with the sequence of the 2016 Kumamoto, Japan, earthquake sequence. The sequence includes the April 15, 2016, Mw 7.0, mainshock, which was preceded by multiple M6-class foreshock. The mainshock mainly broke the Futagawa fault segment striking NE-SW direction extending over 50km, and it further triggered a M6-class earthquake beyond the distance more than 50km to the northeast (Uchide et al., 2016, submitted), where an active volcano is situated. Compiling the data of seismic analysis and InSAR, we presumed this dynamic triggering event occurred on an active fault known as Yufuin fault (Ando et al., 2016, JPGU general assembly). It is also reported that the coseismic slip was significantly large at a shallow portion of Futagawa Fault near Aso volcano. Since the seismogenic depth becomes significantly shallower in these two areas, we presume the geothermal anomaly play a role as well as the elasto-dynamic processes associated with the coseismic rupture. In this study, we conducted a set of fully dynamic simulations of the earthquake rupture process by assuming the inferred 3D fault geometry and the regional stress field obtained referring the stress tensor inversion. As a result, we showed that the dynamic rupture process was mainly controlled by the irregularity of the fault geometry subjected to the gently varying regional stress field. The foreshocks ruptures have been arrested at the juncture of the branch faults. We also show that the dynamic triggering of M-6 class earthquakes occurred along the Yufuin fault segment (located 50 km NE) because of the strong stress transient up to a few hundreds of kPa due to the rupture directivity effect of the M-7 event. It is also shown that the geothermal condition may lead to the susceptible condition of the dynamic triggering by considering the plastic shear zone on the down dip extension of the Yufuin segment, situated in the vicinity of an

  20. Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

    Directory of Open Access Journals (Sweden)

    Grzegorz Żak

    2017-01-01

    Full Text Available The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, α-stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.

  1. Pipe break prediction based on evolutionary data-driven methods with brief recorded data

    International Nuclear Information System (INIS)

    Xu Qiang; Chen Qiuwen; Li Weifeng; Ma Jinfeng

    2011-01-01

    Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe's probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency.

  2. Data-driven modeling and real-time distributed control for energy efficient manufacturing systems

    International Nuclear Information System (INIS)

    Zou, Jing; Chang, Qing; Arinez, Jorge; Xiao, Guoxian

    2017-01-01

    As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. In this paper, a novel data-driven stochastic manufacturing system modeling method is proposed to identify and predict energy saving opportunities and their impact on production. A real-time distributed feedback production control policy, which integrates the current and predicted system performance, is established to improve the overall profit and energy efficiency. A case study is presented to demonstrate the effectiveness of the proposed control policy. - Highlights: • A data-driven stochastic manufacturing system model is proposed. • Real-time system performance and energy saving opportunity identification method is developed. • Prediction method for future potential system performance and energy saving opportunity is developed. • A real-time distributed feedback control policy is established to improve energy efficiency and overall system profit.

  3. A Data-Driven, Integrated Flare Model Based on Self-Organized Criticality

    Science.gov (United States)

    Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M.

    2013-09-01

    We interpret solar flares as events originating in solar active regions having reached the self-organized critical state, by alternatively using two versions of an "integrated flare model" - one static and one dynamic. In both versions the initial conditions are derived from observations aiming to investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. In the static model, we first apply a nonlinear force-free extrapolation that reconstructs the three-dimensional magnetic fields from two-dimensional vector magnetograms. We then locate magnetic discontinuities exceeding a threshold in the Laplacian of the magnetic field. These discontinuities are relaxed in local diffusion events, implemented in the form of cellular-automaton evolution rules. Subsequent loading and relaxation steps lead the system to self-organized criticality, after which the statistical properties of the simulated events are examined. In the dynamic version we deploy an enhanced driving mechanism, which utilizes the observed evolution of active regions, making use of sequential vector magnetograms. We first apply the static cellular automaton model to consecutive solar vector magnetograms until the self-organized critical state is reached. We then evolve the magnetic field inbetween these processed snapshots through spline interpolation, acting as a natural driver in the dynamic model. The identification of magnetically unstable sites as well as their relaxation follow the same rules as in the static model after each interpolation step. Subsequent interpolation/driving and relaxation steps cover all transitions until the end of the sequence. Physical requirements, such as the divergence-free condition for the magnetic field vector, are approximately satisfied in both versions of the model. We obtain robust power laws in the distribution functions of the modelled

  4. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

    OpenAIRE

    Rui Sun; Qi Cheng; Guanyu Wang; Washington Yotto Ochieng

    2017-01-01

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in ...

  5. Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains

    OpenAIRE

    Sethi, Tegjyot Singh; Kantardzic, Mehmed

    2017-01-01

    While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication of attacks, has prompted the need for a data driven solution, with machine learning forming the core of many cybersecurity systems. Machine learning was not designed with security in mind, and the essential assumption of stationarity, requiri...

  6. 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.

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

    OpenAIRE

    Wenxu Yan; Jing Deng; Dezhi Xu

    2016-01-01

    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 als...

  8. Extension of a data-driven gating technique to 3D, whole body PET studies

    International Nuclear Information System (INIS)

    Schleyer, Paul J; O'Doherty, Michael J; Marsden, Paul K

    2011-01-01

    Respiratory gating can be used to separate a PET acquisition into a series of near motion-free bins. This is typically done using additional gating hardware; however, software-based methods can derive the respiratory signal from the acquired data itself. The aim of this work was to extend a data-driven respiratory gating method to acquire gated, 3D, whole body PET images of clinical patients. The existing method, previously demonstrated with 2D, single bed-position data, uses a spectral analysis to find regions in raw PET data which are subject to respiratory motion. The change in counts over time within these regions is then used to estimate the respiratory signal of the patient. In this work, the gating method was adapted to only accept lines of response from a reduced set of axial angles, and the respiratory frequency derived from the lung bed position was used to help identify the respiratory frequency in all other bed positions. As the respiratory signal does not identify the direction of motion, a registration-based technique was developed to align the direction for all bed positions. Data from 11 clinical FDG PET patients were acquired, and an optical respiratory monitor was used to provide a hardware-based signal for comparison. All data were gated using both the data-driven and hardware methods, and reconstructed. The centre of mass of manually defined regions on gated images was calculated, and the overall displacement was defined as the change in the centre of mass between the first and last gates. The mean displacement was 10.3 mm for the data-driven gated images and 9.1 mm for the hardware gated images. No significant difference was found between the two gating methods when comparing the displacement values. The adapted data-driven gating method was demonstrated to successfully produce respiratory gated, 3D, whole body, clinical PET acquisitions.

  9. A data-driven approach for retrieving temperatures and abundances in brown dwarf atmospheres

    OpenAIRE

    Line, MR; Fortney, JJ; Marley, MS; Sorahana, S

    2014-01-01

    © 2014. The American Astronomical Society. All rights reserved. Brown dwarf spectra contain a wealth of information about their molecular abundances, temperature structure, and gravity. We present a new data driven retrieval approach, previously used in planetary atmosphere studies, to extract the molecular abundances and temperature structure from brown dwarf spectra. The approach makes few a priori physical assumptions about the state of the atmosphere. The feasibility of the approach is fi...

  10. 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.

  11. Data-Driven and Expectation-Driven Discovery of Empirical Laws.

    Science.gov (United States)

    1982-10-10

    occurred in small integer proportions to each other. In 1809, Joseph Gay- Lussac found evidence for his law of combining volumes, which stated that a...of Empirical Laws Patrick W. Langley Gary L. Bradshaw Herbert A. Simon T1he Robotics Institute Carnegie-Mellon University Pittsburgh, Pennsylvania...Subtitle) S. TYPE OF REPORT & PERIOD COVERED Data-Driven and Expectation-Driven Discovery Interim Report 2/82-10/82 of Empirical Laws S. PERFORMING ORG

  12. Multivariate modeling of complications with data driven variable selection: Guarding against overfitting and effects of data set size

    International Nuclear Information System (INIS)

    Schaaf, Arjen van der; Xu Chengjian; Luijk, Peter van; Veld, Aart A. van’t; Langendijk, Johannes A.; Schilstra, Cornelis

    2012-01-01

    Purpose: Multivariate modeling of complications after radiotherapy is frequently used in conjunction with data driven variable selection. This study quantifies the risk of overfitting in a data driven modeling method using bootstrapping for data with typical clinical characteristics, and estimates the minimum amount of data needed to obtain models with relatively high predictive power. Materials and methods: To facilitate repeated modeling and cross-validation with independent datasets for the assessment of true predictive power, a method was developed to generate simulated data with statistical properties similar to real clinical data sets. Characteristics of three clinical data sets from radiotherapy treatment of head and neck cancer patients were used to simulate data with set sizes between 50 and 1000 patients. A logistic regression method using bootstrapping and forward variable selection was used for complication modeling, resulting for each simulated data set in a selected number of variables and an estimated predictive power. The true optimal number of variables and true predictive power were calculated using cross-validation with very large independent data sets. Results: For all simulated data set sizes the number of variables selected by the bootstrapping method was on average close to the true optimal number of variables, but showed considerable spread. Bootstrapping is more accurate in selecting the optimal number of variables than the AIC and BIC alternatives, but this did not translate into a significant difference of the true predictive power. The true predictive power asymptotically converged toward a maximum predictive power for large data sets, and the estimated predictive power converged toward the true predictive power. More than half of the potential predictive power is gained after approximately 200 samples. Our simulations demonstrated severe overfitting (a predicative power lower than that of predicting 50% probability) in a number of small

  13. 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.

  14. 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

    2017-11-01

    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.

  15. Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype

    Science.gov (United States)

    Martin, Rodney A.; Schwabacher, Mark A.; Matthews, Bryan L.

    2010-01-01

    In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.

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

    Directory of Open Access Journals (Sweden)

    Chang-Hee Han

    2016-01-01

    Full Text Available 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.

  17. Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

    Science.gov (United States)

    Kamesh, Reddi; Rani, K Yamuna

    2016-09-01

    A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  18. 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.

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

    Science.gov (United States)

    Han, Chang-Hee; Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan; Im, Chang-Hwan

    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.

  20. Low latitude ionospheric TEC responses to dynamical complexity quantifiers during transient events over Nigeria

    Science.gov (United States)

    Ogunsua, Babalola

    2018-04-01

    In this study, the values of chaoticity and dynamical complexity parameters for some selected storm periods in the year 2011 and 2012 have been computed. This was done using detrended TEC data sets measured from Birnin-Kebbi, Torro and Enugu global positioning system (GPS) receiver stations in Nigeria. It was observed that the significance of difference (SD) values were mostly greater than 1.96 but surprisingly lower than 1.96 in September 29, 2011. The values of the computed SD were also found to be reduced in most cases just after the geomagnetic storm with immediate recovery a day after the main phase of the storm while the values of Lyapunov exponent and Tsallis entropy remains reduced due to the influence of geomagnetic storms. It was also observed that the value of Lyapunov exponent and Tsallis entropy reveals similar variation pattern during storm period in most cases. Also recorded surprisingly were lower values of these dynamical quantifiers during the solar flare event of August 8th and 9th of the year 2011. The possible mechanisms responsible for these observations were further discussed in this work. However, our observations show that the ionospheric effects of some other possible transient events other than geomagnetic storms can also be revealed by the variation of chaoticity and dynamical complexity.

  1. Dynamic EBF1 occupancy directs sequential epigenetic and transcriptional events in B-cell programming.

    Science.gov (United States)

    Li, Rui; Cauchy, Pierre; Ramamoorthy, Senthilkumar; Boller, Sören; Chavez, Lukas; Grosschedl, Rudolf

    2018-01-15

    B-cell fate determination requires the action of transcription factors that operate in a regulatory network to activate B-lineage genes and repress lineage-inappropriate genes. However, the dynamics and hierarchy of events in B-cell programming remain obscure. To uncouple the dynamics of transcription factor expression from functional consequences, we generated induction systems in developmentally arrested Ebf1 -/- pre-pro-B cells to allow precise experimental control of EBF1 expression in the genomic context of progenitor cells. Consistent with the described role of EBF1 as a pioneer transcription factor, we show in a time-resolved analysis that EBF1 occupancy coincides with EBF1 expression and precedes the formation of chromatin accessibility. We observed dynamic patterns of EBF1 target gene expression and sequential up-regulation of transcription factors that expand the regulatory network at the pro-B-cell stage. A continuous EBF1 function was found to be required for Cd79a promoter activity and for the maintenance of an accessible chromatin domain that is permissive for binding of other transcription factors. Notably, transient EBF1 occupancy was detected at lineage-inappropriate genes prior to their silencing in pro-B cells. Thus, persistent and transient functions of EBF1 allow for an ordered sequence of epigenetic and transcriptional events in B-cell programming. © 2018 Li et al.; Published by Cold Spring Harbor Laboratory Press.

  2. 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

  3. Event-scale soil moisture dynamics in open evergreen woodlands of southwest Spain

    Science.gov (United States)

    Lozano-Parra, F. J.; Schnabel, S.; Gómez-Gutiérrez, Á.

    2012-04-01

    Rangelands with a disperse tree cover occupy large areas in the southwestern part of the Iberian Pensinsula and are also found in other parts of the Mediterranean. In these grazed, savannah-like ecosystems water constitutes an important limiting factor for vegetation growth because of the strong summer dry period, being annual potential evapotranspiration nearly twice the annual rainfall amount. Previous studies by other authors have found lower values of soil water content below the tree canopy as compared to the open spaces, covered only by herbaceous vegetation. The differences of soil moisture between tree covered and open areas vary along the year, commonly being highest during autumn, low when water content is close to saturation and the inverse during summer. Our studies indicate that the spatial variation of soil moisture is more complex. The main objective of this study is to analyze soil moisture dynamics at the event scale below tree canopies (Quercus ilex) and in the open spaces. Because soils are commonly very shallow (Cambisols) and a high concentration of grass roots is found in the upper five centimetres, soil moisture measurements were carried out at 5, 10, 15 and 30 cm depth. The study area is located in Extremadura. Soil moisture is measured continuously with a time resolution of 30 minutes using capacitive sensors and rainfall is registered in 5-minute intervals. Data from the hydrological year 2010-11 are presented here. The main factors which produced variations in soil moisture in the upper 5 cm were amount and duration of the rainfall event. Rainfall intensity was also significantly related with an increase of the water content. At greater depth (30 cm) soil moisture was more related with antecedent rainfall, as for example the amount of precipitation registered 30 and 45 days prior to the event. Maximum increases produced by a rainstorm were approximately 0.20 m3m-3 in grasslands and 0.17 m3m-3 below tree canopy. However, in the uppermost

  4. 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.

  5. Discrimination of Dynamic Tactile Contact by Temporally Precise Event Sensing in Spiking Neuromorphic Networks.

    Science.gov (United States)

    Lee, Wang Wei; Kukreja, Sunil L; Thakor, Nitish V

    2017-01-01

    This paper presents a neuromorphic tactile encoding methodology that utilizes a temporally precise event-based representation of sensory signals. We introduce a novel concept where touch signals are characterized as patterns of millisecond precise binary events to denote pressure changes. This approach is amenable to a sparse signal representation and enables the extraction of relevant features from thousands of sensing elements with sub-millisecond temporal precision. We also proposed measures adopted from computational neuroscience to study the information content within the spiking representations of artificial tactile signals. Implemented on a state-of-the-art 4096 element tactile sensor array with 5.2 kHz sampling frequency, we demonstrate the classification of transient impact events while utilizing 20 times less communication bandwidth compared to frame based representations. Spiking sensor responses to a large library of contact conditions were also synthesized using finite element simulations, illustrating an 8-fold improvement in information content and a 4-fold reduction in classification latency when millisecond-precise temporal structures are available. Our research represents a significant advance, demonstrating that a neuromorphic spatiotemporal representation of touch is well suited to rapid identification of critical contact events, making it suitable for dynamic tactile sensing in robotic and prosthetic applications.

  6. External radioactive markers for PET data-driven respiratory gating in positron emission tomography.

    Science.gov (United States)

    Büther, Florian; Ernst, Iris; Hamill, James; Eich, Hans T; Schober, Otmar; Schäfers, Michael; Schäfers, Klaus P

    2013-04-01

    Respiratory gating is an established approach to overcoming respiration-induced image artefacts in PET. Of special interest in this respect are raw PET data-driven gating methods which do not require additional hardware to acquire respiratory signals during the scan. However, these methods rely heavily on the quality of the acquired PET data (statistical properties, data contrast, etc.). We therefore combined external radioactive markers with data-driven respiratory gating in PET/CT. The feasibility and accuracy of this approach was studied for [(18)F]FDG PET/CT imaging in patients with malignant liver and lung lesions. PET data from 30 patients with abdominal or thoracic [(18)F]FDG-positive lesions (primary tumours or metastases) were included in this prospective study. The patients underwent a 10-min list-mode PET scan with a single bed position following a standard clinical whole-body [(18)F]FDG PET/CT scan. During this scan, one to three radioactive point sources (either (22)Na or (18)F, 50-100 kBq) in a dedicated holder were attached the patient's abdomen. The list mode data acquired were retrospectively analysed for respiratory signals using established data-driven gating approaches and additionally by tracking the motion of the point sources in sinogram space. Gated reconstructions were examined qualitatively, in terms of the amount of respiratory displacement and in respect of changes in local image intensity in the gated images. The presence of the external markers did not affect whole-body PET/CT image quality. Tracking of the markers led to characteristic respiratory curves in all patients. Applying these curves for gated reconstructions resulted in images in which motion was well resolved. Quantitatively, the performance of the external marker-based approach was similar to that of the best intrinsic data-driven methods. Overall, the gain in measured tumour uptake from the nongated to the gated images indicating successful removal of respiratory motion

  7. Karst aquifer characterization using geophysical remote sensing of dynamic recharge events

    Science.gov (United States)

    Grapenthin, R.; Bilek, S. L.; Luhmann, A. J.

    2017-12-01

    Geophysical monitoring techniques, long used to make significant advances in a wide range of deeper Earth science disciplines, are now being employed to track surficial processes such as landslide, glacier, and river flow. Karst aquifers are another important hydrologic resource that can benefit from geophysical remote sensing, as this monitoring allows for safe, noninvasive karst conduit measurements. Conduit networks are typically poorly constrained, let alone the processes that occur within them. Geophysical monitoring can also provide a regionally integrated analysis to characterize subsurface architecture and to understand the dynamics of flow and recharge processes in karst aquifers. Geophysical signals are likely produced by several processes during recharge events in karst aquifers. For example, pressure pulses occur when water enters conduits that are full of water, and experiments suggest seismic signals result from this process. Furthermore, increasing water pressure in conduits during recharge events increases the load applied to conduit walls, which deforms the surrounding rock to yield measureable surface displacements. Measureable deformation should also occur with mass loading, with subsidence and rebound signals associated with increases and decreases of water mass stored in the aquifer, respectively. Additionally, geophysical signals will likely arise with turbulent flow and pore pressure change in the rock surrounding conduits. Here we present seismic data collected during a pilot study of controlled and natural recharge events in a karst aquifer system near Bear Spring, near Eyota, MN, USA as well as preliminary model results regarding the processes described above. In addition, we will discuss an upcoming field campaign where we will use seismometers, tiltmeters, and GPS instruments to monitor for recharge-induced responses in a FL, USA karst system with existing cave maps, coupling these geophysical observations with hydrologic and

  8. A study on discrete event dynamic model for nuclear operations of main feed water pump

    International Nuclear Information System (INIS)

    Bae, J. C.; Choi, J. I.

    2000-01-01

    A major objective of the study is to propose a supervisory control algorithm based on the discrete event dynamic system (DEDS) model and apply it to the automation of nuclear operations. The study is motivated by the suitability of the DEDS model for simulation of man-made control action and the potential of the DEDS based supervisory control algorithm for enhanced licensibility, when implemented in nuclear plants, through design transparency due to strong analytic backgrounds. The DEDS model can analytically show the robust stability of the proposed supervisory controller providing design transparency for enhanced licensibility when implemented in nuclear operations

  9. 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.

  10. Impact of unexpected events, shocking news, and rumors on foreign exchange market dynamics

    Science.gov (United States)

    McDonald, Mark; Suleman, Omer; Williams, Stacy; Howison, Sam; Johnson, Neil F.

    2008-04-01

    The dynamical response of a population of interconnected objects, when exposed to external perturbations, is of great interest to physicists working on complex systems. Here we focus on human systems, by analyzing 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. For the unfolding events of 9/11, our results show that there was a gradual collective understanding of what was happening, rather than an immediate realization. More generally, we find that for news items which are not simple economic statements—and hence whose implications for the market are not immediately obvious—there are periods of collective discovery during which opinions seem to vary in a remarkably synchronized way.

  11. Data-driven gating in PET: Influence of respiratory signal noise on motion resolution.

    Science.gov (United States)

    Büther, Florian; Ernst, Iris; Frohwein, Lynn Johann; Pouw, Joost; Schäfers, Klaus Peter; Stegger, Lars

    2018-05-21

    Data-driven gating (DDG) approaches for positron emission tomography (PET) are interesting alternatives to conventional hardware-based gating methods. In DDG, the measured PET data themselves are utilized to calculate a respiratory signal, that is, subsequently used for gating purposes. The success of gating is then highly dependent on the statistical quality of the PET data. In this study, we investigate how this quality determines signal noise and thus motion resolution in clinical PET scans using a center-of-mass-based (COM) DDG approach, specifically with regard to motion management of target structures in future radiotherapy planning applications. PET list mode datasets acquired in one bed position of 19 different radiotherapy patients undergoing pretreatment [ 18 F]FDG PET/CT or [ 18 F]FDG PET/MRI were included into this retrospective study. All scans were performed over a region with organs (myocardium, kidneys) or tumor lesions of high tracer uptake and under free breathing. Aside from the original list mode data, datasets with progressively decreasing PET statistics were generated. From these, COM DDG signals were derived for subsequent amplitude-based gating of the original list mode file. The apparent respiratory shift d from end-expiration to end-inspiration was determined from the gated images and expressed as a function of signal-to-noise ratio SNR of the determined gating signals. This relation was tested against additional 25 [ 18 F]FDG PET/MRI list mode datasets where high-precision MR navigator-like respiratory signals were available as reference signal for respiratory gating of PET data, and data from a dedicated thorax phantom scan. All original 19 high-quality list mode datasets demonstrated the same behavior in terms of motion resolution when reducing the amount of list mode events for DDG signal generation. Ratios and directions of respiratory shifts between end-respiratory gates and the respective nongated image were constant over all

  12. A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference

    KAUST Repository

    Tegner, Jesper; Zenil, Hector; Kiani, Narsis A.; Ball, Gordon; Gomez-Cabrero, David

    2016-01-01

    Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.

  13. A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference

    KAUST Repository

    Tegner, Jesper

    2016-10-04

    Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.

  14. How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern

    Directory of Open Access Journals (Sweden)

    Jean Damascène Mazimpaka

    2017-01-01

    Full Text Available The context in which a moving object moves contributes to the movement pattern observed. Likewise, the movement pattern reflects the properties of the movement context. In particular, big events influence human mobility depending on the dynamics of the events. However, this influence has not been explored to understand big events. In this paper, we propose a methodology for learning about big events from human mobility pattern. The methodology involves extracting and analysing the stopping, approaching, and moving-away interactions between public transportation vehicles and the geographic context. The analysis is carried out at two different temporal granularity levels to discover global and local patterns. The results of evaluating this methodology on bus trajectories demonstrate that it can discover occurrences of big events from mobility patterns, roughly estimate the event start and end time, and reveal the temporal patterns of arrival and departure of event attendees. This knowledge can be usefully applied in transportation and event planning and management.

  15. 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.

  16. Ab initio molecular dynamics simulations of low energy recoil events in MgO

    International Nuclear Information System (INIS)

    Petersen, B. A.; Liu, B.; Weber, W. J.; Oak Ridge National Laboratory; Zhang, Y.; Oak Ridge National Laboratory

    2017-01-01

    In this paper, low-energy recoil events in MgO are studied using ab initio molecular dynamics simulations to reveal the dynamic displacement processes and final defect configurations. Threshold displacement energies, E_d, are obtained for Mg and O along three low-index crystallographic directions, [100], [110], and [111]. The minimum values for E_d are found along the [110] direction consisting of the same element, either Mg or O atoms. Minimum threshold values of 29.5 eV for Mg and 25.5 eV for O, respectively, are suggested from the calculations. For other directions, the threshold energies are considerably higher, 65.5 and 150.0 eV for O along [111] and [100], and 122.5 eV for Mg along both [111] and [100] directions, respectively. These results show that the recoil events in MgO are partial-charge transfer assisted processes where the charge transfer plays an important role. Finally, there is a similar trend found in other oxide materials, where the threshold displacement energy correlates linearly with the peak partial-charge transfer, suggesting this behavior might be universal in ceramic oxides.

  17. 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.

  18. Discrete dynamic event tree modeling and analysis of nuclear power plant crews for safety assessment

    International Nuclear Information System (INIS)

    Mercurio, D.

    2011-01-01

    Current Probabilistic Risk Assessment (PRA) and Human Reliability Analysis (HRA) methodologies model the evolution of accident sequences in Nuclear Power Plants (NPPs) mainly based on Logic Trees. The evolution of these sequences is a result of the interactions between the crew and plant; in current PRA methodologies, simplified models of these complex interactions are used. In this study, the Accident Dynamic Simulator (ADS), a modeling framework based on the Discrete Dynamic Event Tree (DDET), has been used for the simulation of crew-plant interactions during potential accident scenarios in NPPs. In addition, an operator/crew model has been developed to treat the response of the crew to the plant. The 'crew model' is made up of three operators whose behavior is guided by a set of rules-of-behavior (which represents the knowledge and training of the operators) coupled with written and mental procedures. In addition, an approach for addressing the crew timing variability in DDETs has been developed and implemented based on a set of HRA data from a simulator study. Finally, grouping techniques were developed and applied to the analysis of the scenarios generated by the crew-plant simulation. These techniques support the post-simulation analysis by grouping similar accident sequences, identifying the key contributing events, and quantifying the conditional probability of the groups. These techniques are used to characterize the context of the crew actions in order to obtain insights for HRA. The model has been applied for the analysis of a Small Loss Of Coolant Accident (SLOCA) event for a Pressurized Water Reactor (PWR). The simulation results support an improved characterization of the performance conditions or context of operator actions, which can be used in an HRA, in the analysis of the reliability of the actions. By providing information on the evolution of system indications, dynamic of cues, crew timing in performing procedure steps, situation

  19. Dynamics of coral-associated microbiomes during a thermal bleaching event.

    Science.gov (United States)

    Pootakham, Wirulda; Mhuantong, Wuttichai; Putchim, Lalita; Yoocha, Thippawan; Sonthirod, Chutima; Kongkachana, Wasitthee; Sangsrakru, Duangjai; Naktang, Chaiwat; Jomchai, Nukoon; Thongtham, Nalinee; Tangphatsornruang, Sithichoke

    2018-03-23

    Coral-associated microorganisms play an important role in their host fitness and survival. A number of studies have demonstrated connections between thermal tolerance in corals and the type/relative abundance of Symbiodinium they harbor. More recently, the shifts in coral-associated bacterial profiles were also shown to be linked to the patterns of coral heat tolerance. Here, we investigated the dynamics of Porites lutea-associated bacterial and algal communities throughout a natural bleaching event, using full-length 16S rRNA and internal transcribed spacer sequences (ITS) obtained from PacBio circular consensus sequencing. We provided evidence of significant changes in the structure and diversity of coral-associated microbiomes during thermal stress. The balance of the symbiosis shifted from a predominant association between corals and Gammaproteobacteria to a predominance of Alphaproteobacteria and to a lesser extent Betaproteobacteria following the bleaching event. On the contrary, the composition and diversity of Symbiodinium communities remained unaltered throughout the bleaching event. It appears that the switching and/or shuffling of Symbiodinium types may not be the primary mechanism used by P. lutea to cope with increasing seawater temperature. The shifts in the structure and diversity of associated bacterial communities may contribute more to the survival of the coral holobiont under heat stress. © 2018 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

  20. Scaling up liquid state machines to predict over address events from dynamic vision sensors.

    Science.gov (United States)

    Kaiser, Jacques; Stal, Rainer; Subramoney, Anand; Roennau, Arne; Dillmann, Rüdiger

    2017-09-01

    Short-term visual prediction is important both in biology and robotics. It allows us to anticipate upcoming states of the environment and therefore plan more efficiently. In theoretical neuroscience, liquid state machines have been proposed as a biologically inspired method to perform asynchronous prediction without a model. However, they have so far only been demonstrated in simulation or small scale pre-processed camera images. In this paper, we use a liquid state machine to predict over the whole  [Formula: see text]  event stream provided by a real dynamic vision sensor (DVS, or silicon retina). Thanks to the event-based nature of the DVS, the liquid is constantly fed with data when an object is in motion, fully embracing the asynchronicity of spiking neural networks. We propose a smooth continuous representation of the event stream for the short-term visual prediction task. Moreover, compared to previous works (2002 Neural Comput. 2525 282-93 and Burgsteiner H et al 2007 Appl. Intell. 26 99-109), we scale the input dimensionality that the liquid operates on by two order of magnitudes. We also expose the current limits of our method by running experiments in a challenging environment where multiple objects are in motion. This paper is a step towards integrating biologically inspired algorithms derived in theoretical neuroscience to real world robotic setups. We believe that liquid state machines could complement current prediction algorithms used in robotics, especially when dealing with asynchronous sensors.

  1. Pesticide load dynamics during stormwater flow events in Mediterranean coastal streams: Alexander stream case study.

    Science.gov (United States)

    Topaz, Tom; Egozi, Roey; Eshel, Gil; Chefetz, Benny

    2018-06-01

    Cultivated land is a major source of pesticides, which are transported with the runoff water and eroded soil during rainfall events and pollute riverine and estuarine environments. Common ecotoxicological assessments of riverine systems are mainly based on water sampling and analysis of only the dissolved phase, and address a single pesticide's toxicological impact under laboratory conditions. A clear overview of mixtures of pesticides in the adsorbed and dissolved phases is missing, and therefore the full ecotoxicological impact is not fully addressed. The aim of this study was to characterize and quantify pesticide concentrations in both suspended sediment and dissolved phases, to provide a better understanding of pesticide-load dynamics during storm events in coastal streams in a Mediterranean climate. High-resolution sampling campaigns of seven flood events were conducted during two rainy seasons in Alexander stream, Israel. Samples of suspended sediments were separated from the solution and both media were analyzed separately for 250 pesticides. A total of 63 pesticides were detected; 18 and 16 pesticides were found solely in the suspended sediments and solution, respectively. Significant differences were observed among the pesticide groups: only 7% of herbicide, 20% of fungicide and 42% of insecticide load was transported with the suspended sediments. However, in both dissolved and adsorbed phases, a mix of pesticides was found which were graded from "mobile" to "non-mobile" with varied distribution coefficients. Diuron, and tebuconazole were frequently found in large quantities in both phases. Whereas insecticide and fungicide transport is likely governed by application time and method, the governing factor for herbicide load was the magnitude of the stream discharge. The results show a complex dynamic of pesticide load affected by excessive use of pesticides, which should be taken into consideration when designing projects to monitor riverine and estuarine

  2. Differences in N loading affect DOM dynamics during typhoon events in a forested mountainous catchment.

    Science.gov (United States)

    Yeh, Tz-Ching; Liao, Chien-Sen; Chen, Ting-Chien; Shih, Yu-Ting; Huang, Jr-Chuan; Zehetner, Franz; Hein, Thomas

    2018-03-21

    The dissolved organic matter (DOM) and nutrient dynamics in small mountainous rivers (SMRs) strongly depend on hydrologic conditions, and especially on extreme events. Here, we investigated the quantity and quality of DOM and inorganic nutrients during base-flow and typhoon events, in a chronically N-saturated mainstream and low N-loaded tributaries of a forested small mountainous reservoir catchment in Taiwan. Our results suggest that divergent transport mechanisms were triggered in the mainstream vs. tributaries during typhoons. The mainstream DON increased from 3.4 to 34.7% of the TDN pool with a static DOC:NO 3 -N ratio and enhanced DOM freshness, signalling a N-enriched DOM transport. Conversely, DON decreased from 46 to 6% of the TDN pool in the tributaries and was coupled with a rapid increase of the DOC:NO 3 -N ratio and humified DOM signals, suggesting the DON and DOC were passively and simultaneously transported. This study confirmed hydrology and spatial dimensions being the main drivers shaping the composition and concentration of DOM and inorganic nutrients in small mountainous catchments subject to hydrologic extremes. We highlighted that the dominant flow paths largely controlled the N-saturation status and DOM composition within each sub-catchment, the effect of land-use could therefore be obscured. Furthermore, N-saturation status and DOM composition are not only a result of hydrologic dynamics, but potential agents modifying the transport mechanism of solutes export from fluvial systems. We emphasize the importance of viewing elemental dynamics from the perspective of a terrestrial-aquatic continuum; and of taking hydrologic phases and individual catchment characteristics into account in water quality management. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  3. Data-driven criteria to assess fear remission and phenotypic variability of extinction in rats.

    Science.gov (United States)

    Shumake, Jason; Jones, Carolyn; Auchter, Allison; Monfils, Marie-Hélène

    2018-03-19

    Fear conditioning is widely employed to examine the mechanisms that underlie dysregulations of the fear system. Various manipulations are often used following fear acquisition to attenuate fear memories. In rodent studies, freezing is often the main output measure to quantify 'fear'. Here, we developed data-driven criteria for defining a standard benchmark that indicates remission from conditioned fear and for identifying subgroups with differential treatment responses. These analyses will enable a better understanding of individual differences in treatment responding.This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'. © 2018 The Author(s).

  4. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

    OpenAIRE

    Kim, Eun-Kyung; Kim, Hyo-Eun; Han, Kyunghwa; Kang, Bong Joo; Sohn, Yu-Mee; Woo, Ok Hee; Lee, Chan Wha

    2018-01-01

    We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remai...

  5. Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer

    Science.gov (United States)

    Clark, Jeremy; Cooper, Colin S; Mills, Robert; Rayward-Smith, Victor J; de la Iglesia, Beatriz

    2015-01-01

    Background Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the

  6. Classification Systems, their Digitization and Consequences for Data-Driven Decision Making

    DEFF Research Database (Denmark)

    Stein, Mari-Klara; Newell, Sue; Galliers, Robert D.

    2013-01-01

    Classification systems are foundational in many standardized software tools. This digitization of classification systems gives them a new ‘materiality’ that, jointly with the social practices of information producers/consumers, has significant consequences on the representational quality of such ...... and the foundational role of representational quality in understanding the success and consequences of data-driven decision-making.......-narration and meta-narration), and three different information production/consumption situations. We contribute to the relational theorization of representational quality and extend classification systems research by drawing explicit attention to the importance of ‘materialization’ of classification systems...

  7. 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.

  8. A data driven method to measure electron charge mis-identification rate

    CERN Document Server

    Bakhshiansohi, Hamed

    2009-01-01

    Electron charge mis-measurement is an important challenge in analyses which depend on the charge of electron. To estimate the probability of {\\it electron charge mis-measurement} a data driven method is introduced and a good agreement with MC based methods is achieved.\\\\ The third moment of $\\phi$ distribution of hits in electron SuperCluster is studied. The correlation between this variable and the electron charge is also investigated. Using this `new' variable and some other variables the electron charge measurement is improved by two different approaches.

  9. 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.

  10. 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.

  11. 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.

  12. Torque controlled rotary-shear experiments reveal pseudotachilites formation-dynamics and precursor events

    Science.gov (United States)

    Tisato, Nicola; Cordonnier, Benoit; De Siena, Luca; Lavier, Luc; Di Toro, Giulio

    2017-04-01

    Except few cases, rotary shear tests, which are designed to study dynamic friction and strengthening/weakening mechanisms in seismogenic faults, are performed by imposing, to the specimens, a slipping velocity that is pre-defined. This approach has been adopted from engineering that typically, tests man-made objects that, when functioning, spin or slide at a pre-defined velocity under a pre-defined load. On the other hand, natural earthquakes are the effect of a rupture that nucleates, propagates and arrests in the subsurface. These three phases, and the consequent emerging fault slipping velocity, are controlled by the accumulated and released energy around the seismogenic fault before, during and after the earthquake. Thus, imposing the slipping velocity in laboratory experiments might not represent the best option to uncover many aspects of earthquake nucleation and fault slipping dynamics. Here we present some experiments performed with an innovative rotary shear apparatus that uses a clock-spring that when winded provides to the rotating sample a linearly increasing torque. Thus, the nucleation of simulated events occur spontaneously when the shear stress on the slipping surface overcomes the static friction times the normal load that is controlled by a deadweight. In addition, this method allows studying precursory seismic events resembling natural slow-slip earthquakes. We report some preliminary results for a transparent polymer that has melting point 340 K and allows observing the slipping surface (i.e., the contact between the two samples). By coupling: i) the rotary shear apparatus, ii) a video camera recording at 60 fps and a iii) laser pointer we observed the formation and evolution of a melt film that forms in the slipping surface after a phase of "dry" stick-slip. After each seismic event the melt layer solidify forming a pseudotachilite that partially welds the slipping surfaces. We also present the mechanical data that show rupture strengthening in

  13. 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.

  14. A Data-driven Concept Schema for Defining Clinical Research Data Needs

    Science.gov (United States)

    Hruby, Gregory W.; Hoxha, Julia; Ravichandran, Praveen Chandar; Mendonça, Eneida A.; Hanauer, David A; Weng, Chunhua

    2016-01-01

    OBJECTIVES The Patient, Intervention, Control/Comparison, and Outcome (PICO) framework is an effective technique for framing a clinical question. We aim to develop the counterpart of PICO to structure clinical research data needs. METHODS We use a data-driven approach to abstracting key concepts representing clinical research data needs by adapting and extending an expert-derived framework originally developed for defining cancer research data needs. We annotated clinical trial eligibility criteria, EHR data request logs, and data queries to electronic health records (EHR), to extract and harmonize concept classes representing clinical research data needs. We evaluated the class coverage, class preservation from the original framework, schema generalizability, schema understandability, and schema structural correctness through a semi-structured interview with eight multidisciplinary domain experts. We iteratively refined the schema based on the evaluations. RESULTS Our data-driven schema preserved 68% of the 63 classes from the original framework and covered 88% (73/82) of the classes proposed by evaluators. Class coverage for participants of different backgrounds ranged from 60% to 100% with a median value of 95% agreement among the individual evaluators. The schema was found understandable and structurally sound. CONCLUSIONS Our proposed schema may serve as the counterpart to PICO for improving the research data needs communication between researchers and informaticians. PMID:27185504

  15. A copula-based sampling method for data-driven prognostics

    International Nuclear Information System (INIS)

    Xi, Zhimin; Jing, Rong; Wang, Pingfeng; Hu, Chao

    2014-01-01

    This paper develops a Copula-based sampling method for data-driven prognostics. The method essentially consists of an offline training process and an online prediction process: (i) the offline training process builds a statistical relationship between the failure time and the time realizations at specified degradation levels on the basis of off-line training data sets; and (ii) the online prediction process identifies probable failure times for online testing units based on the statistical model constructed in the offline process and the online testing data. Our contributions in this paper are three-fold, namely the definition of a generic health index system to quantify the health degradation of an engineering system, the construction of a Copula-based statistical model to learn the statistical relationship between the failure time and the time realizations at specified degradation levels, and the development of a simulation-based approach for the prediction of remaining useful life (RUL). Two engineering case studies, namely the electric cooling fan health prognostics and the 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology. - Highlights: • We develop a novel mechanism for data-driven prognostics. • A generic health index system quantifies health degradation of engineering systems. • Off-line training model is constructed based on the Bayesian Copula model. • Remaining useful life is predicted from a simulation-based approach

  16. 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.

  17. 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.

  18. 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.

  19. Data-driven integration of genome-scale regulatory and metabolic network models

    Science.gov (United States)

    Imam, Saheed; Schäuble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

    2015-01-01

    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. PMID:25999934

  20. 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...

  1. 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.

  2. Data-driven CT protocol review and management—experience from a large academic hospital.

    Science.gov (United States)

    Zhang, Da; Savage, Cristy A; Li, Xinhua; Liu, Bob

    2015-03-01

    Protocol review plays a critical role in CT quality assurance, but large numbers of protocols and inconsistent protocol names on scanners and in exam records make thorough protocol review formidable. In this investigation, we report on a data-driven cataloging process that can be used to assist in the reviewing and management of CT protocols. We collected lists of scanner protocols, as well as 18 months of recent exam records, for 10 clinical scanners. We developed computer algorithms to automatically deconstruct the protocol names on the scanner and in the exam records into core names and descriptive components. Based on the core names, we were able to group the scanner protocols into a much smaller set of "core protocols," and to easily link exam records with the scanner protocols. We calculated the percentage of usage for each core protocol, from which the most heavily used protocols were identified. From the percentage-of-usage data, we found that, on average, 18, 33, and 49 core protocols per scanner covered 80%, 90%, and 95%, respectively, of all exams. These numbers are one order of magnitude smaller than the typical numbers of protocols that are loaded on a scanner (200-300, as reported in the literature). Duplicated, outdated, and rarely used protocols on the scanners were easily pinpointed in the cataloging process. The data-driven cataloging process can facilitate the task of protocol review. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  3. Data-driven approach for assessing utility of medical tests using electronic medical records.

    Science.gov (United States)

    Skrøvseth, Stein Olav; Augestad, Knut Magne; Ebadollahi, Shahram

    2015-02-01

    To precisely define the utility of tests in a clinical pathway through data-driven analysis of the electronic medical record (EMR). The information content was defined in terms of the entropy of the expected value of the test related to a given outcome. A kernel density classifier was used to estimate the necessary distributions. To validate the method, we used data from the EMR of the gastrointestinal department at a university hospital. Blood tests from patients undergoing surgery for gastrointestinal surgery were analyzed with respect to second surgery within 30 days of the index surgery. The information content is clearly reflected in the patient pathway for certain combinations of tests and outcomes. C-reactive protein tests coupled to anastomosis leakage, a severe complication show a clear pattern of information gain through the patient trajectory, where the greatest gain from the test is 3-4 days post index surgery. We have defined the information content in a data-driven and information theoretic way such that the utility of a test can be precisely defined. The results reflect clinical knowledge. In the case we used the tests carry little negative impact. The general approach can be expanded to cases that carry a substantial negative impact, such as in certain radiological techniques. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  4. Data-driven directions for effective footwear provision for the high-risk diabetic foot.

    Science.gov (United States)

    Arts, M L J; de Haart, M; Waaijman, R; Dahmen, R; Berendsen, H; Nollet, F; Bus, S A

    2015-06-01

    Custom-made footwear is used to offload the diabetic foot to prevent plantar foot ulcers. This prospective study evaluates the offloading effects of modifying custom-made footwear and aims to provide data-driven directions for the provision of effectively offloading footwear in clinical practice. Eighty-five people with diabetic neuropathy and a recently healed plantar foot ulcer, who participated in a clinical trial on footwear effectiveness, had their custom-made footwear evaluated with in-shoe plantar pressure measurements at three-monthly intervals. Footwear was modified when peak pressure was ≥ 200 kPa. The effect of single and combined footwear modifications on in-shoe peak pressure at these high-pressure target locations was assessed. All footwear modifications significantly reduced peak pressure at the target locations compared with pre-modification levels (range -6.7% to -24.0%, P diabetic neuropathy and a recently healed plantar foot ulcer, significant offloading can be achieved at high-risk foot regions by modifying custom-made footwear. These results provide data-driven directions for the design and evaluation of custom-made footwear for high-risk people with diabetes, and essentially mean that each shoe prescribed should incorporate those design features that effectively offload the foot. © 2015 The Authors. Diabetic Medicine © 2015 Diabetes UK.

  5. 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 body insensitivity and disability requiring them to be immobile in seats for prolonged periods. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

  6. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

    International Nuclear Information System (INIS)

    Hu Chao; Youn, Byeng D.; Wang Pingfeng; Taek Yoon, Joung

    2012-01-01

    Prognostics aims at determining whether a failure of an engineered system (e.g., a nuclear power plant) is impending and estimating the remaining useful life (RUL) before the failure occurs. The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust; (ii) it wastes the resources for constructing the algorithms that are discarded; (iii) it requires the testing data in addition to the training data. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.

  7. Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

    International Nuclear Information System (INIS)

    An, Dawn; Kim, Nam H.; Choi, Joo-Ho

    2015-01-01

    This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available. - Highlights: • Practical review of data-driven and physics-based prognostics are provided. • As common prognostics algorithms, NN, GP, PF and BM are introduced. • Algorithms’ attributes, pros and cons, and applicable conditions are discussed. • This will be helpful to choose the best algorithm for different applications

  8. 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.

  9. HOMOLOGOUS HELICAL JETS: OBSERVATIONS BY IRIS, SDO, AND HINODE AND MAGNETIC MODELING WITH DATA-DRIVEN SIMULATIONS

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, Mark C. M.; Pontieu, B. De; Tarbell, T. D.; Fu, Y.; Martínez-Sykora, J.; Boerner, P.; Wülser, J. P.; Lemen, J.; Title, A. M.; Hurlburt, N. [Lockheed Martin Solar and Astrophysics Laboratory, 3251 Hanover Street Bldg. 252, Palo Alto, CA 94304 (United States); Tian, H.; Testa, P.; Reeves, K. K.; Golub, L.; McKillop, S.; Saar, S. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Kleint, L. [University of Applied Sciences and Arts Northwestern Switzerland, Bahnhofstr. 6, 5210 Windisch (Switzerland); Kankelborg, C.; Jaeggli, S. [Department of Physics, Montana State University, Bozeman, P.O. Box 173840, Bozeman, MT 59717 (United States); Carlsson, M., E-mail: cheung@lmsal.com [Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029, Blindern, NO-0315 Oslo (Norway); and others

    2015-03-10

    We report on observations of recurrent jets by instruments on board the Interface Region Imaging Spectrograph, Solar Dynamics Observatory (SDO), and Hinode spacecraft. Over a 4 hr period on 2013 July 21, recurrent coronal jets were observed to emanate from NOAA Active Region 11793. Far-ultraviolet spectra probing plasma at transition region temperatures show evidence of oppositely directed flows with components reaching Doppler velocities of ±100 km s{sup −1}. 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 the jets. These numerical experiments show that the emergence of current-carrying magnetic field in the vicinity of the pore supplies the magnetic twist needed for recurrent helical jet formation.

  10. 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.

  11. An optimal baseline selection methodology for data-driven damage detection and temperature compensation in acousto-ultrasonics

    International Nuclear Information System (INIS)

    Torres-Arredondo, M-A; Sierra-Pérez, Julián; Cabanes, Guénaël

    2016-01-01

    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. (paper)

  12. 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.

  13. 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.

  14. Comparing the temporal dynamics of thematic and taxonomic processing using event-related potentials.

    Directory of Open Access Journals (Sweden)

    Olivera Savic

    Full Text Available We report the results of a study comparing the temporal dynamics of thematic and taxonomic knowledge activation in a picture-word priming paradigm using event-related potentials. Although we found no behavioral differences between thematic and taxonomic processing, ERP data revealed distinct patterns of N400 and P600 amplitude modulation for thematic and taxonomic priming. Thematically related target stimuli elicited less negativity than taxonomic targets between 280-460 ms after stimulus onset, suggesting easier semantic processing of thematic than taxonomic relationships. Moreover, P600 mean amplitude was significantly increased for taxonomic targets between 520-600 ms, consistent with a greater need for stimulus reevaluation in that condition. These results offer novel evidence in favor of a dissociation between thematic and taxonomic thinking in the early phases of conceptual evaluation.

  15. Comparing the temporal dynamics of thematic and taxonomic processing using event-related potentials.

    Science.gov (United States)

    Savic, Olivera; Savic, Andrej M; Kovic, Vanja

    2017-01-01

    We report the results of a study comparing the temporal dynamics of thematic and taxonomic knowledge activation in a picture-word priming paradigm using event-related potentials. Although we found no behavioral differences between thematic and taxonomic processing, ERP data revealed distinct patterns of N400 and P600 amplitude modulation for thematic and taxonomic priming. Thematically related target stimuli elicited less negativity than taxonomic targets between 280-460 ms after stimulus onset, suggesting easier semantic processing of thematic than taxonomic relationships. Moreover, P600 mean amplitude was significantly increased for taxonomic targets between 520-600 ms, consistent with a greater need for stimulus reevaluation in that condition. These results offer novel evidence in favor of a dissociation between thematic and taxonomic thinking in the early phases of conceptual evaluation.

  16. 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.

  17. A DATA-DRIVEN ANALYTIC MODEL FOR PROTON ACCELERATION BY LARGE-SCALE SOLAR CORONAL SHOCKS

    Energy Technology Data Exchange (ETDEWEB)

    Kozarev, Kamen A. [Smithsonian Astrophysical Observatory (United States); Schwadron, Nathan A. [Institute for the Study of Earth, Oceans, and Space, University of New Hampshire (United States)

    2016-11-10

    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.

  18. Ant colony optimization and event-based dynamic task scheduling and staffing for software projects

    Science.gov (United States)

    Ellappan, Vijayan; Ashwini, J.

    2017-11-01

    In programming change organizations from medium to inconceivable scale broadens, the issue of wander orchestrating is amazingly unusual and testing undertaking despite considering it a manual system. Programming wander-organizing requirements to deal with the issue of undertaking arranging and in addition the issue of human resource portion (also called staffing) in light of the way that most of the advantages in programming ventures are individuals. We propose a machine learning approach with finds respond in due order regarding booking by taking in the present arranging courses of action and an event based scheduler revives the endeavour arranging system moulded by the learning computation in perspective of the conformity in event like the begin with the Ander, the instant at what time possessions be free starting to ended errands, and the time when delegates stick together otherwise depart the wander inside the item change plan. The route toward invigorating the timetable structure by the even based scheduler makes the arranging method dynamic. It uses structure components to exhibit the interrelated surges of endeavours, slip-ups and singular all through different progression organizes and is adjusted to mechanical data. It increases past programming wander movement ask about by taking a gander at a survey based process with a one of a kind model, organizing it with the data based system for peril assessment and cost estimation, and using a choice showing stage.

  19. Exceptional Air Mass Transport and Dynamical Drivers of an Extreme Wintertime Arctic Warm Event

    Science.gov (United States)

    Binder, Hanin; Boettcher, Maxi; Grams, Christian M.; Joos, Hanna; Pfahl, Stephan; Wernli, Heini

    2017-12-01

    At the turn of the years 2015/2016, maximum surface temperature in the Arctic reached record-high values, exceeding the melting point, which led to a strong reduction of the Arctic sea ice extent in the middle of the cold season. Here we show, using a Lagrangian method, that a combination of very different airstreams contributed to this event: (i) warm low-level air of subtropical origin, (ii) initially cold low-level air of polar origin heated by surface fluxes, and (iii) strongly descending air heated by adiabatic compression. The poleward transport of these warm airstreams occurred along an intense low-level jet between a series of cyclones and a quasi-stationary anticyclone. The complex 3-D configuration that enabled this transport was facilitated by continuous warm conveyor belt ascent into the upper part of the anticyclone. This study emphasizes the combined role of multiple transport processes and transient synoptic-scale dynamics for establishing an extreme Arctic warm event.

  20. A Novel Event-Based Incipient Slip Detection Using Dynamic Active-Pixel Vision Sensor (DAVIS).

    Science.gov (United States)

    Rigi, Amin; Baghaei Naeini, Fariborz; Makris, Dimitrios; Zweiri, Yahya

    2018-01-24

    In this paper, a novel approach to detect incipient slip based on the contact area between a transparent silicone medium and different objects using a neuromorphic event-based vision sensor (DAVIS) is proposed. Event-based algorithms are developed to detect incipient slip, slip, stress distribution and object vibration. Thirty-seven experiments were performed on five objects with different sizes, shapes, materials and weights to compare precision and response time of the proposed approach. The proposed approach is validated by using a high speed constitutional camera (1000 FPS). The results indicate that the sensor can detect incipient slippage with an average of 44.1 ms latency in unstructured environment for various objects. It is worth mentioning that the experiments were conducted in an uncontrolled experimental environment, therefore adding high noise levels that affected results significantly. However, eleven of the experiments had a detection latency below 10 ms which shows the capability of this method. The results are very promising and show a high potential of the sensor being used for manipulation applications especially in dynamic environments.

  1. 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.

  2. Source space analysis of event-related dynamic reorganization of brain networks.

    Science.gov (United States)

    Ioannides, Andreas A; Dimitriadis, Stavros I; Saridis, George A; Voultsidou, Marotesa; Poghosyan, Vahe; Liu, Lichan; Laskaris, Nikolaos A

    2012-01-01

    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.

  3. Dynamical Changes Induced by the Solar Proton Events in October-November 2003

    Science.gov (United States)

    Jackman, C. H.; Roble, R. G.; Fleming, E. L.

    2006-05-01

    The very large solar storms in October-November 2003 caused solar proton events (SPEs) at the Earth and impacted the upper atmospheric polar cap regions. The Thermosphere Ionosphere Mesosphere Electrodynamic General Circulation Mode (TIME-GCM) was used to study the atmospheric dynamical influence of the solar protons that occurred in Oct-Nov 2003, the fourth largest period of SPEs measured in the past 40 years. The highly energetic solar protons caused ionization and changes in the electric field, which led to Joule heating of the mesosphere and lower thermosphere. This heating led to temperature increases up to 4K in the upper mesosphere. The solar proton-induced ionization, as well as dissociation processes, led to the production of odd hydrogen (HOx) and odd nitrogen (NOy). Substantial (>40%) short-lived ozone decreases followed these enhancements of HOx and NOy and led to a cooling of the mesosphere and upper stratosphere. This cooling led to temperature decreases up to 2.5K. The solar proton-caused temperature changes led to maximum meridional and zonal wind variations of +/- 2 m/s on background winds up to +/- 30 m/s. The solar proton-induced wind perturbations were computed to taper off over a period of several days past the SPEs. Solar cycle 23 was accompanied by ten very large SPEs between 1998 and 2005, along with numerous smaller events. These solar proton-driven atmospheric variations need to be carefully considered when examining other polar changes.

  4. Position sensitive regions in a generic radiation sensor based on single event upsets in dynamic RAMs

    International Nuclear Information System (INIS)

    Darambara, D.G.; Spyrou, N.M.

    1997-01-01

    Modern integrated circuits are highly complex systems and, as such, are susceptible to occasional failures. Semiconductor memory devices, particularly dynamic random access memories (dRAMs), are subject to random, transient single event upsets (SEUs) created by energetic ionizing radiation. These radiation-induced soft failures in the stored data of silicon based memory chips provide the foundation for a new, highly efficient, low cost generic radiation sensor. The susceptibility and the detection efficiency of a given dRAM device to SEUs is a complicated function of the circuit design and geometry, the operating conditions and the physics of the charge collection mechanisms involved. Typically, soft error rates measure the cumulative response of all sensitive regions of the memory by broad area chip exposure in ionizing radiation environments. However, this study shows that many regions of a dynamic memory are competing charge collection centres having different upset thresholds. The contribution to soft fails from discrete regions or individual circuit elements of the memory device is unambiguously separated. Hence the use of the dRAM as a position sensitive radiation detector, with high spatial resolution, is assessed and demonstrated. (orig.)

  5. Temporal variability in phosphorus transfers: classifying concentration–discharge event dynamics

    Directory of Open Access Journals (Sweden)

    P. Haygarth

    2004-01-01

    Full Text Available The importance of temporal variability in relationships between phosphorus (P concentration (Cp and discharge (Q is linked to a simple means of classifying the circumstances of Cp–Q relationships in terms of functional types of response. New experimental data at the upstream interface of grassland soil and catchment systems at a range of scales (lysimeters to headwaters in England and Australia are used to demonstrate the potential of such an approach. Three types of event are defined as Types 1–3, depending on whether the relative change in Q exceeds the relative change in Cp (Type 1, whether Cp and Q are positively inter-related (Type 2 and whether Cp varies yet Q is unchanged (Type 3. The classification helps to characterise circumstances that can be explained mechanistically in relation to (i the scale of the study (with a tendency towards Type 1 in small scale lysimeters, (ii the form of P with a tendency for Type 1 for soluble (i.e., p–Q relationships that can be developed further to contribute to future models of P transfer and delivery from slope to stream. Studies that evaluate the temporal dynamics of the transfer of P are currently grossly under-represented in comparison with models based on static/spatial factors. Keywords: phosphorus, concentration, discharge, lysimeters, temporal dynamics, overland flow

  6. SIMULATED HUMAN ERROR PROBABILITY AND ITS APPLICATION TO DYNAMIC HUMAN FAILURE EVENTS

    Energy Technology Data Exchange (ETDEWEB)

    Herberger, Sarah M.; Boring, Ronald L.

    2016-10-01

    Abstract Objectives: Human reliability analysis (HRA) methods typically analyze human failure events (HFEs) at the overall task level. For dynamic HRA, it is important to model human activities at the subtask level. There exists a disconnect between dynamic subtask level and static task level that presents issues when modeling dynamic scenarios. For example, the SPAR-H method is typically used to calculate the human error probability (HEP) at the task level. As demonstrated in this paper, quantification in SPAR-H does not translate to the subtask level. Methods: Two different discrete distributions were generated for each SPAR-H Performance Shaping Factor (PSF) to define the frequency of PSF levels. The first distribution was a uniform, or uninformed distribution that assumed the frequency of each PSF level was equally likely. The second non-continuous distribution took the frequency of PSF level as identified from an assessment of the HERA database. These two different approaches were created to identify the resulting distribution of the HEP. The resulting HEP that appears closer to the known distribution, a log-normal centered on 1E-3, is the more desirable. Each approach then has median, average and maximum HFE calculations applied. To calculate these three values, three events, A, B and C are generated from the PSF level frequencies comprised of subtasks. The median HFE selects the median PSF level from each PSF and calculates HEP. The average HFE takes the mean PSF level, and the maximum takes the maximum PSF level. The same data set of subtask HEPs yields starkly different HEPs when aggregated to the HFE level in SPAR-H. Results: Assuming that each PSF level in each HFE is equally likely creates an unrealistic distribution of the HEP that is centered at 1. Next the observed frequency of PSF levels was applied with the resulting HEP behaving log-normally with a majority of the values under 2.5% HEP. The median, average and maximum HFE calculations did yield

  7. Global retrieval of soil moisture and vegetation properties using data-driven methods

    Science.gov (United States)

    Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

    2017-04-01

    Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre

  8. 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.

  9. Role of the boundary layer dynamics effects on an extreme air pollution event in Paris

    Science.gov (United States)

    Dupont, J.-C.; Haeffelin, M.; Badosa, J.; Elias, T.; Favez, O.; Petit, J. E.; Meleux, F.; Sciare, J.; Crenn, V.; Bonne, J. L.

    2016-09-01

    The physical and chemical aerosol properties are explored here based on ground-based observations in the Paris region to better understand the role of clouds, radiative fluxes and dynamics on aerosol loading during a heavy regional air pollution that occurred in March 2014 over North-Western Europe. This event is primarily characterized by a fine particle mass (PM2.5) increase from 10 to more than 120 μg m-3 and a simultaneous decrease of the horizontal visibility from 40 to 1 km, mainly due to significant formation of ammonium nitrate particles. The aerosol optical depth (AOD) at 550 nm increased steadily from about 0.06 on March 6 to more than 0.9 five days later. The scattering of the solar radiation by polluted particles induced, at the peak of the heavy pollution event, an instantaneous shortwave flux decrease of about 300 W m-2 for direct irradiance and an increase of about 150 W m-2 for diffuse irradiance (only scattering). The mean surface aerosol effect efficiency (effect per unit optical depth) is of about -80 W m-2 with a mean aerosol direct radiative effect of -23 W m-2. The dynamical and radiative processes that can be responsible for the diurnal cycle of PM2.5 in terms of amplitude and timing are investigated. A comparative analysis is performed for 4 consecutive days (between March 11 and 14), showing that the PM2.5 diurnal cycle can be modulated in time and amplitude by local processes such as the boundary layer depth development (ranging from 100 m to 1350 m), surface relative humidity (100%-35%), thermal structure (10 °C-16 °C for day/night amplitude), dynamics (wind speed ranging from 4 m s-1 to 1.5 m s-1) and turbulence (turbulent kinetic energy reaching 2 m2 s-2) near the surface and wind shear along the vertical. Finally, modeled and measured surface PM2.5 loadings are also compared here, notably illustrating the need of accurate boundary layer depth data for efficient air quality forecasts.

  10. The impact of extreme flooding events and anthropogenic stressors on the macrobenthic communities’ dynamics

    Science.gov (United States)

    Cardoso, P. G.; Raffaelli, D.; Lillebø, A. I.; Verdelhos, T.; Pardal, M. A.

    2008-02-01

    Marine and coastal environments are among the most ecologically and socio-economically important habitats on Earth. However, climate change associated with a variety of anthropogenic stressors (e.g. eutrophication) may interact to produce combined impacts on biodiversity and ecosystem functioning, which in turn will have profound implications for marine ecosystems and the economic and social systems that depend upon them. Over period 1980-2000, the environment of the Mondego estuary, Portugal, has deteriorated through eutrophication, manifested in the replacement of seagrasses by opportunistic macroalgae, degradation of water quality and increased turbidity, and the system has also experienced extreme flood events. A restoration plan was implemented in 1998 which aimed to reverse the eutrophication effects, especially to restore the original natural seagrass ( Zostera noltii) community. This paper explores the interactions between extreme weather events (e.g. intense floods) and anthropogenic stressors (e.g. eutrophication) on the dynamics of the macrobenthic assemblages and the socio-economic implications that follow. We found that during the previous decade, the intensification of extreme flooding events had significant effects on the structure and functioning of macrobenthic communities, specifically a decline in total biomass, a decline in species richness and a decline in suspension feeders. However, the earlier eutrophication process also strongly modified the macrobenthic community, seen as a decline in species richness, increase in detritivores and a decline in herbivores together with a significant increase in small deposit-feeding polychaetes. After the implementation of the management plan, macrobenthic assemblages seemed to be recovering from eutrophication, but it is argued here that those earlier impacts reduced system stability and the resilience of the macrobenthic assemblages, so that its ability to cope with other stressors was compromised. Thus

  11. Automatic translation of MPI source into a latency-tolerant, data-driven form

    International Nuclear Information System (INIS)

    Nguyen, Tan; Cicotti, Pietro; Bylaska, Eric; Quinlan, Dan; Baden, Scott

    2017-01-01

    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. We reformulate MPI source into a task dependency graph representation, which partially orders the 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. As a result, the translator is more than a means of hiding communication costs automatically; it demonstrates the utility of semantic level optimization against a well-known library.

  12. The test of data driven TDC application in high energy physics experiment

    International Nuclear Information System (INIS)

    Liu Shubin; Guo Jianhua; Zhang Yanli; Zhao Long; An Qi

    2006-01-01

    In the high energy physics domain there is a trend to use integrated, high resolution, multi-hit time-digital-converter for time measurement, of which the data driven TDC is an important direction. Study on the method of how to test high performance TDC's characters and how to improve these characters will help us to select the proper TDC. The authors have studied the testing of a new high resolution TDC, which is planned to use in the third modification project of Beijing Spectrometer (BESIII). This paper introduces the test platform we built for the TDC, and the method by which we tested for nonlinearity, resolution, double pulse resolution characters, etc. The paper also gives the test results and introduces the compensation way to achieve a very high resolution (24.4 ps). (authors)

  13. Data-driven techniques to estimate parameters in a rate-dependent ferromagnetic hysteresis model

    International Nuclear Information System (INIS)

    Hu Zhengzheng; Smith, Ralph C.; Ernstberger, Jon M.

    2012-01-01

    The quantification of rate-dependent ferromagnetic hysteresis is important in a range of applications including high speed milling using Terfenol-D actuators. There exist a variety of frameworks for characterizing rate-dependent hysteresis including the magnetic model in Ref. , the homogenized energy framework, Preisach formulations that accommodate after-effects, and Prandtl-Ishlinskii models. A critical issue when using any of these models to characterize physical devices concerns the efficient estimation of model parameters through least squares data fits. A crux of this issue is the determination of initial parameter estimates based on easily measured attributes of the data. In this paper, we present data-driven techniques to efficiently and robustly estimate parameters in the homogenized energy model. This framework was chosen due to its physical basis and its applicability to ferroelectric, ferromagnetic and ferroelastic materials.

  14. 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...

  15. Sensor fault analysis using decision theory and data-driven modeling of pressurized water reactor subsystems

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Skorska, M.

    1984-01-01

    Instrument fault detection and estimation is important for process surveillance, control, and safety functions of a power plant. The method incorporates the dual-hypotheses decision procedure and system characterization using data-driven time-domain models of signals representing the system. The multivariate models can be developed on-line and can be adapted to changing system conditions. For the method to be effective, specific subsystems of pressurized water reactors were considered, and signal selection was made such that a strong causal relationship exists among the measured variables. The technique is applied to the reactor core subsystem of the loss-of-fluid test reactor using in-core neutron detector and core-exit thermocouple signals. Thermocouple anomalies such as bias error, noise error, and slow drift in the sensor are detected and estimated using appropriate measurement models

  16. Data-driven process decomposition and robust online distributed modelling for large-scale processes

    Science.gov (United States)

    Shu, Zhang; Lijuan, Li; Lijuan, Yao; Shipin, Yang; Tao, Zou

    2018-02-01

    With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.

  17. 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 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...... 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 system analysis and control framework. Therefore, only measured data is used to obtain the proposed controllability measure. The proposed controllability measure not only shows if the system is controllable or not, but also reveals the level of controllability, which is the information its previous...

  18. Beyond Crowd Judgments: Data-driven Estimation of Market Value in Association Football

    DEFF Research Database (Denmark)

    Müller, Oliver; Simons, Alexander; Weinmann, Markus

    2017-01-01

    concern. Market values can be understood as estimates of transfer fees—that is, prices that could be paid for a player on the football market—so they play an important role in transfer negotiations. These values have traditionally been estimated by football experts, but crowdsourcing has emerged......Association football is a popular sport, but it is also a big business. From a managerial perspective, the most important decisions that team managers make concern player transfers, so issues related to player valuation, especially the determination of transfer fees and market values, are of major......’ market values using multilevel regression analysis. The regression results suggest that data-driven estimates of market value can overcome several of the crowd's practical limitations while producing comparably accurate numbers. Our results have important implications for football managers and scouts...

  19. 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

    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......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...

  20. 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.

  1. A data-driven fault-tolerant control design of linear multivariable systems with performance optimization.

    Science.gov (United States)

    Li, Zhe; Yang, Guang-Hong

    2017-09-01

    In this paper, an integrated data-driven fault-tolerant control (FTC) design scheme is proposed under the configuration of the Youla parameterization for multiple-input multiple-output (MIMO) systems. With unknown system model parameters, the canonical form identification technique is first applied to design the residual observer in fault-free case. In faulty case, with online tuning of the Youla parameters based on the system data via the gradient-based algorithm, the fault influence is attenuated with system performance optimization. In addition, to improve the robustness of the residual generator to a class of system deviations, a novel adaptive scheme is proposed for the residual generator to prevent its over-activation. Simulation results of a two-tank flow system demonstrate the optimized performance and effect of the proposed FTC scheme. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  2. 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.

  3. A data-driven multiplicative fault diagnosis approach for automation processes.

    Science.gov (United States)

    Hao, Haiyang; Zhang, Kai; Ding, Steven X; Chen, Zhiwen; Lei, Yaguo

    2014-09-01

    This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented. Copyright © 2013. Published by Elsevier Ltd.

  4. Data-driven gradient algorithm for high-precision quantum control

    Science.gov (United States)

    Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

    2018-04-01

    In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

  5. 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.

  6. Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

    Directory of Open Access Journals (Sweden)

    Merima Kulin

    2016-06-01

    Full Text Available 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.

  7. 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.

  8. Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

    Directory of Open Access Journals (Sweden)

    Xianming Dou

    2017-12-01

    Full Text Available Accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originating from global environmental change, and providing helpful information about carbon and water content for analyzing and diagnosing past and future climate change. The main focus of the current work was to investigate the feasibility of four comparatively new methods, including generalized regression neural network, group method of data handling (GMDH, extreme learning machine and adaptive neuro-fuzzy inference system (ANFIS, for elucidating the carbon and water fluxes in a forest ecosystem. A comparison was made between these models and two widely used data-driven models, artificial neural network (ANN and support vector machine (SVM. All the models were evaluated based on the following statistical indices: coefficient of determination, Nash-Sutcliffe efficiency, root mean square error and mean absolute error. Results indicated that the data-driven models are capable of accounting for most variance in each flux with the limited meteorological variables. The ANN model provided the best estimates for gross primary productivity (GPP and net ecosystem exchange (NEE, while the ANFIS model achieved the best for ecosystem respiration (R, indicating that no single model was consistently superior to others for the carbon flux prediction. In addition, the GMDH model consistently produced somewhat worse results for all the carbon flux and evapotranspiration (ET estimations. On the whole, among the carbon and water fluxes, all the models produced similar highly satisfactory accuracy for GPP, R and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. Based on these findings, it was concluded that these advanced models are promising alternatives to ANN and SVM for estimating the terrestrial carbon and water fluxes.

  9. Preface [HD3-2015: International meeting on high-dimensional data-driven science

    International Nuclear Information System (INIS)

    2016-01-01

    A never-ending series of innovations in measurement technology and evolutions in information and communication technologies have led to the ongoing generation and accumulation of large quantities of high-dimensional data every day. While detailed data-centric approaches have been pursued in respective research fields, situations have been encountered where the same mathematical framework of high-dimensional data analysis can be found in a wide variety of seemingly unrelated research fields, such as estimation on the basis of undersampled Fourier transform in nuclear magnetic resonance spectroscopy in chemistry, in magnetic resonance imaging in medicine, and in astronomical interferometry in astronomy. In such situations, bringing diverse viewpoints together therefore becomes a driving force for the creation of innovative developments in various different research fields. This meeting focuses on “Sparse Modeling” (SpM) as a methodology for creation of innovative developments through the incorporation of a wide variety of viewpoints in various research fields. The objective of this meeting is to offer a forum where researchers with interest in SpM can assemble and exchange information on the latest results and newly established methodologies, and discuss future directions of the interdisciplinary studies for High-Dimensional Data-Driven science (HD 3 ). The meeting was held in Kyoto from 14-17 December 2015. We are pleased to publish 22 papers contributed by invited speakers in this volume of Journal of Physics: Conference Series. We hope that this volume will promote further development of High-Dimensional Data-Driven science. (paper)

  10. A data-driven weighting scheme for multivariate phenotypic endpoints recapitulates zebrafish developmental cascades

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Guozhu, E-mail: gzhang6@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Roell, Kyle R., E-mail: krroell@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Truong, Lisa, E-mail: lisa.truong@oregonstate.edu [Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR (United States); Tanguay, Robert L., E-mail: robert.tanguay@oregonstate.edu [Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR (United States); Reif, David M., E-mail: dmreif@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC (United States)

    2017-01-01

    Zebrafish have become a key alternative model for studying health effects of environmental stressors, partly due to their genetic similarity to humans, fast generation time, and the efficiency of generating high-dimensional systematic data. Studies aiming to characterize adverse health effects in zebrafish typically include several phenotypic measurements (endpoints). While there is a solid biomedical basis for capturing a comprehensive set of endpoints, making summary judgments regarding health effects requires thoughtful integration across endpoints. Here, we introduce a Bayesian method to quantify the informativeness of 17 distinct zebrafish endpoints as a data-driven weighting scheme for a multi-endpoint summary measure, called weighted Aggregate Entropy (wAggE). We implement wAggE using high-throughput screening (HTS) data from zebrafish exposed to five concentrations of all 1060 ToxCast chemicals. Our results show that our empirical weighting scheme provides better performance in terms of the Receiver Operating Characteristic (ROC) curve for identifying significant morphological effects and improves robustness over traditional curve-fitting approaches. From a biological perspective, our results suggest that developmental cascade effects triggered by chemical exposure can be recapitulated by analyzing the relationships among endpoints. Thus, wAggE offers a powerful approach for analysis of multivariate phenotypes that can reveal underlying etiological processes. - Highlights: • Introduced a data-driven weighting scheme for multiple phenotypic endpoints. • Weighted Aggregate Entropy (wAggE) implies differential importance of endpoints. • Endpoint relationships reveal developmental cascade effects triggered by exposure. • wAggE is generalizable to multi-endpoint data of different shapes and scales.

  11. Dynamical Changes Induced by the Very Large Solar Proton Events in October-November 2003

    Science.gov (United States)

    Jackman, Charles H.; Roble, Raymond G.

    2006-01-01

    The very large solar storms in October-November 2003 caused solar proton events (SPEs) at the Earth and impacted the upper atmospheric polar cap regions. The Thermosphere Ionosphere Mesosphere Electrodynamic General Circulation Mode (TIME-GCM) was used to study the atmospheric dynamical influence of the solar protons that occurred in Oct-Nov 2003, the fourth largest period of SPEs measured in the past 40 years. The highly energetic solar protons caused ionization and changes in the electric field, which led to Joule heating of the mesosphere and lower thermosphere. This heating led to temperature increases up to 4K in the upper mesosphere. The solar proton-induced ionization, as well as dissociation processes, led to the production of odd hydrogen (HO(x)) and odd nitrogen (NO(y)). Substantial (>40%) short-lived ozone decreases followed these enhancements of HO(x) and NO(y) and led to a cooling of the mesosphere and upper stratosphere. This cooling led to temperature decreases up to 2.5K. The solar proton-caused temperature changes led to maximum meridional and zonal wind variations of +/- 2 m/s on background winds up to +/- 30 m/s. The solar proton-induced wind perturbations were computed to taper off over a period of several days past the SPEs. Solar cycle 23 was accompanied by ten very large SPEs between 1998 and 2005, along with numerous smaller events. These solar proton-driven atmospheric variations need to be carefully considered when examining other polar changes.

  12. Using High Energy Precipitation for Magnetic Mapping in the Nightside Transition Region During Dynamic Events

    Science.gov (United States)

    Spanswick, E.

    2017-12-01

    Identifying the magnetic footprint of a satellite can be done using the in situ observations together with some ionospheric or low-altitude satellite observation to argue that the two measurements were made on the same field line. Nishimura et al. [2011], e.g., correlated a time series of chorus wave power near the magnetic equator with the time series of intensities of every pixel of a is roughly magnetically conjugate ASI. Often, the pattern of correlation shows a well-defined peak at the location of the satellite's magnetic footprint. Their results cannot be replicated during dynamic events (e.g., substorms), because the required auroral forms do not occur at such times. It would be important if we could make mappings with such confidence during active times. The Transition Region Explorer (TREx), which is presently being implemented, is a new ground-based facility that will remote sense electron precipitation across 3 hours of MLT and 12 degrees of magnetic latitude spanning the auroral zone in western Canada. TREx includes the world's first imaging riometers array with a contiguous field of view large enough to seamlessly track the spatio-temporal evolution of high energy electron precipitation at mesoscales. Two studies motivated the TREx riometers array. First, Baker et al. [1981] demonstrated riometer absorption is an excellent proxy for the electron energy flux integrated from 30 keV to 200keV keV at the magnetic equator on the flux tube corresponding to the location of that riometers. Second, Spanswick et al. [2007] showed the correlation between the riometers absorption and the integrated electron energy flux near the magnetic equator peaked when the satellite was nearest to conjugate to the riometers. Here we present observations using CANOPUS single beam riometers and CRRES MEB to illustrate how the relative closeness of the footpoint of an equatorial spacecraft can be assessed using high energy precipitation. As well, we present the capabilities of

  13. 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.

  14. Initial Chemical Events in CL-20 Under Extreme Conditions: An Ab Initio Molecular Dynamics Study

    National Research Council Canada - National Science Library

    Isaev, Olexandr; Kholod, Yana; Gorb, Leonid; Qasim, Mohammad; Fredrickson, Herb; Leszczynski, Jerzy

    2006-01-01

    .... In the present study molecular structure, electrostatic potential, vibrational spectrum and dynamics of thermal decomposition of CL-20 have been investigated by static and dynamic methods of ab...

  15. 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.

  16. Development of a viscoplastic dynamic fracture mechanics treatment for crack arrest predictions in a PTS event

    International Nuclear Information System (INIS)

    Kanninen, M.F.; Hudak, S.J. Jr.; Reed, K.W.; Dexter, R.J.; Polch, E.Z.; Cardinal, J.W.; Achenbach, J.D.; Popelar, C.H.

    1986-01-01

    The objective of this research is to develop a fundamentally correct methodology for the prediction of crack arrest at the high upper shelf conditions occurring in a postulated pressurized thermal shock (PTS) event. The effort is aimed at the development of a versatile finite-element method for the solution of time-dependent boundary value problems that admit inertia effects, a prescribed spatial temperature distribution, and viscoplastic constitutive and fracture behavior. Supporting this development are (1) material characterization and fracture experimentation, (2) detailed mathematical analyses of the near-tip region, (3) elastodynamic fracture analysis, and (4) elastic-plastic tearing instability analyses. As a first step, dynamic-viscoplastic analyses are currently being made of the wide plate tests being performed by the National Bureau of Standards in a companion HSST program. Some preliminary conclusions drawn from this work and from the supporting research activities are offered in this paper. The outstanding critical issues that subsequent research must focus on are also described

  17. Ab Initio Study of the Dynamical Si–O Bond Breaking Event in α-Quartz

    International Nuclear Information System (INIS)

    Su Rui; Zhang Hong; Han Wei; Chen Jun

    2015-01-01

    The Si–O bond breaking event in the α-quartz at the first triplet (T_1) excitation state is studied by using ab initio molecular dynamics (AIMD) and nudged elastic band calculations. A meta-stable non-bridging oxygen hole center and E′ center (NBOHC-E′) is observed in the AIMD which consists of a broken Si–O bond with a Si–O distance of 2.54 Å. By disallowing the re-bonding of the Si and O atoms, another defect configuration (III-Si/V-Si) is obtained and validated to be stable at both ground and excitation states. The NBOHC-E′ is found to present on the minimal energy pathway of the initial to III-Si/V-Si transition, showing that the generating of the NBOHC-E′ is an important step of the excitation induced structure defect. The energy barriers to produce the NBOHC-E′ and III-Si/V-Si defects are calculated to be 1.19 and 1.28 eV, respectively. The electronic structures of the two defects are calculated by the self-consistent GW calculations and the results show a clear electron transition from the bonding orbital to the non-bonding orbital. (paper)

  18. Temporal variability in phosphorus transfers: classifying concentration-discharge event dynamics

    Science.gov (United States)

    Haygarth, P.; Turner, B. L.; Fraser, A.; Jarvis, S.; Harrod, T.; Nash, D.; Halliwell, D.; Page, T.; Beven, K.

    The importance of temporal variability in relationships between phosphorus (P) concentration (Cp) and discharge (Q) is linked to a simple means of classifying the circumstances of Cp-Q relationships in terms of functional types of response. New experimental data at the upstream interface of grassland soil and catchment systems at a range of scales (lysimeters to headwaters) in England and Australia are used to demonstrate the potential of such an approach. Three types of event are defined as Types 1-3, depending on whether the relative change in Q exceeds the relative change in Cp (Type 1), whether Cp and Q are positively inter-related (Type 2) and whether Cp varies yet Q is unchanged (Type 3). The classification helps to characterise circumstances that can be explained mechanistically in relation to (i) the scale of the study (with a tendency towards Type 1 in small scale lysimeters), (ii) the form of P with a tendency for Type 1 for soluble (i.e., <0.45 μm P forms) and (iii) the sources of P with Type 3 dominant where P availability overrides transport controls. This simple framework provides a basis for development of a more complex and quantitative classification of Cp-Q relationships that can be developed further to contribute to future models of P transfer and delivery from slope to stream. Studies that evaluate the temporal dynamics of the transfer of P are currently grossly under-represented in comparison with models based on static/spatial factors.

  19. Proactive monitoring of a wind turbine array with lidar measurements, SCADA data and a data-driven RANS solver

    Science.gov (United States)

    Iungo, G.; Said, E. A.; Santhanagopalan, V.; Zhan, L.

    2016-12-01

    Power production of a wind farm and durability of wind turbines are strongly dependent on non-linear wake interactions occurring within a turbine array. Wake dynamics are highly affected by the specific site conditions, such as topography and local atmospheric conditions. Furthermore, contingencies through the life of a wind farm, such as turbine ageing and off-design operations, make prediction of wake interactions and power performance a great challenge in wind energy. In this work, operations of an onshore wind turbine array were monitored through lidar measurements, SCADA and met-tower data. The atmospheric wind field investing the wind farm was estimated by using synergistically the available data through five different methods, which are characterized by different confidence levels. By combining SCADA data and the lidar measurements, it was possible to estimate power losses connected with wake interactions. For this specific array, power losses were estimated to be 4% and 2% of the total power production for stable and convective atmospheric regimes, respectively. The entire dataset was then leveraged for the calibration of a data-driven RANS (DDRANS) solver for prediction of wind turbine wakes and power production. The DDRANS is based on a parabolic formulation of the Navier-Stokes equations with axisymmetry and boundary layer approximations, which allow achieving very low computational costs. Accuracy in prediction of wind turbine wakes and power production is achieved through an optimal tuning of the turbulence closure model. The latter is based on a mixing length model, which was developed based on previous wind turbine wake studies carried out through large eddy simulations and wind tunnel experiments. Several operative conditions of the wind farm under examination were reproduced through DDRANS for different stability regimes, wind directions and wind velocity. The results show that DDRANS is capable of achieving a good level of accuracy in prediction

  20. Offside Decisions by Expert Assistant Referees in Association Football: Perception and Recall of Spatial Positions in Complex Dynamic Events

    Science.gov (United States)

    Gilis, Bart; Helsen, Werner; Catteeuw, Peter; Wagemans, Johan

    2008-01-01

    This study investigated the offside decision-making process in association football. The first aim was to capture the specific offside decision-making skills in complex dynamic events. Second, we analyzed the type of errors to investigate the factors leading to incorrect decisions. Federation Internationale de Football Association (FIFA; n = 29)…

  1. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    Science.gov (United States)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-06-01

    Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space Rn. An isometric mapping F from M to a low-dimensional, compact, connected set A⊂Rd(d≪n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructing low

  2. Developing a Data Driven Process-Based Model for Remote Sensing of Ecosystem Production

    Science.gov (United States)

    Elmasri, B.; Rahman, A. F.

    2010-12-01

    Estimating ecosystem carbon fluxes at various spatial and temporal scales is essential for quantifying the global carbon cycle. Numerous models have been developed for this purpose using several environmental variables as well as vegetation indices derived from remotely sensed data. Here we present a data driven modeling approach for gross primary production (GPP) that is based on a process based model BIOME-BGC. The proposed model was run using available remote sensing data and it does not depend on look-up tables. Furthermore, this approach combines the merits of both empirical and process models, and empirical models were used to estimate certain input variables such as light use efficiency (LUE). This was achieved by using remotely sensed data to the mathematical equations that represent biophysical photosynthesis processes in the BIOME-BGC model. Moreover, a new spectral index for estimating maximum photosynthetic activity, maximum photosynthetic rate index (MPRI), is also developed and presented here. This new index is based on the ratio between the near infrared and the green bands (ρ858.5/ρ555). The model was tested and validated against MODIS GPP product and flux measurements from two eddy covariance flux towers located at Morgan Monroe State Forest (MMSF) in Indiana and Harvard Forest in Massachusetts. Satellite data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) and MODIS were used. The data driven model showed a strong correlation between the predicted and measured GPP at the two eddy covariance flux towers sites. This methodology produced better predictions of GPP than did the MODIS GPP product. Moreover, the proportion of error in the predicted GPP for MMSF and Harvard forest was dominated by unsystematic errors suggesting that the results are unbiased. The analysis indicated that maintenance respiration is one of the main factors that dominate the overall model outcome errors and improvement in maintenance respiration estimation

  3. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    International Nuclear Information System (INIS)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-01-01

    Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R n . An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R d (d<< n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology

  4. Enabling Data-Driven Methodologies Across the Data Lifecycle and Ecosystem

    Science.gov (United States)

    Doyle, R. J.; Crichton, D.

    2017-12-01

    NASA has unlocked unprecedented scientific knowledge through exploration of the Earth, our solar system, and the larger universe. NASA is generating enormous amounts of data that are challenging traditional approaches to capturing, managing, analyzing and ultimately gaining scientific understanding from science data. New architectures, capabilities and methodologies are needed to span the entire observing system, from spacecraft to archive, while integrating data-driven discovery and analytic capabilities. NASA data have a definable lifecycle, from remote collection point to validated accessibility in multiple archives. Data challenges must be addressed across this lifecycle, to capture opportunities and avoid decisions that may limit or compromise what is achievable once data arrives at the archive. Data triage may be necessary when the collection capacity of the sensor or instrument overwhelms data transport or storage capacity. By migrating computational and analytic capability to the point of data collection, informed decisions can be made about which data to keep; in some cases, to close observational decision loops onboard, to enable attending to unexpected or transient phenomena. Along a different dimension than the data lifecycle, scientists and other end-users must work across an increasingly complex data ecosystem, where the range of relevant data is rarely owned by a single institution. To operate effectively, scalable data architectures and community-owned information models become essential. NASA's Planetary Data System is having success with this approach. Finally, there is the difficult challenge of reproducibility and trust. While data provenance techniques will be part of the solution, future interactive analytics environments must support an ability to provide a basis for a result: relevant data source and algorithms, uncertainty tracking, etc., to assure scientific integrity and to enable confident decision making. Advances in data science offer

  5. 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.

  6. 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.

  7. Data-driven classification of bipolar I disorder from longitudinal course of mood.

    Science.gov (United States)

    Cochran, A L; McInnis, M G; Forger, D B

    2016-10-11

    The Diagnostic and Statistical Manual of Mental Disorder (DSM) classification of bipolar disorder defines categories to reflect common understanding of mood symptoms rather than scientific evidence. This work aimed to determine whether bipolar I can be objectively classified from longitudinal mood data and whether resulting classes have clinical associations. Bayesian nonparametric hierarchical models with latent classes and patient-specific models of mood are fit to data from Longitudinal Interval Follow-up Evaluations (LIFE) of bipolar I patients (N=209). Classes are tested for clinical associations. No classes are justified using the time course of DSM-IV mood states. Three classes are justified using the course of subsyndromal mood symptoms. Classes differed in attempted suicides (P=0.017), disability status (P=0.012) and chronicity of affective symptoms (P=0.009). Thus, bipolar I disorder can be objectively classified from mood course, and individuals in the resulting classes share clinical features. Data-driven classification from mood course could be used to enrich sample populations for pharmacological and etiological studies.

  8. The Facilitation of a Sustainable Power System: A Practice from Data-Driven Enhanced Boiler Control

    Directory of Open Access Journals (Sweden)

    Zhenlong Wu

    2018-04-01

    Full Text Available An increasing penetration of renewable energy may bring significant challenges to a power system due to its inherent intermittency. To achieve a sustainable future for renewable energy, a conventional power plant is required to be able to change its power output rapidly for a grid balance purpose. However, the rapid power change may result in the boiler operating in a dangerous manner. To this end, this paper aims to improve boiler control performance via a data-driven control strategy, namely Active Disturbance Rejection Control (ADRC. For practical implementation, a tuning method is developed for ADRC controller parameters to maximize its potential in controlling a boiler operating in different conditions. Based on a Monte Carlo simulation, a Probabilistic Robustness (PR index is subsequently formulated to represent the controller’s sensitivity to the varying conditions. The stability region of the ADRC controller is depicted to provide the search space in which the optimal group of parameters is searched for based on the PR index. Illustrative simulations are performed to verify the efficacy of the proposed method. Finally, the proposed method is experimentally applied to a boiler’s secondary air control system successfully. The results of the field application show that the proposed ADRC based on PR can ensure the expected control performance even though it works in a wider range of operating conditions. The field application depicts a promising future for the ADRC controller as an alternative solution in the power industry to integrate more renewable energy into the power grid.

  9. Forecasting success via early adoptions analysis: A data-driven study.

    Directory of Open Access Journals (Sweden)

    Giulio Rossetti

    Full Text Available Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

  10. Optimizing preventive maintenance policy: A data-driven application for a light rail braking system.

    Science.gov (United States)

    Corman, Francesco; Kraijema, Sander; Godjevac, Milinko; Lodewijks, Gabriel

    2017-10-01

    This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.

  11. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study.

    Science.gov (United States)

    Kim, Eun-Kyung; Kim, Hyo-Eun; Han, Kyunghwa; Kang, Bong Joo; Sohn, Yu-Mee; Woo, Ok Hee; Lee, Chan Wha

    2018-02-09

    We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients' age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remainder were used for training. The core algorithm of DIB-MG is a deep convolutional neural network; a deep learning algorithm specialized for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth. At the operating point (threshold) of 0.5, sensitivity was 75.6% and 76.1% when specificity was 90.2% and 88.5%, and AUC was 0.903 and 0.906 for the validation and test sets, respectively. This research showed the potential of DIB-MG as a screening tool for breast cancer.

  12. Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision.

    Science.gov (United States)

    Tu, Junchao; Zhang, Liyan

    2018-01-12

    A new solution to the problem of galvanometric laser scanning (GLS) system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN)to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM). By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

  13. Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision

    Directory of Open Access Journals (Sweden)

    Junchao Tu

    2018-01-01

    Full Text Available A new solution to the problem of galvanometric laser scanning (GLS system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN)to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM. By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

  14. 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.

  15. Data-free and data-driven spectral perturbations for RANS UQ

    Science.gov (United States)

    Edeling, Wouter; Mishra, Aashwin; Iaccarino, Gianluca

    2017-11-01

    Despite recent developments in high-fidelity turbulent flow simulations, RANS modeling is still vastly used by industry, due to its inherent low cost. Since accuracy is a concern in RANS modeling, model-form UQ is an essential tool for assessing the impacts of this uncertainty on quantities of interest. Applying the spectral decomposition to the modeled Reynolds-Stress Tensor (RST) allows for the introduction of decoupled perturbations into the baseline intensity (kinetic energy), shape (eigenvalues), and orientation (eigenvectors). This constitutes a natural methodology to evaluate the model form uncertainty associated to different aspects of RST modeling. In a predictive setting, one frequently encounters an absence of any relevant reference data. To make data-free predictions with quantified uncertainty we employ physical bounds to a-priori define maximum spectral perturbations. When propagated, these perturbations yield intervals of engineering utility. High-fidelity data opens up the possibility of inferring a distribution of uncertainty, by means of various data-driven machine-learning techniques. We will demonstrate our framework on a number of flow problems where RANS models are prone to failure. This research was partially supported by the Defense Advanced Research Projects Agency under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo), and the DOE PSAAP-II program.

  16. Lessons learned from a data-driven college access program: The National College Advising Corps.

    Science.gov (United States)

    Horng, Eileen L; Evans, Brent J; Antonio, Anthony L; Foster, Jesse D; Kalamkarian, Hoori S; Hurd, Nicole F; Bettinger, Eric P

    2013-01-01

    This chapter discusses the collaboration between a national college access program, the National College Advising Corps (NCAC), and its research and evaluation team at Stanford University. NCAC is currently active in almost four hundred high schools and through the placement of a recent college graduate to serve as a college adviser provides necessary information and support for students who may find it difficult to navigate the complex college admission process. The advisers also conduct outreach to underclassmen in an effort to improve the school-wide college-going culture. Analyses include examination of both quantitative and qualitative data from numerous sources and partners with every level of the organization from the national office to individual high schools. The authors discuss balancing the pursuit of evaluation goals with academic scholarship. In an effort to benefit other programs seeking to form successful data-driven interventions, the authors provide explicit examples of the partnership and present several examples of how the program has benefited from the data gathered by the evaluation team. © WILEY PERIODICALS, INC.

  17. 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

    2017-02-16

    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.

  18. Forecasting success via early adoptions analysis: A data-driven study.

    Science.gov (United States)

    Rossetti, Giulio; Milli, Letizia; Giannotti, Fosca; Pedreschi, Dino

    2017-01-01

    Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

  19. Data-Driven Astrochemistry: One Step Further within the Origin of Life Puzzle.

    Science.gov (United States)

    Ruf, Alexander; d'Hendecourt, Louis L S; Schmitt-Kopplin, Philippe

    2018-06-01

    Astrochemistry, meteoritics and chemical analytics represent a manifold scientific field, including various disciplines. In this review, clarifications on astrochemistry, comet chemistry, laboratory astrophysics and meteoritic research with respect to organic and metalorganic chemistry will be given. The seemingly large number of observed astrochemical molecules necessarily requires explanations on molecular complexity and chemical evolution, which will be discussed. Special emphasis should be placed on data-driven analytical methods including ultrahigh-resolving instruments and their interplay with quantum chemical computations. These methods enable remarkable insights into the complex chemical spaces that exist in meteorites and maximize the level of information on the huge astrochemical molecular diversity. In addition, they allow one to study even yet undescribed chemistry as the one involving organomagnesium compounds in meteorites. Both targeted and non-targeted analytical strategies will be explained and may touch upon epistemological problems. In addition, implications of (metal)organic matter toward prebiotic chemistry leading to the emergence of life will be discussed. The precise description of astrochemical organic and metalorganic matter as seeds for life and their interactions within various astrophysical environments may appear essential to further study questions regarding the emergence of life on a most fundamental level that is within the molecular world and its self-organization properties.

  20. Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

    Directory of Open Access Journals (Sweden)

    Hua-pu Lu

    2015-01-01

    Full Text Available With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzy c-means based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

  1. 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.

  2. Clinical review: optimizing enteral nutrition for critically ill patients - a simple data-driven formula

    Science.gov (United States)

    2011-01-01

    In modern critical care, the paradigm of 'therapeutic nutrition' is replacing traditional 'supportive nutrition'. Standard enteral formulas meet basic macro- and micronutrient needs; therapeutic enteral formulas meet these basic needs and also contain specific pharmaconutrients that may attenuate hyperinflammatory responses, enhance the immune responses to infection, or improve gastrointestinal tolerance. Choosing the right enteral feeding formula may positively affect a patient's outcome; targeted use of therapeutic formulas can reduce the incidence of infectious complications, shorten lengths of stay in the ICU and in the hospital, and lower risk for mortality. In this paper, we review principles of how to feed (enteral, parenteral, or both) and when to feed (early versus delayed start) patients who are critically ill. We discuss what to feed these patients in the context of specific pharmaconutrients in specialized feeding formulations, that is, arginine, glutamine, antioxidants, certain ω-3 and ω-6 fatty acids, hydrolyzed proteins, and medium-chain triglycerides. We summarize current expert guidelines for nutrition in patients with critical illness, and we present specific clinical evidence on the use of enteral formulas supplemented with anti-inflammatory or immune-modulating nutrients, and gastrointestinal tolerance-promoting nutritional formulas. Finally, we introduce an algorithm to help bedside clinicians make data-driven feeding decisions for patients with critical illness. PMID:22136305

  3. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

    Science.gov (United States)

    Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

    2017-09-29

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  4. Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

    Directory of Open Access Journals (Sweden)

    Po-Chiang Lin

    2016-01-01

    Full Text Available Network densification is regarded as one of the important ingredients to increase capacity for next generation mobile communication networks. However, it also leads to mobility problems since users are more likely to hand over to another cell in dense or even ultradense mobile communication networks. Therefore, supporting seamless and robust connectivity through such networks becomes a very important issue. In this paper, we investigate handover (HO optimization in next generation mobile communication networks. We propose a data-driven handover optimization (DHO approach, which aims to mitigate mobility problems including too-late HO, too-early HO, HO to wrong cell, ping-pong HO, and unnecessary HO. The key performance indicator (KPI is defined as the weighted average of the ratios of these mobility problems. The DHO approach collects data from the mobile communication measurement results and provides a model to estimate the relationship between the KPI and features from the collected dataset. Based on the model, the handover parameters, including the handover margin and time-to-trigger, are optimized to minimize the KPI. Simulation results show that the proposed DHO approach could effectively mitigate mobility problems.

  5. Design of a data-driven predictive controller for start-up process of AMT vehicles.

    Science.gov (United States)

    Lu, Xiaohui; Chen, Hong; Wang, Ping; Gao, Bingzhao

    2011-12-01

    In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.

  6. Data-driven quantification of the robustness and sensitivity of cell signaling networks

    International Nuclear Information System (INIS)

    Mukherjee, Sayak; Seok, Sang-Cheol; Vieland, Veronica J; Das, Jayajit

    2013-01-01

    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. (paper)

  7. VLAM-G: Interactive Data Driven Workflow Engine for Grid-Enabled Resources

    Directory of Open Access Journals (Sweden)

    Vladimir Korkhov

    2007-01-01

    Full Text Available Grid brings the power of many computers to scientists. However, the development of Grid-enabled applications requires knowledge about Grid infrastructure and low-level API to Grid services. In turn, workflow management systems provide a high-level environment for rapid prototyping of experimental computing systems. Coupling Grid and workflow paradigms is important for the scientific community: it makes the power of the Grid easily available to the end user. The paradigm of data driven workflow execution is one of the ways to enable distributed workflow on the Grid. The work presented in this paper is carried out in the context of the Virtual Laboratory for e-Science project. We present the VLAM-G workflow management system and its core component: the Run-Time System (RTS. The RTS is a dataflow driven workflow engine which utilizes Grid resources, hiding the complexity of the Grid from a scientist. Special attention is paid to the concept of dataflow and direct data streaming between distributed workflow components. We present the architecture and components of the RTS, describe the features of VLAM-G workflow execution, and evaluate the system by performance measurements and a real life use case.

  8. An asynchronous data-driven readout prototype for CEPC vertex detector

    Science.gov (United States)

    Yang, Ping; Sun, Xiangming; Huang, Guangming; Xiao, Le; Gao, Chaosong; Huang, Xing; Zhou, Wei; Ren, Weiping; Li, Yashu; Liu, Jianchao; You, Bihui; Zhang, Li

    2017-12-01

    The Circular Electron Positron Collider (CEPC) is proposed as a Higgs boson and/or Z boson factory for high-precision measurements on the Higgs boson. The precision of secondary vertex impact parameter plays an important role in such measurements which typically rely on flavor-tagging. Thus silicon CMOS Pixel Sensors (CPS) are the most promising technology candidate for a CEPC vertex detector, which can most likely feature a high position resolution, a low power consumption and a fast readout simultaneously. For the R&D of the CEPC vertex detector, we have developed a prototype MIC4 in the Towerjazz 180 nm CMOS Image Sensor (CIS) process. We have proposed and implemented a new architecture of asynchronous zero-suppression data-driven readout inside the matrix combined with a binary front-end inside the pixel. The matrix contains 128 rows and 64 columns with a small pixel pitch of 25 μm. The readout architecture has implemented the traditional OR-gate chain inside a super pixel combined with a priority arbiter tree between the super pixels, only reading out relevant pixels. The MIC4 architecture will be introduced in more detail in this paper. It will be taped out in May and will be characterized when the chip comes back.

  9. Outcomes from the GLEON fellowship program. Training graduate students in data driven network science.

    Science.gov (United States)

    Dugan, H.; Hanson, P. C.; Weathers, K. C.

    2016-12-01

    In the water sciences there is a massive need for graduate students who possess the analytical and technical skills to deal with large datasets and function in the new paradigm of open, collaborative -science. The Global Lake Ecological Observatory Network (GLEON) graduate fellowship program (GFP) was developed as an interdisciplinary training program to supplement the intensive disciplinary training of traditional graduate education. The primary goal of the GFP was to train a diverse cohort of graduate students in network science, open-web technologies, collaboration, and data analytics, and importantly to provide the opportunity to use these skills to conduct collaborative research resulting in publishable scientific products. The GFP is run as a series of three week-long workshops over two years that brings together a cohort of twelve students. In addition, fellows are expected to attend and contribute to at least one international GLEON all-hands' meeting. Here, we provide examples of training modules in the GFP (model building, data QA/QC, information management, bayesian modeling, open coding/version control, national data programs), as well as scientific outputs (manuscripts, software products, and new global datasets) produced by the fellows, as well as the process by which this team science was catalyzed. Data driven education that lets students apply learned skills to real research projects reinforces concepts, provides motivation, and can benefit their publication record. This program design is extendable to other institutions and networks.

  10. Automatic data-driven real-time segmentation and recognition of surgical workflow.

    Science.gov (United States)

    Dergachyova, Olga; Bouget, David; Huaulmé, Arnaud; Morandi, Xavier; Jannin, Pierre

    2016-06-01

    With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection. The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision. On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases. Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.

  11. A transparent and data-driven global tectonic regionalization model for seismic hazard assessment

    Science.gov (United States)

    Chen, Yen-Shin; Weatherill, Graeme; Pagani, Marco; Cotton, Fabrice

    2018-05-01

    A key concept that is common to many assumptions inherent within seismic hazard assessment is that of tectonic similarity. This recognizes that certain regions of the globe may display similar geophysical characteristics, such as in the attenuation of seismic waves, the magnitude scaling properties of seismogenic sources or the seismic coupling of the lithosphere. Previous attempts at tectonic regionalization, particularly within a seismic hazard assessment context, have often been based on expert judgements; in most of these cases, the process for delineating tectonic regions is neither reproducible nor consistent from location to location. In this work, the regionalization process is implemented in a scheme that is reproducible, comprehensible from a geophysical rationale, and revisable when new relevant data are published. A spatial classification-scheme is developed based on fuzzy logic, enabling the quantification of concepts that are approximate rather than precise. Using the proposed methodology, we obtain a transparent and data-driven global tectonic regionalization model for seismic hazard applications as well as the subjective probabilities (e.g. degree of being active/degree of being cratonic) that indicate the degree to which a site belongs in a tectonic category.

  12. Modern data-driven decision support systems: the role of computing with words and computational linguistics

    Science.gov (United States)

    Kacprzyk, Janusz; Zadrożny, Sławomir

    2010-05-01

    We present how the conceptually and numerically simple concept of a fuzzy linguistic database summary can be a very powerful tool for gaining much insight into the very essence of data. The use of linguistic summaries provides tools for the verbalisation of data analysis (mining) results which, in addition to the more commonly used visualisation, e.g. via a graphical user interface, can contribute to an increased human consistency and ease of use, notably for supporting decision makers via the data-driven decision support system paradigm. Two new relevant aspects of the analysis are also outlined which were first initiated by the authors. First, following Kacprzyk and Zadrożny, it is further considered how linguistic data summarisation is closely related to some types of solutions used in natural language generation (NLG). This can make it possible to use more and more effective and efficient tools and techniques developed in NLG. Second, similar remarks are given on relations to systemic functional linguistics. Moreover, following Kacprzyk and Zadrożny, comments are given on an extremely relevant aspect of scalability of linguistic summarisation of data, using a new concept of a conceptual scalability.

  13. On the selection of user-defined parameters in data-driven stochastic subspace identification

    Science.gov (United States)

    Priori, C.; De Angelis, M.; Betti, R.

    2018-02-01

    The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI. Then, the case of non-stationary seismic excitations with a reduced number of samples is considered. In this paper, partitions of the data matrix different from the one proposed in the SSI literature are investigated, together with the influence of different choices of the weighting matrices. The study is carried out considering two different applications: (1) data obtained from vibration tests on a scaled structure and (2) in-situ tests on a reinforced concrete building. Referring to the former, the identification of a steel frame structure tested on a shaking table is performed using its responses in terms of absolute accelerations to a stationary (white noise) base excitation and to non-stationary seismic excitations of low intensity. Black-box and modal models are identified in both cases and the results are compared with those from an input-output subspace technique. With regards to the latter, the identification of a complex hospital building is conducted using data obtained from ambient vibration tests.

  14. 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.

  15. Data Driven - Android based displays on data acquisition and system status

    CERN Document Server

    Canilho, Paulo

    2014-01-01

    For years, both hardware and software engineers have struggled with the acquisition of device information in a flexible and fast perspective, numerous devices cannot have their status quickly tested due to time limitation associated with the travelling to a computer terminal. For instance, in order to test a scintillator status, one has to inject beam into the device and quickly return to a terminal to see the results, this is not only time demanding but extremely inconvenient for the person responsible, it consumes time that would be used in more pressing matters. In this train of thoughts, the proposal of creating an interface to bring a stable, flexible, user friendly and data driven solution to this problem was created. Being the most common operative system for mobile display, the Android API proved to have the best efficient in financing, since it is based on an open source software, and in implementation difficulty since it’s backend development resides in JAVA calls and XML for visual representation...

  16. Data-driven fault detection, isolation and estimation of aircraft gas turbine engine actuator and sensors

    Science.gov (United States)

    Naderi, E.; Khorasani, K.

    2018-02-01

    In this work, a data-driven fault detection, isolation, and estimation (FDI&E) methodology is proposed and developed specifically for monitoring the aircraft gas turbine engine actuator and sensors. The proposed FDI&E filters are directly constructed by using only the available system I/O data at each operating point of the engine. The healthy gas turbine engine is stimulated by a sinusoidal input containing a limited number of frequencies. First, the associated system Markov parameters are estimated by using the FFT of the input and output signals to obtain the frequency response of the gas turbine engine. These data are then used for direct design and realization of the fault detection, isolation and estimation filters. Our proposed scheme therefore does not require any a priori knowledge of the system linear model or its number of poles and zeros at each operating point. We have investigated the effects of the size of the frequency response data on the performance of our proposed schemes. We have shown through comprehensive case studies simulations that desirable fault detection, isolation and estimation performance metrics defined in terms of the confusion matrix criterion can be achieved by having access to only the frequency response of the system at only a limited number of frequencies.

  17. A data-driven decomposition approach to model aerodynamic forces on flapping airfoils

    Science.gov (United States)

    Raiola, Marco; Discetti, Stefano; Ianiro, Andrea

    2017-11-01

    In this work, we exploit a data-driven decomposition of experimental data from a flapping airfoil experiment with the aim of isolating the main contributions to the aerodynamic force and obtaining a phenomenological model. Experiments are carried out on a NACA 0012 airfoil in forward flight with both heaving and pitching motion. Velocity measurements of the near field are carried out with Planar PIV while force measurements are performed with a load cell. The phase-averaged velocity fields are transformed into the wing-fixed reference frame, allowing for a description of the field in a domain with fixed boundaries. The decomposition of the flow field is performed by means of the POD applied on the velocity fluctuations and then extended to the phase-averaged force data by means of the Extended POD approach. This choice is justified by the simple consideration that aerodynamic forces determine the largest contributions to the energetic balance in the flow field. Only the first 6 modes have a relevant contribution to the force. A clear relationship can be drawn between the force and the flow field modes. Moreover, the force modes are closely related (yet slightly different) to the contributions of the classic potential models in literature, allowing for their correction. This work has been supported by the Spanish MINECO under Grant TRA2013-41103-P.

  18. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

    Directory of Open Access Journals (Sweden)

    Rui Sun

    2017-09-01

    Full Text Available The use of Unmanned Aerial Vehicles (UAVs has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  19. Data-driven strategies for robust forecast of continuous glucose monitoring time-series.

    Science.gov (United States)

    Fiorini, Samuele; Martini, Chiara; Malpassi, Davide; Cordera, Renzo; Maggi, Davide; Verri, Alessandro; Barla, Annalisa

    2017-07-01

    Over the past decade, continuous glucose monitoring (CGM) has proven to be a very resourceful tool for diabetes management. To date, CGM devices are employed for both retrospective and online applications. Their use allows to better describe the patients' pathology as well as to achieve a better control of patients' level of glycemia. The analysis of CGM sensor data makes possible to observe a wide range of metrics, such as the glycemic variability during the day or the amount of time spent below or above certain glycemic thresholds. However, due to the high variability of the glycemic signals among sensors and individuals, CGM data analysis is a non-trivial task. Standard signal filtering solutions fall short when an appropriate model personalization is not applied. State-of-the-art data-driven strategies for online CGM forecasting rely upon the use of recursive filters. Each time a new sample is collected, such models need to adjust their parameters in order to predict the next glycemic level. In this paper we aim at demonstrating that the problem of online CGM forecasting can be successfully tackled by personalized machine learning models, that do not need to recursively update their parameters.

  20. The influence of hydrologic residence time on lake carbon cycling dynamics following extreme precipitation events

    Science.gov (United States)

    Jacob A. Zwart; Stephen D. Sebestyen; Christopher T. Solomon; Stuart E. Jones

    2016-01-01

    The frequency and magnitude of extreme events are expected to increase in the future, yet little is known about effects of such events on ecosystem structure and function. We examined how extreme precipitation events affect exports of terrestrial dissolved organic carbon (t-DOC) from watersheds to lakes as well as in-lake heterotrophy in three north-temperate lakes....

  1. 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.

  2. A distributed real-time system for event-driven control and dynamic data acquisition on a fusion plasma experiment

    International Nuclear Information System (INIS)

    Sousa, J.; Combo, A.; Batista, A.; Correia, M.; Trotman, D.; Waterhouse, J.; Varandas, C.A.F.

    2000-01-01

    A distributed real-time trigger and timing system, designed in a tree-type topology and implemented in VME and CAMAC versions, has been developed for a magnetic confinement fusion experiment. It provides sub-microsecond time latencies for the transport of small data objects allowing event-driven discharge control with failure counteraction, dynamic pre-trigger sampling and event recording as well as accurate simultaneous triggers and synchronism on all nodes with acceptable optimality and predictability of timeliness. This paper describes the technical characteristics of the hardware components (central unit composed by one or more reflector crates, event and synchronism reflector cards, event and pulse node module, fan-out and fan-in modules) as well as software for both tests and integration on a global data acquisition system. The results of laboratory operation for several configurations and the overall performance of the system are presented and analysed

  3. Competitive Semantic Memory Retrieval: Temporal Dynamics Revealed by Event-Related Potentials.

    Directory of Open Access Journals (Sweden)

    Robin Hellerstedt

    Full Text Available Memories compete for retrieval when they are related to a common retrieval cue. Previous research has shown that retrieval of a target memory may lead to subsequent retrieval-induced forgetting (RIF of currently irrelevant competing memories. In the present study, we investigated the time course of competitive semantic retrieval and examined the neurocognitive mechanisms underlying RIF. We contrasted two theoretical accounts of RIF by examining a critical aspect of this memory phenomenon, namely the extent to which it depends on successful retrieval of the target memory. Participants first studied category-exemplar word-pairs (e.g. Fruit-Apple. Next, we recorded electrophysiological measures of brain activity while the participants performed a competitive semantic cued-recall task. In this task, the participants were provided with the studied categories but they were instructed to retrieve other unstudied exemplars (e.g. Fruit-Ma__?. We investigated the event-related potential (ERP correlates of retrieval success by comparing ERPs from successful and failed retrieval trials. To isolate the ERP correlates of continuous retrieval attempts from the ERP correlates of retrieval success, we included an impossible retrieval condition, with incompletable word-stem cues (Drinks-Wy__ and compared it with a non-retrieval presentation baseline condition (Occupation-Dentist. The participants' memory for all the studied exemplars was tested in the final phase of the experiment. Taken together, the behavioural results suggest that RIF is independent of target retrieval. Beyond investigating the mechanisms underlying RIF, the present study also elucidates the temporal dynamics of semantic cued-recall by isolating the ERP correlates of retrieval attempt and retrieval success. The ERP results revealed that retrieval attempt is reflected in a late posterior negativity, possibly indicating construction of candidates for completing the word-stem cue and retrieval

  4. Competitive Semantic Memory Retrieval: Temporal Dynamics Revealed by Event-Related Potentials

    Science.gov (United States)

    Hellerstedt, Robin; Johansson, Mikael

    2016-01-01

    Memories compete for retrieval when they are related to a common retrieval cue. Previous research has shown that retrieval of a target memory may lead to subsequent retrieval-induced forgetting (RIF) of currently irrelevant competing memories. In the present study, we investigated the time course of competitive semantic retrieval and examined the neurocognitive mechanisms underlying RIF. We contrasted two theoretical accounts of RIF by examining a critical aspect of this memory phenomenon, namely the extent to which it depends on successful retrieval of the target memory. Participants first studied category-exemplar word-pairs (e.g. Fruit—Apple). Next, we recorded electrophysiological measures of brain activity while the participants performed a competitive semantic cued-recall task. In this task, the participants were provided with the studied categories but they were instructed to retrieve other unstudied exemplars (e.g. Fruit—Ma__?). We investigated the event-related potential (ERP) correlates of retrieval success by comparing ERPs from successful and failed retrieval trials. To isolate the ERP correlates of continuous retrieval attempts from the ERP correlates of retrieval success, we included an impossible retrieval condition, with incompletable word-stem cues (Drinks—Wy__) and compared it with a non-retrieval presentation baseline condition (Occupation—Dentist). The participants’ memory for all the studied exemplars was tested in the final phase of the experiment. Taken together, the behavioural results suggest that RIF is independent of target retrieval. Beyond investigating the mechanisms underlying RIF, the present study also elucidates the temporal dynamics of semantic cued-recall by isolating the ERP correlates of retrieval attempt and retrieval success. The ERP results revealed that retrieval attempt is reflected in a late posterior negativity, possibly indicating construction of candidates for completing the word-stem cue and retrieval

  5. Competitive Semantic Memory Retrieval: Temporal Dynamics Revealed by Event-Related Potentials.

    Science.gov (United States)

    Hellerstedt, Robin; Johansson, Mikael

    2016-01-01

    Memories compete for retrieval when they are related to a common retrieval cue. Previous research has shown that retrieval of a target memory may lead to subsequent retrieval-induced forgetting (RIF) of currently irrelevant competing memories. In the present study, we investigated the time course of competitive semantic retrieval and examined the neurocognitive mechanisms underlying RIF. We contrasted two theoretical accounts of RIF by examining a critical aspect of this memory phenomenon, namely the extent to which it depends on successful retrieval of the target memory. Participants first studied category-exemplar word-pairs (e.g. Fruit-Apple). Next, we recorded electrophysiological measures of brain activity while the participants performed a competitive semantic cued-recall task. In this task, the participants were provided with the studied categories but they were instructed to retrieve other unstudied exemplars (e.g. Fruit-Ma__?). We investigated the event-related potential (ERP) correlates of retrieval success by comparing ERPs from successful and failed retrieval trials. To isolate the ERP correlates of continuous retrieval attempts from the ERP correlates of retrieval success, we included an impossible retrieval condition, with incompletable word-stem cues (Drinks-Wy__) and compared it with a non-retrieval presentation baseline condition (Occupation-Dentist). The participants' memory for all the studied exemplars was tested in the final phase of the experiment. Taken together, the behavioural results suggest that RIF is independent of target retrieval. Beyond investigating the mechanisms underlying RIF, the present study also elucidates the temporal dynamics of semantic cued-recall by isolating the ERP correlates of retrieval attempt and retrieval success. The ERP results revealed that retrieval attempt is reflected in a late posterior negativity, possibly indicating construction of candidates for completing the word-stem cue and retrieval monitoring

  6. Flux transfer events at the dayside magnetopause: Transient reconnection or magnetosheath dynamic pressure pulses?

    International Nuclear Information System (INIS)

    Lockwood, M.

    1991-01-01

    The suggestion is discussed that characteristic particle and field signatures at the dayside magnetopause, termed flux transfer events, are, in at least some cases, due to transient solar wind and/or magnetosheath dynamic pressure increases, rather than time-dependent magnetic reconnection. It is found that most individual cases of FTEs observed by a single spacecraft can, at least qualitatively, be explained by the pressure pulse model, provided a few rather unsatisfactory features of the predictions are explained in terms of measurement uncertainties. The most notable exceptions to this are some two-regime observations made by two satellites simultaneously, one on either side of the magnetopause. However, this configuration has not been frequently achieved for sufficient time, such observations are rare, and the relevant tests are still not conclusive. The strongest evidence that FTEs are produced by magnetic reconnection is the dependence of their occurence on the north-south component of the interplanetary magnetic field (IMF) or of the magnetosheath field. The pressure pulse model provides an explanation for this dependence in the case of magnetosheath FTEs, but does not apply to magnetosphere FTEs. The only surveys of magnetosphere FTEs have not employed the simultaneous IMF, but have shown that their occurence is strongly dependent on the north-south component of the magnetosheath field, as observed earlier/later on the same magnetopause crossing. This paper employs statistics on the variability of the IMF orientation to investigate the effects of IMF changes between the times of the magnetosheath and FTE observations. It is shown that the previously published results are consistent with magnetospheric FTEs being entirely absent when the magentosheath field is northward

  7. Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution.

    Science.gov (United States)

    Ferrari, Ulisse

    2016-08-01

    Maximum entropy models provide the least constrained probability distributions that reproduce statistical properties of experimental datasets. In this work we characterize the learning dynamics that maximizes the log-likelihood in the case of large but finite datasets. We first show how the steepest descent dynamics is not optimal as it is slowed down by the inhomogeneous curvature of the model parameters' space. We then provide a way for rectifying this space which relies only on dataset properties and does not require large computational efforts. We conclude by solving the long-time limit of the parameters' dynamics including the randomness generated by the systematic use of Gibbs sampling. In this stochastic framework, rather than converging to a fixed point, the dynamics reaches a stationary distribution, which for the rectified dynamics reproduces the posterior distribution of the parameters. We sum up all these insights in a "rectified" data-driven algorithm that is fast and by sampling from the parameters' posterior avoids both under- and overfitting along all the directions of the parameters' space. Through the learning of pairwise Ising models from the recording of a large population of retina neurons, we show how our algorithm outperforms the steepest descent method.

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

    Directory of Open Access Journals (Sweden)

    Muhammad Murtadha Othman

    2017-06-01

    Full Text Available With the advent of advanced technology in smart grid, the implementation of renewable energy in a stressed and complicated power system operation, aggravated by a competitive electricity market and critical system contingencies, this will inflict higher probabilities of the occurrence of a severe dynamic power system blackout. 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 system contingencies such as the rapid increase in total loading condition and sensitive initial transmission line tripping. An extensive analysis of dynamic power system blackout has been carried out in a case study of the following power systems: IEEE RTS-79 and IEEE RTS-96. The findings have shown that the total loading conditions and sensitive transmission lines need to be given full attention by the utility to prevent the occurrence of dynamic power system blackout.

  9. Coronal Fine Structure in Dynamic Events Observed by Hi-C

    Science.gov (United States)

    Winebarger, Amy; Schuler, Timothy

    2013-01-01

    The High-Resolution Coronal Imager (Hi-C) flew aboard a NASA sounding rocket on 2012 July 11 and captured roughly 345 s of high spatial and temporal resolution images of the solar corona in a narrowband 193 Angstrom channel. We have analyzed the fluctuations in intensity of Active Region 11520. We selected events based on a lifetime greater than 11 s (two Hi-C frames) and intensities greater than a threshold determined from the photon and readout noise. We compare the Hi-C events with those determined from AIA. We find that HI-C detects shorter and smaller events than AIA. We also find that the intensity increase in the Hi-C events is approx. 3 times greater than the intensity increase in the AIA events we conclude the events are related to linear sub-structure that is unresolved by AIA

  10. The dynamics of daily events and well-being across cultures: when less is more.

    Science.gov (United States)

    Oishi, Shigehiro; Diener, Ed; Choi, Dong-Won; Kim-Prieto, Chu; Choi, Incheol

    2007-10-01

    The authors examined cultural and individual differences in the relation between daily events and daily satisfaction. In a preliminary study, they established cross-cultural equivalence of 50 daily events. In the main study, participants in the United States, Korea, and Japan completed daily surveys on the 50 events and daily satisfaction for 21 days. The multilevel random coefficient model analyses showed that (a) the within-person association between positive events and daily satisfaction was significantly stronger among Asian American, Korean, and Japanese participants than among European American participants and (b) the within-person association between positive events and daily satisfaction was significantly weaker among individuals high in global life satisfaction than among those low in global life satisfaction. The findings demonstrate a weaker effect of positive events on daily well-being among individuals and cultures high in global well-being. (PsycINFO Database Record (c) 2007 APA, all rights reserved).

  11. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

    Science.gov (United States)

    Hao, Ming; Bryant, Stephen H; Wang, Yanli

    2018-02-06

    While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

  12. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Directory of Open Access Journals (Sweden)

    A. Elshorbagy

    2010-10-01

    Full Text Available In this second part of the two-part paper, the data driven modeling (DDM experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs, genetic programming (GP, evolutionary polynomial regression (EPR, Support vector machines (SVM, M5 model trees (M5, K-nearest neighbors (K-nn, and multiple linear regression (MLR techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it

  13. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Science.gov (United States)

    Elshorbagy, A.; Corzo, G.; Srinivasulu, S.; Solomatine, D. P.

    2010-10-01

    In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it should

  14. Data-driven analysis of simultaneous EEG/fMRI reveals neurophysiological phenotypes of impulse control.

    Science.gov (United States)

    Schmüser, Lena; Sebastian, Alexandra; Mobascher, Arian; Lieb, Klaus; Feige, Bernd; Tüscher, Oliver

    2016-09-01

    Response inhibition is the ability to suppress inadequate but prepotent or ongoing response tendencies. A fronto-striatal network is involved in these processes. Between-subject differences in the intra-individual variability have been suggested to constitute a key to pathological processes underlying impulse control disorders. Single-trial EEG/fMRI analysis allows to increase sensitivity for inter-individual differences by incorporating intra-individual variability. Thirty-eight healthy subjects performed a visual Go/Nogo task during simultaneous EEG/fMRI. Of 38 healthy subjects, 21 subjects reliably showed Nogo-related ICs (Nogo-IC-positive) while 17 subjects (Nogo-IC-negative) did not. Comparing both groups revealed differences on various levels: On trait level, Nogo-IC-negative subjects scored higher on questionnaires regarding attention deficit/hyperactivity disorder; on a behavioral level, they displayed slower response times (RT) and higher intra-individual RT variability while both groups did not differ in their inhibitory performance. On the neurophysiological level, Nogo-IC-negative subjects showed a hyperactivation of left inferior frontal cortex/insula and left putamen as well as significantly reduced P3 amplitudes. Thus, a data-driven approach for IC classification and the resulting presence or absence of early Nogo-specific ICs as criterion for group selection revealed group differences at behavioral and neurophysiological levels. This may indicate electrophysiological phenotypes characterized by inter-individual variations of neural and behavioral correlates of impulse control. We demonstrated that the inter-individual difference in an electrophysiological correlate of response inhibition is correlated with distinct, potentially compensatory neural activity. This may suggest the existence of electrophysiologically dissociable phenotypes of behavioral and neural motor response inhibition with the Nogo-IC-positive phenotype possibly providing

  15. A data-driven approach for denoising GNSS position time series

    Science.gov (United States)

    Li, Yanyan; Xu, Caijun; Yi, Lei; Fang, Rongxin

    2017-12-01

    Global navigation satellite system (GNSS) datasets suffer from common mode error (CME) and other unmodeled errors. To decrease the noise level in GNSS positioning, we propose a new data-driven adaptive multiscale denoising method in this paper. Both synthetic and real-world long-term GNSS datasets were employed to assess the performance of the proposed method, and its results were compared with those of stacking filtering, principal component analysis (PCA) and the recently developed multiscale multiway PCA. It is found that the proposed method can significantly eliminate the high-frequency white noise and remove the low-frequency CME. Furthermore, the proposed method is more precise for denoising GNSS signals than the other denoising methods. For example, in the real-world example, our method reduces the mean standard deviation of the north, east and vertical components from 1.54 to 0.26, 1.64 to 0.21 and 4.80 to 0.72 mm, respectively. Noise analysis indicates that for the original signals, a combination of power-law plus white noise model can be identified as the best noise model. For the filtered time series using our method, the generalized Gauss-Markov model is the best noise model with the spectral indices close to - 3, indicating that flicker walk noise can be identified. Moreover, the common mode error in the unfiltered time series is significantly reduced by the proposed method. After filtering with our method, a combination of power-law plus white noise model is the best noise model for the CMEs in the study region.

  16. Full field reservoir modeling of shale assets using advanced data-driven analytics

    Directory of Open Access Journals (Sweden)

    Soodabeh Esmaili

    2016-01-01

    Full Text Available Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems - induced or natural leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called “hard data” directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The “hard data” refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of “soft data” (non-measured, interpretive data such as frac length, width, height and conductivity in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.

  17. Assigning clinical codes with data-driven concept representation on Dutch clinical free text.

    Science.gov (United States)

    Scheurwegs, Elyne; Luyckx, Kim; Luyten, Léon; Goethals, Bart; Daelemans, Walter

    2017-05-01

    Clinical codes are used for public reporting purposes, are fundamental to determining public financing for hospitals, and form the basis for reimbursement claims to insurance providers. They are assigned to a patient stay to reflect the diagnosis and performed procedures during that stay. This paper aims to enrich algorithms for automated clinical coding by taking a data-driven approach and by using unsupervised and semi-supervised techniques for the extraction of multi-word expressions that convey a generalisable medical meaning (referred to as concepts). Several methods for extracting concepts from text are compared, two of which are constructed from a large unannotated corpus of clinical free text. A distributional semantic model (i.c. the word2vec skip-gram model) is used to generalize over concepts and retrieve relations between them. These methods are validated on three sets of patient stay data, in the disease areas of urology, cardiology, and gastroenterology. The datasets are in Dutch, which introduces a limitation on available concept definitions from expert-based ontologies (e.g. UMLS). The results show that when expert-based knowledge in ontologies is unavailable, concepts derived from raw clinical texts are a reliable alternative. Both concepts derived from raw clinical texts perform and concepts derived from expert-created dictionaries outperform a bag-of-words approach in clinical code assignment. Adding features based on tokens that appear in a semantically similar context has a positive influence for predicting diagnostic codes. Furthermore, the experiments indicate that a distributional semantics model can find relations between semantically related concepts in texts but also introduces erroneous and redundant relations, which can undermine clinical coding performance. Copyright © 2017. Published by Elsevier Inc.

  18. WaveSeq: a novel data-driven method of detecting histone modification enrichments using wavelets.

    Directory of Open Access Journals (Sweden)

    Apratim Mitra

    Full Text Available BACKGROUND: Chromatin immunoprecipitation followed by next-generation sequencing is a genome-wide analysis technique that can be used to detect various epigenetic phenomena such as, transcription factor binding sites and histone modifications. Histone modification profiles can be either punctate or diffuse which makes it difficult to distinguish regions of enrichment from background noise. With the discovery of histone marks having a wide variety of enrichment patterns, there is an urgent need for analysis methods that are robust to various data characteristics and capable of detecting a broad range of enrichment patterns. RESULTS: To address these challenges we propose WaveSeq, a novel data-driven method of detecting regions of significant enrichment in ChIP-Seq data. Our approach utilizes the wavelet transform, is free of distributional assumptions and is robust to diverse data characteristics such as low signal-to-noise ratios and broad enrichment patterns. Using publicly available datasets we showed that WaveSeq compares favorably with other published methods, exhibiting high sensitivity and precision for both punctate and diffuse enrichment regions even in the absence of a control data set. The application of our algorithm to a complex histone modification data set helped make novel functional discoveries which further underlined its utility in such an experimental setup. CONCLUSIONS: WaveSeq is a highly sensitive method capable of accurate identification of enriched regions in a broad range of data sets. WaveSeq can detect both narrow and broad peaks with a high degree of accuracy even in low signal-to-noise ratio data sets. WaveSeq is also suited for application in complex experimental scenarios, helping make biologically relevant functional discoveries.

  19. Data Science and its Relationship to Big Data and Data-Driven Decision Making.

    Science.gov (United States)

    Provost, Foster; Fawcett, Tom

    2013-03-01

    Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

  20. Practical aspects of data-driven motion correction approach for brain SPECT

    International Nuclear Information System (INIS)

    Kyme, A.Z.; Hutton, B.F.; Hatton, R.L.; Skerrett, D.; Barnden, L.

    2002-01-01

    Full text: Patient motion can cause image artifacts in SPECT despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward-projections. By optimising the orientation of a partial reconstruction, parameters can be obtained for each misaligned projection and applied to update this volume using a 3D reconstruction algorithm. Phantom validation was performed to explore practical aspects of this approach. Noisy projection datasets simulating a patient undergoing at least one fully 3D movement during acquisition were compiled from various projections of the digital Hoffman brain phantom. Motion correction was then applied to the reconstructed studies. Correction success was assessed visually and quantitatively. Resilience with respect to subset order and missing data in the reconstruction and updating stages, detector geometry considerations, and the need for implementing an iterated correction were assessed in the process. Effective correction of the corrupted studies was achieved. Visually, artifactual regions in the reconstructed slices were suppressed and/or removed. Typically the ratio of mean square difference between the corrected and reference studies compared to that between the corrupted and reference studies was > 2. Although components of the motions are missed using a single-head implementation, improvement was still evident in the correction. The need for multiple iterations in the approach was small due to the bulk of misalignment errors being corrected in the first pass. Dispersion of subsets for reconstructing and updating the partial reconstruction appears to give optimal correction. Further validation is underway using triple-head physical phantom data. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

  1. Management and Nonlinear Analysis of Disinfection System of Water Distribution Networks Using Data Driven Methods

    Directory of Open Access Journals (Sweden)

    Mohammad Zounemat-Kermani

    2018-03-01

    Full Text Available Chlorination unit is widely used to supply safe drinking water and removal of pathogens from water distribution networks. Data-driven approach is one appropriate method for analyzing performance of chlorine in water supply network. In this study, multi-layer perceptron neural network (MLP with three training algorithms (gradient descent, conjugate gradient and BFGS and support vector machine (SVM with RBF kernel function were used to predict the concentration of residual chlorine in water supply networks of Ahmadabad Dafeh and Ahruiyeh villages in Kerman Province. Daily data including discharge (flow, chlorine consumption and residual chlorine were employed from the beginning of 1391 Hijri until the end of 1393 Hijri (for 3 years. To assess the performance of studied models, the criteria such as Nash-Sutcliffe efficiency (NS, root mean square error (RMSE, mean absolute percentage error (MAPE and correlation coefficient (CORR were used that in best modeling situation were 0.9484, 0.0255, 1.081, and 0.974 respectively which resulted from BFGS algorithm. The criteria indicated that MLP model with BFGS and conjugate gradient algorithms were better than all other models in 90 and 10 percent of cases respectively; while the MLP model based on gradient descent algorithm and the SVM model were better in none of the cases. According to the results of this study, proper management of chlorine concentration can be implemented by predicted values of residual chlorine in water supply network. Thus, decreased performance of perceptron network and support vector machine in water supply network of Ahruiyeh in comparison to Ahmadabad Dafeh can be inferred from improper management of chlorination.

  2. Simulation of shallow groundwater levels: Comparison of a data-driven and a conceptual model

    Science.gov (United States)

    Fahle, Marcus; Dietrich, Ottfried; Lischeid, Gunnar

    2015-04-01

    Despite an abundance of models aimed at simulating shallow groundwater levels, application of such models is often hampered by a lack of appropriate input data. Difficulties especially arise with regard to soil data, which are typically hard to obtain and prone to spatial variability, eventually leading to uncertainties in the model results. Modelling approaches relying entirely on easily measured quantities are therefore an alternative to encourage the applicability of models. We present and compare two models for calculating 1-day-ahead predictions of the groundwater level that are only based on measurements of potential evapotranspiration, precipitation and groundwater levels. The first model is a newly developed conceptual model that is parametrized using the White method (which estimates the actual evapotranspiration on basis of diurnal groundwater fluctuations) and a rainfall-response ratio. Inverted versions of the two latter approaches are then used to calculate the predictions of the groundwater level. Furthermore, as a completely data-driven alternative, a simple feed-forward multilayer perceptron neural network was trained based on the same inputs and outputs. Data of 4 growing periods (April to October) from a study site situated in the Spreewald wetland in North-east Germany were taken to set-up the models and compare their performance. In addition, response surfaces that relate model outputs to combinations of different input variables are used to reveal those aspects in which the two approaches coincide and those in which they differ. Finally, it will be evaluated whether the conceptual approach can be enhanced by extracting knowledge of the neural network. This is done by replacing in the conceptual model the default function that relates groundwater recharge and groundwater level, which is assumed to be linear, by the non-linear function extracted from the neural network.

  3. Data Driven Trigger Design and Analysis for the NOvA Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Kurbanov, Serdar [Univ. of Virginia, Charlottesville, VA (United States)

    2016-01-01

    This thesis primarily describes analysis related to studying the Moon shadow with cosmic rays, an analysis using upward-going muons trigger data, and other work done as part of MSc thesis work conducted at Fermi National Laboratory. While at Fermilab I made hardware and software contributions to two experiments - NOvA and Mu2e. NOvA is a neutrino experiment with the primary goal of measuring parameters related to neutrino oscillation. This is a running experiment, so it's possible to provide analysis of real beam and cosmic data. Most of this work was related to the Data-Driven Trigger (DDT) system of NOvA. The results of the Upward-Going muon analysis was presented at ICHEP in August 2016. The analysis demonstrates the proof of principle for a low-mass dark matter search. Mu2e is an experiment currently being built at Fermilab. Its primary goal is to detect the hypothetical neutrinoless conversion from a muon into an electron. I contributed to the production and tests of Cathode Strip Chambers (CSCs) which are required for testing the Cosmic Ray Veto (CRV) system for the experiment. This contribution is described in the last chapter along with a short description of the technical work provided for the DDT system of the NOvA experiment. All of the work described in this thesis will be extended by the next generation of UVA graduate students and postdocs as new data is collected by the experiment. I hope my eorts of have helped lay the foundation for many years of beautiful results from Mu2e and NOvA.

  4. Data-driven nutrient analysis and reality check: Human inputs, catchment delivery and management effects

    Science.gov (United States)

    Destouni, G.

    2017-12-01

    Measures for mitigating nutrient loads to aquatic ecosystems should have observable effects, e.g, in the Baltic region after joint first periods of nutrient management actions under the Baltic Sea Action Plan (BASP; since 2007) and the EU Water Framework Directive (WFD; since 2009). Looking for such observable effects, all openly available water and nutrient monitoring data since 2003 are compiled and analyzed for Sweden as a case study. Results show that hydro-climatically driven water discharge dominates the determination of waterborne loads of both phosphorus and nitrogen. Furthermore, the nutrient loads and water discharge are all similarly well correlated with the ecosystem status classification of Swedish water bodies according to the WFD. Nutrient concentrations, which are hydro-climatically correlated and should thus reflect human effects better than loads, have changed only slightly over the study period (2003-2013) and even increased in moderate-to-bad status waters, where the WFD and BSAP jointly target nutrient decreases. These results indicate insufficient distinction and mitigation of human-driven nutrient components by the internationally harmonized applications of both the WFD and the BSAP. Aiming for better general identification of such components, nutrient data for the large transboundary catchments of the Baltic Sea and the Sava River are compared. The comparison shows cross-regional consistency in nutrient relationships to driving hydro-climatic conditions (water discharge) for nutrient loads, and socio-economic conditions (population density and farmland share) for nutrient concentrations. A data-driven screening methodology is further developed for estimating nutrient input and retention-delivery in catchments. Its first application to nested Sava River catchments identifies characteristic regional values of nutrient input per area and relative delivery, and hotspots of much larger inputs, related to urban high-population areas.

  5. A review on data-driven fault severity assessment in rolling bearings

    Science.gov (United States)

    Cerrada, Mariela; Sánchez, René-Vinicio; Li, Chuan; Pacheco, Fannia; Cabrera, Diego; Valente de Oliveira, José; Vásquez, Rafael E.

    2018-01-01

    Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field.

  6. Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

    Energy Technology Data Exchange (ETDEWEB)

    Leistedt, Boris; Hogg, David W., E-mail: boris.leistedt@nyu.edu, E-mail: david.hogg@nyu.edu [Center for Cosmology and Particle Physics, Department of Physics, New York University, New York, NY 10003 (United States)

    2017-03-20

    We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

  7. Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

    International Nuclear Information System (INIS)

    Leistedt, Boris; Hogg, David W.

    2017-01-01

    We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

  8. Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection

    Directory of Open Access Journals (Sweden)

    Yan Pei

    2018-03-01

    Full Text Available Wind turbine yaw control plays an important role in increasing the wind turbine production and also in protecting the wind turbine. Accurate measurement of yaw angle is the basis of an effective wind turbine yaw controller. The accuracy of yaw angle measurement is affected significantly by the problem of zero-point shifting. Hence, it is essential to evaluate the zero-point shifting error on wind turbines on-line in order to improve the reliability of yaw angle measurement in real time. Particularly, qualitative evaluation of the zero-point shifting error could be useful for wind farm operators to realize prompt and cost-effective maintenance on yaw angle sensors. In the aim of qualitatively evaluating the zero-point shifting error, the yaw angle sensor zero-point shifting fault is firstly defined in this paper. A data-driven method is then proposed to detect the zero-point shifting fault based on Supervisory Control and Data Acquisition (SCADA data. The zero-point shifting fault is detected in the proposed method by analyzing the power performance under different yaw angles. The SCADA data are partitioned into different bins according to both wind speed and yaw angle in order to deeply evaluate the power performance. An indicator is proposed in this method for power performance evaluation under each yaw angle. The yaw angle with the largest indicator is considered as the yaw angle measurement error in our work. A zero-point shifting fault would trigger an alarm if the error is larger than a predefined threshold. Case studies from several actual wind farms proved the effectiveness of the proposed method in detecting zero-point shifting fault and also in improving the wind turbine performance. Results of the proposed method could be useful for wind farm operators to realize prompt adjustment if there exists a large error of yaw angle measurement.

  9. Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

    Science.gov (United States)

    Chen, Daizhuo; Fraiberger, Samuel P; Moakler, Robert; Provost, Foster

    2017-09-01

    Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from "Likes" on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the "cloaking device"-a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users.

  10. Disruption of functional networks in dyslexia: A whole-brain, data-driven analysis of connectivity

    Science.gov (United States)

    Finn, Emily S.; Shen, Xilin; Holahan, John M.; Scheinost, Dustin; Lacadie, Cheryl; Papademetris, Xenophon; Shaywitz, Sally E.; Shaywitz, Bennett A.; Constable, R. Todd

    2013-01-01

    Background Functional connectivity analyses of fMRI data are a powerful tool for characterizing brain networks and how they are disrupted in neural disorders. However, many such analyses examine only one or a small number of a priori seed regions. Studies that consider the whole brain frequently rely on anatomic atlases to define network nodes, which may result in mixing distinct activation timecourses within a single node. Here, we improve upon previous methods by using a data-driven brain parcellation to compare connectivity profiles of dyslexic (DYS) versus non-impaired (NI) readers in the first whole-brain functional connectivity analysis of dyslexia. Methods Whole-brain connectivity was assessed in children (n = 75; 43 NI, 32 DYS) and adult (n = 104; 64 NI, 40 DYS) readers. Results Compared to NI readers, DYS readers showed divergent connectivity within the visual pathway and between visual association areas and prefrontal attention areas; increased right-hemisphere connectivity; reduced connectivity in the visual word-form area (part of the left fusiform gyrus specialized for printed words); and persistent connectivity to anterior language regions around the inferior frontal gyrus. Conclusions Together, findings suggest that NI readers are better able to integrate visual information and modulate their attention to visual stimuli, allowing them to recognize words based on their visual properties, while DYS readers recruit altered reading circuits and rely on laborious phonology-based “sounding out” strategies into adulthood. These results deepen our understanding of the neural basis of dyslexia and highlight the importance of synchrony between diverse brain regions for successful reading. PMID:24124929

  11. Improving Spoken Language Outcomes for Children With Hearing Loss: Data-driven Instruction.

    Science.gov (United States)

    Douglas, Michael

    2016-02-01

    To assess the effects of data-driven instruction (DDI) on spoken language outcomes of children with cochlear implants and hearing aids. Retrospective, matched-pairs comparison of post-treatment speech/language data of children who did and did not receive DDI. Private, spoken-language preschool for children with hearing loss. Eleven matched pairs of children with cochlear implants who attended the same spoken language preschool. Groups were matched for age of hearing device fitting, time in the program, degree of predevice fitting hearing loss, sex, and age at testing. Daily informal language samples were collected and analyzed over a 2-year period, per preschool protocol. Annual informal and formal spoken language assessments in articulation, vocabulary, and omnibus language were administered at the end of three time intervals: baseline, end of year one, and end of year two. The primary outcome measures were total raw score performance of spontaneous utterance sentence types and syntax element use as measured by the Teacher Assessment of Spoken Language (TASL). In addition, standardized assessments (the Clinical Evaluation of Language Fundamentals--Preschool Version 2 (CELF-P2), the Expressive One-Word Picture Vocabulary Test (EOWPVT), the Receptive One-Word Picture Vocabulary Test (ROWPVT), and the Goldman-Fristoe Test of Articulation 2 (GFTA2)) were also administered and compared with the control group. The DDI group demonstrated significantly higher raw scores on the TASL each year of the study. The DDI group also achieved statistically significant higher scores for total language on the CELF-P and expressive vocabulary on the EOWPVT, but not for articulation nor receptive vocabulary. Post-hoc assessment revealed that 78% of the students in the DDI group achieved scores in the average range compared with 59% in the control group. The preliminary results of this study support further investigation regarding DDI to investigate whether this method can consistently

  12. STUDY OF THE POYNTING FLUX IN ACTIVE REGION 10930 USING DATA-DRIVEN MAGNETOHYDRODYNAMIC SIMULATION

    International Nuclear Information System (INIS)

    Fan, Y. L.; Wang, H. N.; He, H.; Zhu, X. S.

    2011-01-01

    Powerful solar flares are closely related to the evolution of magnetic field configuration on the photosphere. We choose the Poynting flux as a parameter in the study of magnetic field changes. We use time-dependent multidimensional MHD simulations around a flare occurrence to generate the results, with the temporal variation of the bottom boundary conditions being deduced from the projected normal characteristic method. By this method, the photospheric magnetogram could be incorporated self-consistently as the bottom condition of data-driven simulations. The model is first applied to a simulation datum produced by an emerging magnetic flux rope as a test case. Then, the model is used to study NOAA AR 10930, which has an X3.4 flare, the data of which has been obtained by the Hinode/Solar Optical Telescope on 2006 December 13. We compute the magnitude of Poynting flux (S total ), radial Poynting flux (S z ), a proxy for ideal radial Poynting flux (S proxy ), Poynting flux due to plasma surface motion (S sur ), and Poynting flux due to plasma emergence (S emg ) and analyze their extensive properties in four selected areas: the whole sunspot, the positive sunspot, the negative sunspot, and the strong-field polarity inversion line (SPIL) area. It is found that (1) the S total , S z , and S proxy parameters show similar behaviors in the whole sunspot area and in the negative sunspot area. The evolutions of these three parameters in the positive area and the SPIL area are more volatile because of the effect of sunspot rotation and flux emergence. (2) The evolution of S sur is largely influenced by the process of sunspot rotation, especially in the positive sunspot. The evolution of S emg is greatly affected by flux emergence, especially in the SPIL area.

  13. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.

    Science.gov (United States)

    Wang, Shuo; Zhou, Mu; Liu, Zaiyi; Liu, Zhenyu; Gu, Dongsheng; Zang, Yali; Dong, Di; Gevaert, Olivier; Tian, Jie

    2017-08-01

    Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%. Copyright © 2017. Published by Elsevier B.V.

  14. Telling Anthropocene Tales: Localizing the impacts of global change using data-driven story maps

    Science.gov (United States)

    Mychajliw, A.; Hadly, E. A.

    2016-12-01

    Navigating the Anthropocene requires innovative approaches for generating scientific knowledge and for its communication outside academia. The global, synergistic nature of the environmental challenges we face - climate change, human population growth, biodiversity loss, pollution, invasive species and diseases - highlight the need for public outreach strategies that incorporate multiple scales and perspectives in an easily understandable and rapidly accessible format. Data-driven story-telling maps are optimal in that they can display variable geographic scales and their intersections with the environmental challenges relevant to both scientists and non-scientists. Maps are a powerful way to present complex data to all stakeholders. We present an overview of best practices in community-engaged scientific story-telling and data translation for policy-makers by reviewing three Story Map projects that map the geographic impacts of global change across multiple spatial and policy scales: the entire United States, the state of California, and the town of Pescadero, California. We document a chain of translation from a primary scientific manscript to a policy document (Scientific Consensus Statement on Maintaining Humanity's Life Support Systems in the 21st Century) to a set of interactive ArcGIS Story Maps. We discuss the widening breadth of participants (students, community members) and audiences (White House, Governor's Office of California, California Congressional Offices, general public) involved. We highlight how scientists, through careful curation of popular news media articles and stakeholder interviews, can co-produce these communication modules with community partners such as non-governmental organizations and government agencies. The placement of scientific and citizen's everyday knowledge of global change into an appropriate geographic context allows for effective dissemination by political units such as congressional districts and agency management units

  15. Data-Driven Neural Network Model for Robust Reconstruction of Automobile Casting

    Science.gov (United States)

    Lin, Jinhua; Wang, Yanjie; Li, Xin; Wang, Lu

    2017-09-01

    In computer vision system, it is a challenging task to robustly reconstruct complex 3D geometries of automobile castings. However, 3D scanning data is usually interfered by noises, the scanning resolution is low, these effects normally lead to incomplete matching and drift phenomenon. In order to solve these problems, a data-driven local geometric learning model is proposed to achieve robust reconstruction of automobile casting. In order to relieve the interference of sensor noise and to be compatible with incomplete scanning data, a 3D convolution neural network is established to match the local geometric features of automobile casting. The proposed neural network combines the geometric feature representation with the correlation metric function to robustly match the local correspondence. We use the truncated distance field(TDF) around the key point to represent the 3D surface of casting geometry, so that the model can be directly embedded into the 3D space to learn the geometric feature representation; Finally, the training labels is automatically generated for depth learning based on the existing RGB-D reconstruction algorithm, which accesses to the same global key matching descriptor. The experimental results show that the matching accuracy of our network is 92.2% for automobile castings, the closed loop rate is about 74.0% when the matching tolerance threshold τ is 0.2. The matching descriptors performed well and retained 81.6% matching accuracy at 95% closed loop. For the sparse geometric castings with initial matching failure, the 3D matching object can be reconstructed robustly by training the key descriptors. Our method performs 3D reconstruction robustly for complex automobile castings.

  16. The Application of Cyber Physical System for Thermal Power Plants: Data-Driven Modeling

    Directory of Open Access Journals (Sweden)

    Yongping Yang

    2018-03-01

    Full Text Available Optimal operation of energy systems plays an important role to enhance their lifetime security and efficiency. The determination of optimal operating strategies requires intelligent utilization of massive data accumulated during operation or prediction. The investigation of these data solely without combining physical models may run the risk that the established relationships between inputs and outputs, the models which reproduce the behavior of the considered system/component in a wide range of boundary conditions, are invalid for certain boundary conditions, which never occur in the database employed. Therefore, combining big data with physical models via cyber physical systems (CPS is of great importance to derive highly-reliable and -accurate models and becomes more and more popular in practical applications. In this paper, we focus on the description of a systematic method to apply CPS to the performance analysis and decision making of thermal power plants. We proposed a general procedure of CPS with both offline and online phases for its application to thermal power plants and discussed the corresponding methods employed to support each sub-procedure. As an example, a data-driven model of turbine island of an existing air-cooling based thermal power plant is established with the proposed procedure and demonstrates its practicality, validity and flexibility. To establish such model, the historical operating data are employed in the cyber layer for modeling and linking each physical component. The decision-making procedure of optimal frequency of air-cooling condenser is also illustrated to show its applicability of online use. It is concluded that the cyber physical system with the data mining technique is effective and promising to facilitate the real-time analysis and control of thermal power plants.

  17. Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology

    Science.gov (United States)

    Moosavi, V.; Aschwanden, G.; Velasco, E.

    2015-09-01

    Finding the number and best locations of fixed air quality monitoring stations at street level is challenging because of the complexity of the urban environment and the large number of factors affecting the pollutants concentration. Data sets of such urban parameters as land use, building morphology and street geometry in high-resolution grid cells in combination with direct measurements of airborne pollutants at high frequency (1-10 s) along a reasonable number of streets can be used to interpolate concentration of pollutants in a whole gridded domain and determine the optimum number of monitoring sites and best locations for a network of fixed monitors at ground level. In this context, a data-driven modeling methodology is developed based on the application of Self-Organizing Map (SOM) to approximate the nonlinear relations between urban parameters (80 in this work) and aerosol pollution data, such as mass and number concentrations measured along streets of a commercial/residential neighborhood of Singapore. Cross-validations between measured and predicted aerosol concentrations based on the urban parameters at each individual grid cell showed satisfying results. This proof of concept study showed that the selected urban parameters proved to be an appropriate indirect measure of aerosol concentrations within the studied area. The potential locations for fixed air quality monitors are identified through clustering of areas (i.e., group of cells) with similar urban patterns. The typological center of each cluster corresponds to the most representative cell for all other cells in the cluster. In the studied neighborhood four different clusters were identified and for each cluster potential sites for air quality monitoring at ground level are identified.

  18. Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

    International Nuclear Information System (INIS)

    Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

    2017-01-01

    Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction. (paper)

  19. Employment relations: A data driven analysis of job markets using online job boards and online professional networks

    CSIR Research Space (South Africa)

    Marivate, Vukosi N

    2017-08-01

    Full Text Available Data from online job boards and online professional networks present an opportunity to understand job markets as well as how professionals transition from one job/career to another. We propose a data driven approach to begin to understand a slice...

  20. Keys to success for data-driven decision making: Lessons from participatory monitoring and collaborative adaptive management

    Science.gov (United States)

    Recent years have witnessed a call for evidence-based decisions in conservation and natural resource management, including data-driven decision-making. Adaptive management (AM) is one prevalent model for integrating scientific data into decision-making, yet AM has faced numerous challenges and limit...