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.

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.

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.

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.

The power of event-driven analytics in Large Scale Data Processing

CERN Multimedia

CERN. Geneva; Marques, Paulo

2011-01-01

FeedZai is a software company specialized in creating high-­‐throughput low-­‐latency data processing solutions. FeedZai develops a product called "FeedZai Pulse" for continuous event-­‐driven analytics that makes application development easier for end users. It automatically calculates key performance indicators and baselines, showing how current performance differ from previous history, creating timely business intelligence updated to the second. The tool does predictive analytics and trend analysis, displaying data on real-­‐time web-­‐based graphics. In 2010 FeedZai won the European EBN Smart Entrepreneurship Competition, in the Digital Models category, being considered one of the "top-­‐20 smart companies in Europe". The main objective of this seminar/workshop is to explore the topic for large-­‐scale data processing using Complex Event Processing and, in particular, the possible uses of Pulse in...

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application.

Science.gov (United States)

Peissig, Peggy; Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-09-13

The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. ©Peggy Peissig, Kelsey M Schwei, Christopher Kadolph, Joseph Finamore, Efrain Cancel, Catherine A McCarty, Asha Okorie, Kate L Thomas, Jennifer Allen Pacheco, Jyotishman Pathak, Stephen B Ellis, Joshua C Denny, Luke V Rasmussen, Gerard Tromp, Marc S Williams, Tamara R Vrabec, Murray H Brilliant. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.09.2017.

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

Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

KAUST Repository

Dreano, Denis

2017-05-31

Phytoplankton is at the basis of the marine food chain and therefore play a fundamental role in the ocean ecosystem. However, the large-scale phytoplankton dynamics of the Red Sea are not well understood yet, mainly due to the lack of historical in situ measurements. As a result, our knowledge in this area relies mostly on remotely-sensed observations and large-scale numerical marine ecosystem models. Models are very useful to identify the mechanisms driving the variations in chlorophyll concentration and have practical applications for fisheries operation and harmful algae blooms monitoring. Modelling approaches can be divided between physics- driven (dynamical) approaches, and data-driven (statistical) approaches. Dynamical models are based on a set of differential equations representing the transfer of energy and matter between different subsets of the biota, whereas statistical models identify relationships between variables based on statistical relations within the available data. The goal of this thesis is to develop, implement and test novel dynamical and statistical modelling approaches for studying and forecasting the variability of chlorophyll concentration in the Red Sea. These new models are evaluated in term of their ability to efficiently forecast and explain the regional chlorophyll variability. We also propose innovative synergistic strategies to combine data- and physics-driven approaches to further enhance chlorophyll forecasting capabilities and efficiency.

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.

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.

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

Simulating large-scale pedestrian movement using CA and event driven model: Methodology and case study

Science.gov (United States)

Li, Jun; Fu, Siyao; He, Haibo; Jia, Hongfei; Li, Yanzhong; Guo, Yi

2015-11-01

Large-scale regional evacuation is an important part of national security emergency response plan. Large commercial shopping area, as the typical service system, its emergency evacuation is one of the hot research topics. A systematic methodology based on Cellular Automata with the Dynamic Floor Field and event driven model has been proposed, and the methodology has been examined within context of a case study involving the evacuation within a commercial shopping mall. Pedestrians walking is based on Cellular Automata and event driven model. In this paper, the event driven model is adopted to simulate the pedestrian movement patterns, the simulation process is divided into normal situation and emergency evacuation. The model is composed of four layers: environment layer, customer layer, clerk layer and trajectory layer. For the simulation of movement route of pedestrians, the model takes into account purchase intention of customers and density of pedestrians. Based on evacuation model of Cellular Automata with Dynamic Floor Field and event driven model, we can reflect behavior characteristics of customers and clerks at the situations of normal and emergency evacuation. The distribution of individual evacuation time as a function of initial positions and the dynamics of the evacuation process is studied. Our results indicate that the evacuation model using the combination of Cellular Automata with Dynamic Floor Field and event driven scheduling can be used to simulate the evacuation of pedestrian flows in indoor areas with complicated surroundings and to investigate the layout of shopping mall.

Event management for large scale event-driven digital hardware spiking neural networks.

Science.gov (United States)

Caron, Louis-Charles; D'Haene, Michiel; Mailhot, Frédéric; Schrauwen, Benjamin; Rouat, Jean

2013-09-01

The interest in brain-like computation has led to the design of a plethora of innovative neuromorphic systems. Individually, spiking neural networks (SNNs), event-driven simulation and digital hardware neuromorphic systems get a lot of attention. Despite the popularity of event-driven SNNs in software, very few digital hardware architectures are found. This is because existing hardware solutions for event management scale badly with the number of events. This paper introduces the structured heap queue, a pipelined digital hardware data structure, and demonstrates its suitability for event management. The structured heap queue scales gracefully with the number of events, allowing the efficient implementation of large scale digital hardware event-driven SNNs. The scaling is linear for memory, logarithmic for logic resources and constant for processing time. The use of the structured heap queue is demonstrated on a field-programmable gate array (FPGA) with an image segmentation experiment and a SNN of 65,536 neurons and 513,184 synapses. Events can be processed at the rate of 1 every 7 clock cycles and a 406×158 pixel image is segmented in 200 ms. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

Science.gov (United States)

Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

2017-01-01

Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

Science.gov (United States)

Ramachandran, Rahul; Kulkarni, Ajinkya; Maskey, Manil; Bakare, Rohan; Basyal, Sabin; Li, Xiang; Flynn, Shannon

2014-01-01

Approaches used in Earth science research such as case study analysis and climatology studies involve discovering and gathering diverse data sets and information to support the research goals. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. In cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. This paper presents a specialized search, aggregation and curation tool for Earth science to address these challenges. The search rool automatically creates curated 'Data Albums', aggregated collections of information related to a specific event, containing links to relevant data files [granules] from different instruments, tools and services for visualization and analysis, and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non relevant information and data.

Automated Testing of Event-Driven Applications

DEFF Research Database (Denmark)

Jensen, Casper Svenning

may be tested by selecting an interesting input (i.e. a sequence of events), and deciding if a failure occurs when the selected input is applied to the event-driven application under test. Automated testing promises to reduce the workload for developers by automatically selecting interesting inputs...... and detect failures. However, it is non-trivial to conduct automated testing of event-driven applications because of, for example, infinite input spaces and the absence of specifications of correct application behavior. In this PhD dissertation, we identify a number of specific challenges when conducting...... automated testing of event-driven applications, and we present novel techniques for solving these challenges. First, we present an algorithm for stateless model-checking of event-driven applications with partial-order reduction, and we show how this algorithm may be used to systematically test web...

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.

Peripheral visual feedback: a powerful means of supporting effective attention allocation in event-driven, data-rich environments.

Science.gov (United States)

Nikolic, M I; Sarter, N B

2001-01-01

Breakdowns in human-automation coordination in data-rich, event-driven domains such as aviation can be explained in part by a mismatch between the high degree of autonomy yet low observability of modern technology. To some extent, the latter is the result of an increasing reliance in feedback design on foveal vision--an approach that fails to support pilots in tracking system-induced changes and events in parallel with performing concurrent flight-related tasks. One possible solution to the problem is the distribution of tasks and information across sensory modalities and processing channels. A simulator study is presented that compared the effectiveness of current foveal feedback and two implementations of peripheral visual feedback for keeping pilots informed about uncommanded changes in the status of an automated cockpit system. Both peripheral visual displays resulted in higher detection rates and faster response times, without interfering with the performance of concurrent visual tasks any more than does currently available automation feedback. Potential applications include improved display designs that support effective attention allocation in a variety of complex dynamic environments, such as aviation, process control, and medicine.

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...

Spatiotemporal Features for Asynchronous Event-based Data

Directory of Open Access Journals (Sweden)

Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

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....

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.

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.

Sawtooth events and O+ in the plasma sheet and boundary layer: CME- and SIR-driven events

Science.gov (United States)

Lund, E. J.; Nowrouzi, N.; Kistler, L. M.; Cai, X.; Liao, J.

2017-12-01

The role of ionospheric ions in sawtooth events is an open question. Simulations[1,2,3] suggest that O+ from the ionosphere produces a feedback mechanism for driving sawtooth events. However, observational evidence[4,5] suggest that the presence of O+ in the plasma sheet is neither necessary nor sufficient. In this study we investigate whether the solar wind driver of the geomagnetic storm has an effect on the result. Building on an earlier study[4] that used events for which Cluster data is available in the plasma sheet and boundary layer, we perform a superposed epoch analysis for coronal mass ejection (CME) driven storms and streaming interaction region (SIR) driven storms separately, to investigate the hypothesis that ionospheric O+ is an important contributor for CME-driven storms but not SIR-driven storms[2]. [1]O. J. Brambles et al. (2011), Science 332, 1183.[2]O. J. Brambles et al. (2013), JGR 118, 6026.[3]R. H. Varney et al. (2016), JGR 121, 9688.[4]J. Liao et al. (2014), JGR 119, 1572.[5]E. J. Lund et al. (2017), JGR, submitted.

A dynamic, climate-driven model of Rift Valley fever

Directory of Open Access Journals (Sweden)

Joseph Leedale

2016-03-01

Full Text Available Outbreaks of Rift Valley fever (RVF in eastern Africa have previously occurred following specific rainfall dynamics and flooding events that appear to support the emergence of large numbers of mosquito vectors. As such, transmission of the virus is considered to be sensitive to environmental conditions and therefore changes in climate can impact the spatiotemporal dynamics of epizootic vulnerability. Epidemiological information describing the methods and parameters of RVF transmission and its dependence on climatic factors are used to develop a new spatio-temporal mathematical model that simulates these dynamics and can predict the impact of changes in climate. The Liverpool RVF (LRVF model is a new dynamic, process-based model driven by climate data that provides a predictive output of geographical changes in RVF outbreak susceptibility as a result of the climate and local livestock immunity. This description of the multi-disciplinary process of model development is accessible to mathematicians, epidemiological modellers and climate scientists, uniting dynamic mathematical modelling, empirical parameterisation and state-of-the-art climate information.

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).

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.

The Event-Driven Software Library for YARP—With Algorithms and iCub Applications

Directory of Open Access Journals (Sweden)

Arren Glover

2018-01-01

Full Text Available Event-driven (ED cameras are an emerging technology that sample the visual signal based on changes in the signal magnitude, rather than at a fixed-rate over time. The change in paradigm results in a camera with a lower latency, that uses less power, has reduced bandwidth, and higher dynamic range. Such cameras offer many potential advantages for on-line, autonomous, robots; however, the sensor data do not directly integrate with current “image-based” frameworks and software libraries. The iCub robot uses Yet Another Robot Platform (YARP as middleware to provide modular processing and connectivity to sensors and actuators. This paper introduces a library that incorporates an event-based framework into the YARP architecture, allowing event cameras to be used with the iCub (and other YARP-based robots. We describe the philosophy and methods for structuring events to facilitate processing, while maintaining low-latency and real-time operation. We also describe several processing modules made available open-source, and three example demonstrations that can be run on the neuromorphic iCub.

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.

Discrete Event Modeling and Simulation-Driven Engineering for the ATLAS Data Acquisition Network

CERN Document Server

Bonaventura, Matias Alejandro; The ATLAS collaboration; Castro, Rodrigo Daniel

2016-01-01

We present an iterative and incremental development methodology for simulation models in network engineering projects. Driven by the DEVS (Discrete Event Systems Specification) formal framework for modeling and simulation we assist network design, test, analysis and optimization processes. A practical application of the methodology is presented for a case study in the ATLAS particle physics detector, the largest scientific experiment built by man where scientists around the globe search for answers about the origins of the universe. The ATLAS data network convey real-time information produced by physics detectors as beams of particles collide. The produced sub-atomic evidences must be filtered and recorded for further offline scrutiny. Due to the criticality of the transported data, networks and applications undergo careful engineering processes with stringent quality of service requirements. A tight project schedule imposes time pressure on design decisions, while rapid technology evolution widens the palett...

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...

Dynamical critical phenomena in driven-dissipative systems.

Science.gov (United States)

Sieberer, L M; Huber, S D; Altman, E; Diehl, S

2013-05-10

We explore the nature of the Bose condensation transition in driven open quantum systems, such as exciton-polariton condensates. Using a functional renormalization group approach formulated in the Keldysh framework, we characterize the dynamical critical behavior that governs decoherence and an effective thermalization of the low frequency dynamics. We identify a critical exponent special to the driven system, showing that it defines a new dynamical universality class. Hence critical points in driven systems lie beyond the standard classification of equilibrium dynamical phase transitions. We show how the new critical exponent can be probed in experiments with driven cold atomic systems and exciton-polariton condensates.

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.)

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.)

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

A High-Speed, Event-Driven, Active Pixel Sensor Readout for Photon-Counting Microchannel Plate Detectors

Science.gov (United States)

Kimble, Randy A.; Pain, Bedabrata; Norton, Timothy J.; Haas, J. Patrick; Oegerle, William R. (Technical Monitor)

2002-01-01

Silicon array readouts for microchannel plate intensifiers offer several attractive features. In this class of detector, the electron cloud output of the MCP intensifier is converted to visible light by a phosphor; that light is then fiber-optically coupled to the silicon array. In photon-counting mode, the resulting light splashes on the silicon array are recognized and centroided to fractional pixel accuracy by off-chip electronics. This process can result in very high (MCP-limited) spatial resolution while operating at a modest MCP gain (desirable for dynamic range and long term stability). The principal limitation of intensified CCD systems of this type is their severely limited local dynamic range, as accurate photon counting is achieved only if there are not overlapping event splashes within the frame time of the device. This problem can be ameliorated somewhat by processing events only in pre-selected windows of interest of by using an addressable charge injection device (CID) for the readout array. We are currently pursuing the development of an intriguing alternative readout concept based on using an event-driven CMOS Active Pixel Sensor. APS technology permits the incorporation of discriminator circuitry within each pixel. When coupled with suitable CMOS logic outside the array area, the discriminator circuitry can be used to trigger the readout of small sub-array windows only when and where an event splash has been detected, completely eliminating the local dynamic range problem, while achieving a high global count rate capability and maintaining high spatial resolution. We elaborate on this concept and present our progress toward implementing an event-driven APS readout.

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.

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...

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.

Quantitative theory of driven nonlinear brain dynamics.

Science.gov (United States)

Roberts, J A; Robinson, P A

2012-09-01

Strong periodic stimuli such as bright flashing lights evoke nonlinear responses in the brain and interact nonlinearly with ongoing cortical activity, but the underlying mechanisms for these phenomena are poorly understood at present. The dominant features of these experimentally observed dynamics are reproduced by the dynamics of a quantitative neural field model subject to periodic drive. Model power spectra over a range of drive frequencies show agreement with multiple features of experimental measurements, exhibiting nonlinear effects including entrainment over a range of frequencies around the natural alpha frequency f(α), subharmonic entrainment near 2f(α), and harmonic generation. Further analysis of the driven dynamics as a function of the drive parameters reveals rich nonlinear dynamics that is predicted to be observable in future experiments at high drive amplitude, including period doubling, bistable phase-locking, hysteresis, wave mixing, and chaos indicated by positive Lyapunov exponents. Moreover, photosensitive seizures are predicted for physiologically realistic model parameters yielding bistability between healthy and seizure dynamics. These results demonstrate the applicability of neural field models to the new regime of periodically driven nonlinear dynamics, enabling interpretation of experimental data in terms of specific generating mechanisms and providing new tests of the theory. Copyright © 2012 Elsevier Inc. All rights reserved.

Sharing adverse drug event data using business intelligence technology.

Science.gov (United States)

Horvath, Monica M; Cozart, Heidi; Ahmad, Asif; Langman, Matthew K; Ferranti, Jeffrey

2009-03-01

Duke University Health System uses computerized adverse drug event surveillance as an integral part of medication safety at 2 community hospitals and an academic medical center. This information must be swiftly communicated to organizational patient safety stakeholders to find opportunities to improve patient care; however, this process is encumbered by highly manual methods of preparing the data. Following the examples of other industries, we deployed a business intelligence tool to provide dynamic safety reports on adverse drug events. Once data were migrated into the health system data warehouse, we developed census-adjusted reports with user-driven prompts. Drill down functionality enables navigation from aggregate trends to event details by clicking report graphics. Reports can be accessed by patient safety leadership either through an existing safety reporting portal or the health system performance improvement Web site. Elaborate prompt screens allow many varieties of reports to be created quickly by patient safety personnel without consultation with the research analyst. The reduction in research analyst workload because of business intelligence implementation made this individual available to additional patient safety projects thereby leveraging their talents more effectively. Dedicated liaisons are essential to ensure clear communication between clinical and technical staff throughout the development life cycle. Design and development of the business intelligence model for adverse drug event data must reflect the eccentricities of the operational system, especially as new areas of emphasis evolve. Future usability studies examining the data presentation and access model are needed.

Ac-driven vortex-antivortex dynamics in nanostructured superconductor-ferromagnetic hybrids

Energy Technology Data Exchange (ETDEWEB)

Lima, Clessio L.S., E-mail: clsl@df.ufpe.br [Nucleo de Tecnologia, Centro Academico do Agreste, Universidade Federal de Pernambuco, 55002-970 Caruaru-PE (Brazil); Souza Silva, Clecio C. de; Aguiar, J. Albino [Departamento de Fisica, Universidade Federal de Pernambuco, 50670-901 Recife-PE (Brazil)

2012-09-15

The dynamics of ac-driven vortices and antivortices in a superconducting film interacting with an array of magnetic dipoles on top is investigated via hybrid molecular dynamics-Monte Carlo simulations. The dipole array considered in this study is capable to stabilize in equilibrium vortex-antivortex pairs. The appearance of a net electric field out of the ac excitation demonstrates that this system behaves as a voltage rectifier. Because of the asymmetric nature of the effective pinning potential generated by the dipole array, the ac-driven vortices and antivortices are ratcheted in opposite directions, thereby contributing additively to the observed net voltage. In addition, for high frequency values, the dc electric field-ac amplitude curves present a series of steps. A careful analysis of the time series of the electric field and number of vortex-antivortex (v-av) pairs reveals that these steps are related to mode-locking between the drive frequency and the number of v-av creation-annihilation events.

Event-driven processing for hardware-efficient neural spike sorting

Science.gov (United States)

Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

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...

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

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.

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.

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.

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.

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...

Molecular dynamics for irradiation driven chemistry

DEFF Research Database (Denmark)

Sushko, Gennady B.; Solov'yov, Ilia A.; Solov'yov, Andrey V.

2016-01-01

A new molecular dynamics (MD) approach for computer simulations of irradiation driven chemical transformations of complex molecular systems is suggested. The approach is based on the fact that irradiation induced quantum transformations can often be treated as random, fast and local processes...... that describe the classical MD of complex molecular systems under irradiation. The proposed irradiation driven molecular dynamics (IDMD) methodology is designed for the molecular level description of the irradiation driven chemistry. The IDMD approach is implemented into the MBN Explorer software package...... involving small molecules or molecular fragments. We advocate that the quantum transformations, such as molecular bond breaks, creation and annihilation of dangling bonds, electronic charge redistributions, changes in molecular topologies, etc., could be incorporated locally into the molecular force fields...

Dynamic analysis of an accelerator-driven fluid-fueled subcritical radioactive waste burning system

International Nuclear Information System (INIS)

Woosley, M.L. Jr.; Rydin, R.A.

1998-01-01

The recent revival of interest in accelerator-driven subcritical fluid-fueled systems is documented. Several important applications of these systems are mentioned, and this is used to motivate the need for dynamic analysis of the nuclear kinetics of such systems. A physical description of the Los alamos National Laboratory accelerator-based conversion (ABC) concept is provided. This system is used as the basis for the kinetics study in this research. The current approach to the dynamic simulation of an accelerator-driven subcritical fluid-fueled system includes four functional elements: a discrete ordinates model is used to calculate the flux distribution for the source-driven system; a nodal convection model is used to calculate time-dependent isotope and temperature distributions that impact reactivity; a nodal importance weighting model is used to calculate the reactivity impact of temperature and isotope distributions and to feed this information back to the time-dependent nodal convection model; and a transient driver is used to simulate transients, model the balance of plant, and record simulation data. Specific transients that have been analyzed with the current modeling system are discussed. These transients include loss-of-flow and loss-of-cooling accidents, xenon and samarium transients, and cold-plug and overfueling events. The results of various transients have uncovered unpredictable behavior, unresolved design issues, and the need for active control. The need for the development of a nodal-coupling spatial kinetics model is mentioned

Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies.

Science.gov (United States)

Balador, Ali; Uhlemann, Elisabeth; Calafate, Carlos T; Cano, Juan-Carlos

2018-03-23

Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications.

Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies

Directory of Open Access Journals (Sweden)

Ali Balador

2018-03-01

Full Text Available Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications.

Event-driven simulation of neural population synchronization facilitated by electrical coupling.

Science.gov (United States)

Carrillo, Richard R; Ros, Eduardo; Barbour, Boris; Boucheny, Christian; Coenen, Olivier

2007-02-01

Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.

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

Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

Directory of Open Access Journals (Sweden)

Emre eNeftci

2014-01-01

Full Text Available Restricted Boltzmann Machines (RBMs and Deep Belief Networks have been demonstrated to perform efficiently in variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The reverberating activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP carries out the weight updates in an online, asynchronous fashion.We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems.

Science.gov (United States)

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

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.

System on chip module configured for event-driven architecture

Science.gov (United States)

Robbins, Kevin; Brady, Charles E.; Ashlock, Tad A.

2017-10-17

A system on chip (SoC) module is described herein, wherein the SoC modules comprise a processor subsystem and a hardware logic subsystem. The processor subsystem and hardware logic subsystem are in communication with one another, and transmit event messages between one another. The processor subsystem executes software actors, while the hardware logic subsystem includes hardware actors, the software actors and hardware actors conform to an event-driven architecture, such that the software actors receive and generate event messages and the hardware actors receive and generate event messages.

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.

Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data.

Science.gov (United States)

Buonaccorsi, Giovanni A; O'Connor, James P B; Caunce, Angela; Roberts, Caleb; Cheung, Sue; Watson, Yvonne; Davies, Karen; Hope, Lynn; Jackson, Alan; Jayson, Gordon C; Parker, Geoffrey J M

2007-11-01

Dynamic contrast-enhanced MRI (DCE-MRI) time series data are subject to unavoidable physiological motion during acquisition (e.g., due to breathing) and this motion causes significant errors when fitting tracer kinetic models to the data, particularly with voxel-by-voxel fitting approaches. Motion correction is problematic, as contrast enhancement introduces new features into postcontrast images and conventional registration similarity measures cannot fully account for the increased image information content. A methodology is presented for tracer kinetic model-driven registration that addresses these problems by explicitly including a model of contrast enhancement in the registration process. The iterative registration procedure is focused on a tumor volume of interest (VOI), employing a three-dimensional (3D) translational transformation that follows only tumor motion. The implementation accurately removes motion corruption in a DCE-MRI software phantom and it is able to reduce model fitting errors and improve localization in 3D parameter maps in patient data sets that were selected for significant motion problems. Sufficient improvement was observed in the modeling results to salvage clinical trial DCE-MRI data sets that would otherwise have to be rejected due to motion corruption. Copyright 2007 Wiley-Liss, Inc.

An event driven algorithm for fractal cluster formation

NARCIS (Netherlands)

González, S.; Gonzalez Briones, Sebastián; Thornton, Anthony Richard; Luding, Stefan

2011-01-01

A new cluster based event-driven algorithm is developed to simulate the formation of clusters in a two dimensional gas: particles move freely until they collide and "stick" together irreversibly. These clusters aggregate into bigger structures in an isotompic way, forming fractal structures whose

An event driven algorithm for fractal cluster formation

NARCIS (Netherlands)

González, S.; Thornton, Anthony Richard; Luding, Stefan

2010-01-01

A new cluster based event-driven algorithm is developed to simulate the formation of clusters in a two dimensional gas: particles move freely until they collide and "stick" together irreversibly. These clusters aggregate into bigger structures in an isotompic way, forming fractal structures whose

WE-G-BRA-02: SafetyNet: Automating Radiotherapy QA with An Event Driven Framework

International Nuclear Information System (INIS)

Hadley, S; Kessler, M; Litzenberg, D; Lee, C; Irrer, J; Chen, X; Acosta, E; Weyburne, G; Lam, K; Younge, K; Matuszak, M; Keranen, W; Covington, E; Moran, J

2015-01-01

Purpose: Quality assurance is an essential task in radiotherapy that often requires many manual tasks. We investigate the use of an event driven framework in conjunction with software agents to automate QA and eliminate wait times. Methods: An in house developed subscription-publication service, EventNet, was added to the Aria OIS to be a message broker for critical events occurring in the OIS and software agents. Software agents operate without user intervention and perform critical QA steps. The results of the QA are documented and the resulting event is generated and passed back to EventNet. Users can subscribe to those events and receive messages based on custom filters designed to send passing or failing results to physicists or dosimetrists. Agents were developed to expedite the following QA tasks: Plan Revision, Plan 2nd Check, SRS Winston-Lutz isocenter, Treatment History Audit, Treatment Machine Configuration. Results: Plan approval in the Aria OIS was used as the event trigger for plan revision QA and Plan 2nd check agents. The agents pulled the plan data, executed the prescribed QA, stored the results and updated EventNet for publication. The Winston Lutz agent reduced QA time from 20 minutes to 4 minutes and provided a more accurate quantitative estimate of radiation isocenter. The Treatment Machine Configuration agent automatically reports any changes to the Treatment machine or HDR unit configuration. The agents are reliable, act immediately, and execute each task identically every time. Conclusion: An event driven framework has inverted the data chase in our radiotherapy QA process. Rather than have dosimetrists and physicists push data to QA software and pull results back into the OIS, the software agents perform these steps immediately upon receiving the sentinel events from EventNet. Mr Keranen is an employee of Varian Medical Systems. Dr. Moran’s institution receives research support for her effort for a linear accelerator QA project from

WE-G-BRA-02: SafetyNet: Automating Radiotherapy QA with An Event Driven Framework

Energy Technology Data Exchange (ETDEWEB)

Hadley, S; Kessler, M [The University of Michigan, Ann Arbor, MI (United States); Litzenberg, D [Univ Michigan, Ann Arbor, MI (United States); Lee, C; Irrer, J; Chen, X; Acosta, E; Weyburne, G; Lam, K; Younge, K; Matuszak, M [University of Michigan, Ann Arbor, MI (United States); Keranen, W [Varian Medical Systems, Palo Alto, CA (United States); Covington, E [University of Michigan Hospital and Health System, Ann Arbor, MI (United States); Moran, J [Univ Michigan Medical Center, Ann Arbor, MI (United States)

2015-06-15

Purpose: Quality assurance is an essential task in radiotherapy that often requires many manual tasks. We investigate the use of an event driven framework in conjunction with software agents to automate QA and eliminate wait times. Methods: An in house developed subscription-publication service, EventNet, was added to the Aria OIS to be a message broker for critical events occurring in the OIS and software agents. Software agents operate without user intervention and perform critical QA steps. The results of the QA are documented and the resulting event is generated and passed back to EventNet. Users can subscribe to those events and receive messages based on custom filters designed to send passing or failing results to physicists or dosimetrists. Agents were developed to expedite the following QA tasks: Plan Revision, Plan 2nd Check, SRS Winston-Lutz isocenter, Treatment History Audit, Treatment Machine Configuration. Results: Plan approval in the Aria OIS was used as the event trigger for plan revision QA and Plan 2nd check agents. The agents pulled the plan data, executed the prescribed QA, stored the results and updated EventNet for publication. The Winston Lutz agent reduced QA time from 20 minutes to 4 minutes and provided a more accurate quantitative estimate of radiation isocenter. The Treatment Machine Configuration agent automatically reports any changes to the Treatment machine or HDR unit configuration. The agents are reliable, act immediately, and execute each task identically every time. Conclusion: An event driven framework has inverted the data chase in our radiotherapy QA process. Rather than have dosimetrists and physicists push data to QA software and pull results back into the OIS, the software agents perform these steps immediately upon receiving the sentinel events from EventNet. Mr Keranen is an employee of Varian Medical Systems. Dr. Moran’s institution receives research support for her effort for a linear accelerator QA project from

Interestingness-Driven Diffusion Process Summarization in Dynamic Networks

DEFF Research Database (Denmark)

Qu, Qiang; Liu, Siyuan; Jensen, Christian S.

2014-01-01

The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key...... tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which...... render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes...

Hamiltonian-Driven Adaptive Dynamic Programming for Continuous Nonlinear Dynamical Systems.

Science.gov (United States)

Yang, Yongliang; Wunsch, Donald; Yin, Yixin

2017-08-01

This paper presents a Hamiltonian-driven framework of adaptive dynamic programming (ADP) for continuous time nonlinear systems, which consists of evaluation of an admissible control, comparison between two different admissible policies with respect to the corresponding the performance function, and the performance improvement of an admissible control. It is showed that the Hamiltonian can serve as the temporal difference for continuous-time systems. In the Hamiltonian-driven ADP, the critic network is trained to output the value gradient. Then, the inner product between the critic and the system dynamics produces the value derivative. Under some conditions, the minimization of the Hamiltonian functional is equivalent to the value function approximation. An iterative algorithm starting from an arbitrary admissible control is presented for the optimal control approximation with its convergence proof. The implementation is accomplished by a neural network approximation. Two simulation studies demonstrate the effectiveness of Hamiltonian-driven ADP.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

Directory of Open Access Journals (Sweden)

Andrea Maesani

2015-11-01

Full Text Available The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

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

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

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.

Problems in the neutron dynamics of source-driven systems

International Nuclear Information System (INIS)

Ravetto, P.

2001-01-01

The present paper presents some neutronic features of source-driven neutron multiplying systems, with special regards to dynamics, discussing the validity and limitations of classical methods, developed for systems in the vicinity of criticality. Specific characteristics, such as source dominance and the role of delayed neutron emissions are illustrated. Some dynamic peculiarities of innovative concepts proposed for accelerator-driven systems, such as fluid-fuel, are also discussed. The second portion of the work formulates the quasi-static methods for source-driven systems, evidencing its novel features and presenting some numerical results. (author)

Staged Event-Driven Architecture As A Micro-Architecture Of Distributed And Pluginable Crawling Platform

Directory of Open Access Journals (Sweden)

Leszek Siwik

2013-01-01

Full Text Available There are many crawling systems available on the market but they are rather close systems dedicated for performing particular kind and class of tasks with predefined set of scope, strategy etc. In real life however there are meaningful groups of users (e.g. marketing, criminal or governmental analysts requiring not just a yet another crawling system dedicated for performing predefined tasks. They need rather easy-to-use, user friendly all-in-one studio for not only executing and running internet robots and crawlers, but also for (graphical (redefining and (recomposing crawlers according to dynamically changing requirements and use-cases. To realize the above-mentioned idea, Cassiopeia framework has been designed and developed. One has to remember, however, that enormous size and unimaginable structural complexity of WWW network are the reasons that, from a technical and architectural point of view, developing effective internet robots – and the more so developing a framework supporting graphical robots’ composition – becomes a really challenging task. The crucial aspect in the context of crawling efficiency and scalability is concurrency model applied. There are two the most typical concurrency management models i.e. classical concurrency based on the pool of threads and processes and event-driven concurrency. None of them are ideal approaches. That is why, research on alternative models is still conducted to propose efficient and convenient architecture for concurrent and distributed applications. One of promising models is staged event-driven architecture mixing to some extent both of above mentioned classical approaches and providing some additional benefits such as splitting application into separate stages connected by events queues – what is interesting taking requirements about crawler (recomposition into account. The goal of this paper is to present the idea and the PoC  implementation of Cassiopeia framework, with the special

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...

The column architecture -- A novel architecture for event driven 2D pixel imagers

International Nuclear Information System (INIS)

Millaud, J.; Nygren, D.

1996-01-01

The authors describe an electronic architecture for two-dimensional pixel arrays that permits very large increases in rate capability for event- or data-driven applications relative to conventional x-y architectures. The column architecture also permits more efficient use of silicon area in applications requiring local buffering, frameless data acquisition, and it avoids entirely the problem of ambiguities that may arise in conventional approaches. Two examples of active implementation are described: high energy physics and protein crystallography

On Rank Driven Dynamical Systems

Science.gov (United States)

Veerman, J. J. P.; Prieto, F. J.

2014-08-01

We investigate a class of models related to the Bak-Sneppen (BS) model, initially proposed to study evolution. The BS model is extremely simple and yet captures some forms of "complex behavior" such as self-organized criticality that is often observed in physical and biological systems. In this model, random fitnesses in are associated to agents located at the vertices of a graph . Their fitnesses are ranked from worst (0) to best (1). At every time-step the agent with the worst fitness and some others with a priori given rank probabilities are replaced by new agents with random fitnesses. We consider two cases: The exogenous case where the new fitnesses are taken from an a priori fixed distribution, and the endogenous case where the new fitnesses are taken from the current distribution as it evolves. We approximate the dynamics by making a simplifying independence assumption. We use Order Statistics and Dynamical Systems to define a rank-driven dynamical system that approximates the evolution of the distribution of the fitnesses in these rank-driven models, as well as in the BS model. For this simplified model we can find the limiting marginal distribution as a function of the initial conditions. Agreement with experimental results of the BS model is excellent.

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.

Polar cap flow channel events: spontaneous and driven responses

Directory of Open Access Journals (Sweden)

P. E. Sandholt

2010-11-01

Full Text Available We present two case studies of specific flow channel events appearing at the dusk and/or dawn polar cap boundary during passage at Earth of interplanetary (IP coronal mass ejections (ICMEs on 10 January and 25 July 2004. The channels of enhanced (>1 km/s antisunward convection are documented by SuperDARN radars and dawn-dusk crossings of the polar cap by the DMSP F13 satellite. The relationship with Birkeland currents (C1–C2 located poleward of the traditional R1–R2 currents is demonstrated. The convection events are manifest in ground magnetic deflections obtained from the IMAGE (International Monitor for Auroral Geomagnetic Effects Svalbard chain of ground magnetometer stations located within 71–76° MLAT. By combining the ionospheric convection data and the ground magnetograms we are able to study the temporal behaviour of the convection events. In the two ICME case studies the convection events belong to two different categories, i.e., directly driven and spontaneous events. In the 10 January case two sharp southward turnings of the ICME magnetic field excited corresponding convection events as detected by IMAGE and SuperDARN. We use this case to determine the ground magnetic signature of enhanced flow channel events (the NH-dusk/By<0 variant. In the 25 July case a several-hour-long interval of steady southwest ICME field (Bz<0; By<0 gave rise to a long series of spontaneous convection events as detected by IMAGE when the ground stations swept through the 12:00–18:00 MLT sector. From the ground-satellite conjunction on 25 July we infer the pulsed nature of the polar cap ionospheric flow channel events in this case. The typical duration of these convection enhancements in the polar cap is 10 min.

Self-Adaptive Event-Driven Simulation of Multi-Scale Plasma Systems

Science.gov (United States)

Omelchenko, Yuri; Karimabadi, Homayoun

2005-10-01

Multi-scale plasmas pose a formidable computational challenge. The explicit time-stepping models suffer from the global CFL restriction. Efficient application of adaptive mesh refinement (AMR) to systems with irregular dynamics (e.g. turbulence, diffusion-convection-reaction, particle acceleration etc.) may be problematic. To address these issues, we developed an alternative approach to time stepping: self-adaptive discrete-event simulation (DES). DES has origin in operations research, war games and telecommunications. We combine finite-difference and particle-in-cell techniques with this methodology by assuming two caveats: (1) a local time increment, dt for a discrete quantity f can be expressed in terms of a physically meaningful quantum value, df; (2) f is considered to be modified only when its change exceeds df. Event-driven time integration is self-adaptive as it makes use of causality rules rather than parametric time dependencies. This technique enables asynchronous flux-conservative update of solution in accordance with local temporal scales, removes the curse of the global CFL condition, eliminates unnecessary computation in inactive spatial regions and results in robust and fast parallelizable codes. It can be naturally combined with various mesh refinement techniques. We discuss applications of this novel technology to diffusion-convection-reaction systems and hybrid simulations of magnetosonic shocks.

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%.

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

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

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.

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.

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.

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.

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.

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.

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...

A Multi-mission Event-Driven Component-Based System for Support of Flight Software Development, ATLO, and Operations first used by the Mars Science Laboratory (MSL) Project

Science.gov (United States)

Dehghani, Navid; Tankenson, Michael

2006-01-01

This paper details an architectural description of the Mission Data Processing and Control System (MPCS), an event-driven, multi-mission ground data processing components providing uplink, downlink, and data management capabilities which will support the Mars Science Laboratory (MSL) project as its first target mission. MPCS is developed based on a set of small reusable components, implemented in Java, each designed with a specific function and well-defined interfaces. An industry standard messaging bus is used to transfer information among system components. Components generate standard messages which are used to capture system information, as well as triggers to support the event-driven architecture of the system. Event-driven systems are highly desirable for processing high-rate telemetry (science and engineering) data, and for supporting automation for many mission operations processes.

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.

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

Using a data-centric event-driven architecture approach in the integration of real-time systems at DTP2

International Nuclear Information System (INIS)

Tuominen, Janne; Viinikainen, Mikko; Alho, Pekka; Mattila, Jouni

2014-01-01

Integration of heterogeneous and distributed systems is a challenging task, because they might be running on different platforms and written with different implementation languages by multiple organizations. Data-centricity and event-driven architecture (EDA) are concepts that help to implement versatile and well-scaling distributed systems. This paper focuses on the implementation of inter-subsystem communication in a prototype distributed remote handling control system developed at Divertor Test Platform 2 (DTP2). The control system consists of a variety of heterogeneous subsystems, including a client–server web application and hard real-time controllers. A standardized middleware solution (Data Distribution Services (DDS)) that supports a data-centric EDA approach is used to integrate the system. One of the greatest challenges in integrating a system with a data-centric EDA approach is in defining the global data space model. The selected middleware is currently only used for non-deterministic communication. For future application, we evaluated the performance of point-to-point communication with and without the presence of additional network load to ensure applicability to real-time systems. We found that, under certain limitations, the middleware can be used for soft real-time communication. Hard real-time use will require more validation with a more suitable environment

Using a data-centric event-driven architecture approach in the integration of real-time systems at DTP2

Energy Technology Data Exchange (ETDEWEB)

Tuominen, Janne, E-mail: janne.m.tuominen@tut.fi; Viinikainen, Mikko; Alho, Pekka; Mattila, Jouni

2014-10-15

Integration of heterogeneous and distributed systems is a challenging task, because they might be running on different platforms and written with different implementation languages by multiple organizations. Data-centricity and event-driven architecture (EDA) are concepts that help to implement versatile and well-scaling distributed systems. This paper focuses on the implementation of inter-subsystem communication in a prototype distributed remote handling control system developed at Divertor Test Platform 2 (DTP2). The control system consists of a variety of heterogeneous subsystems, including a client–server web application and hard real-time controllers. A standardized middleware solution (Data Distribution Services (DDS)) that supports a data-centric EDA approach is used to integrate the system. One of the greatest challenges in integrating a system with a data-centric EDA approach is in defining the global data space model. The selected middleware is currently only used for non-deterministic communication. For future application, we evaluated the performance of point-to-point communication with and without the presence of additional network load to ensure applicability to real-time systems. We found that, under certain limitations, the middleware can be used for soft real-time communication. Hard real-time use will require more validation with a more suitable environment.

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.

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.

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...

Data-Intensive Science meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

Science.gov (United States)

Ramachandran, Rahul; Word, Andrea; Nair, Udasysankar

2014-01-01

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. For example, challenges in the teaching and learning of atmospheric science can be traced to threshold concepts in fluid dynamics. In particular, Dynamic Meteorology is one of the most challenging courses for graduate students and undergraduates majoring in Atmospheric Science. Dynamic Meteorology introduces threshold concepts - those that prove troublesome for the majority of students but that are essential, associated with fundamental relationships between forces and motion in the atmosphere and requiring the application of basic classical statics, dynamics, and thermodynamic principles to the three dimensionally varying atmospheric structure. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of dataintensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow

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.

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.

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

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.

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.

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.

The Exploration of Design Driven Innovation as a Dynamic Capability

Directory of Open Access Journals (Sweden)

Philips Kembaren

2012-01-01

Full Text Available Innovation enables companies to attain consistent organic growth that brings benefits to stakeholders. Designthinking approach in innovation has been emergent to be an alternative to technological development path inorder to generate competitive and successful product or service in the market place. Design driven innovationcombines functional and semantic dimensions of products or services in the marketplace. Previous researchhas recently revealed practices of design driven innovation in various industries. However, little is known tothe extent that companies in Indonesia practicing design driven innovation. A theoretical framework withperspective from dynamic capability theoretical lens and guided by Dubin’s theory building methodology isproposed to explain the constructs and role of design in the process of innovation. The research is expected tocontribute a new construct to the existing framework, namely construct that related to how we could assessthe value of the design-driven innovation output, perceived by the costumers.Keywords: design driven innovation, dynamic capabilities, theory building

Notification Event Architecture for Traveler Screening: Predictive Traveler Screening Using Event Driven Business Process Management

Science.gov (United States)

Lynch, John Kenneth

2013-01-01

Using an exploratory model of the 9/11 terrorists, this research investigates the linkages between Event Driven Business Process Management (edBPM) and decision making. Although the literature on the role of technology in efficient and effective decision making is extensive, research has yet to quantify the benefit of using edBPM to aid the…

Seabed resident event driven profiling system (SREP). Concept, design and tests

Digital Repository Service at National Institute of Oceanography (India)

Mascarenhas, A.A.M.Q.; Afzulpurkar, S.; Maurya, P.K.; Fernandes, L.; Madhan, R.; Desa, E.S.; Dabolkar, N.A.; Navelkar, G.S.; Naik, L.; Shetye, V.G.; Shetty, N.B.; Prabhudesai, S.P.; Nagvekar, S.; Vimalakumari, D.

The seabed resident event driven profiling system (SREP) described here offers a novel, optimized approach to profiling in coastal waters from seabed to sea surface during the rough seas encountered in the southwest monsoon season (June...

Selection of initial events of accelerator driven subcritical system

International Nuclear Information System (INIS)

Wang Qianglong; Hu Liqin; Wang Jiaqun; Li Yazhou; Yang Zhiyi

2013-01-01

The Probabilistic Safety Assessment (PSA) is an important tool in reactor safety analysis and a significant reference to the design and operation of reactor. It is the origin and foundation of the PSA for a reactor to select the initial events. Accelerator Driven Subcritical System (ADS) has advanced design characteristics, complicated subsystems and little engineering and operating experience, which makes it much more difficult to identify the initial events of ADS. Based on the current design project of ADS, the system's safety characteristics and special issues were analyzed in this article. After a series of deductions with Master Logic Diagram (MLD) and considering the relating experience of other advanced research reactors, a preliminary initial events was listed finally, which provided the foundation for the next safety assessment. (authors)

Observational and Dynamical Wave Climatologies. VOS vs Satellite Data

Science.gov (United States)

Grigorieva, Victoria; Badulin, Sergei; Chernyshova, Anna

2013-04-01

The understanding physics of wind-driven waves is crucially important for fundamental science and practical applications. This is why experimental efforts are targeted at both getting reliable information on sea state and elaborating effective tools of the sea wave forecasting. The global Visual Wave Observations and satellite data from the GLOBWAVE project of the European Space Agency are analyzed in the context of these two viewpoints. Within the first "observational" aspect we re-analyze conventional climatologies of all basic wave parameters for the last decades [5]. An alternative "dynamical" climatology is introduced as a tool of prediction of dynamical features of sea waves on global scales. The features of wave dynamics are studied in terms of one-parametric dependencies of wave heights on wave periods following the theoretical concept of self-similar wind-driven seas [3, 1, 4] and recently proposed approach to analysis of Voluntary Observing Ship (VOS) data [2]. Traditional "observational" climatologies based on VOS and satellite data collections demonstrate extremely consistent pictures for significant wave heights and dominant periods. On the other hand, collocated satellite and VOS data show significant differences in wave heights, wind speeds and, especially, in wave periods. Uncertainties of visual wave observations can explain these differences only partially. We see the key reason of this inconsistency in the methods of satellite data processing which are based on formal application of data interpolation methods rather than on up-to-date physics of wind-driven waves. The problem is considered within the alternative climatology approach where dynamical criteria of wave height-to-period linkage are used for retrieving wave periods and constructing physically consistent dynamical climatology. The key dynamical parameter - exponent R of one-parametric dependence Hs ~ TR shows dramatically less pronounced latitudinal dependence as compared to observed Hs

Event-driven charge-coupled device design and applications therefor

Science.gov (United States)

Doty, John P. (Inventor); Ricker, Jr., George R. (Inventor); Burke, Barry E. (Inventor); Prigozhin, Gregory Y. (Inventor)

2005-01-01

An event-driven X-ray CCD imager device uses a floating-gate amplifier or other non-destructive readout device to non-destructively sense a charge level in a charge packet associated with a pixel. The output of the floating-gate amplifier is used to identify each pixel that has a charge level above a predetermined threshold. If the charge level is above a predetermined threshold the charge in the triggering charge packet and in the charge packets from neighboring pixels need to be measured accurately. A charge delay register is included in the event-driven X-ray CCD imager device to enable recovery of the charge packets from neighboring pixels for accurate measurement. When a charge packet reaches the end of the charge delay register, control logic either dumps the charge packet, or steers the charge packet to a charge FIFO to preserve it if the charge packet is determined to be a packet that needs accurate measurement. A floating-diffusion amplifier or other low-noise output stage device, which converts charge level to a voltage level with high precision, provides final measurement of the charge packets. The voltage level is eventually digitized by a high linearity ADC.

Sampled-data consensus in switching networks of integrators based on edge events

Science.gov (United States)

Xiao, Feng; Meng, Xiangyu; Chen, Tongwen

2015-02-01

This paper investigates the event-driven sampled-data consensus in switching networks of multiple integrators and studies both the bidirectional interaction and leader-following passive reaction topologies in a unified framework. In these topologies, each information link is modelled by an edge of the information graph and assigned a sequence of edge events, which activate the mutual data sampling and controller updates of the two linked agents. Two kinds of edge-event-detecting rules are proposed for the general asynchronous data-sampling case and the synchronous periodic event-detecting case. They are implemented in a distributed fashion, and their effectiveness in reducing communication costs and solving consensus problems under a jointly connected topology condition is shown by both theoretical analysis and simulation examples.

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.

NEVESIM: event-driven neural simulation framework with a Python interface.

Science.gov (United States)

Pecevski, Dejan; Kappel, David; Jonke, Zeno

2014-01-01

NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.

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...

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.

Driven Quantum Dynamics: Will It Blend?

Directory of Open Access Journals (Sweden)

Leonardo Banchi

2017-10-01

Full Text Available Randomness is an essential tool in many disciplines of modern sciences, such as cryptography, black hole physics, random matrix theory, and Monte Carlo sampling. In quantum systems, random operations can be obtained via random circuits thanks to so-called q-designs and play a central role in condensed-matter physics and in the fast scrambling conjecture for black holes. Here, we consider a more physically motivated way of generating random evolutions by exploiting the many-body dynamics of a quantum system driven with stochastic external pulses. We combine techniques from quantum control, open quantum systems, and exactly solvable models (via the Bethe ansatz to generate Haar-uniform random operations in driven many-body systems. We show that any fully controllable system converges to a unitary q-design in the long-time limit. Moreover, we study the convergence time of a driven spin chain by mapping its random evolution into a semigroup with an integrable Liouvillian and finding its gap. Remarkably, we find via Bethe-ansatz techniques that the gap is independent of q. We use mean-field techniques to argue that this property may be typical for other controllable systems, although we explicitly construct counterexamples via symmetry-breaking arguments to show that this is not always the case. Our findings open up new physical methods to transform classical randomness into quantum randomness, via a combination of quantum many-body dynamics and random driving.

Dynamic Structure Factor and Transport Coefficients of a Homogeneously Driven Granular Fluid in Steady State

Science.gov (United States)

Vollmayr-Lee, Katharina; Zippelius, Annette; Aspelmeier, Timo

2011-03-01

We study the dynamic structure factor of a granular fluid of hard spheres, driven into a stationary nonequilibrium state by balancing the energy loss due to inelastic collisions with the energy input due to driving. The driving is chosen to conserve momentum, so that fluctuating hydrodynamics predicts the existence of sound modes. We present results of computer simulations which are based on an event driven algorithm. The dynamic structure factor F (q , ω) is determined for volume fractions 0.05, 0.1 and 0.2 and coefficients of normal restitution 0.8 and 0.9. We observe sound waves, and compare our results for F (q , ω) with the predictions of generalized fluctuating hydrodynamics which takes into account that temperature fluctuations decay either diffusively or with a finite relaxation rate, depending on wave number and inelasticity. We determine the speed of sound and the transport coefficients and compare them to the results of kinetic theory. K.V.L. thanks the Institute of Theoretical Physics, University of Goettingen, for financial support and hospitality.

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.

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 ...

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

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...

Hybridized Kibble-Zurek scaling in the driven critical dynamics across an overlapping critical region

Science.gov (United States)

Zhai, Liang-Jun; Wang, Huai-Yu; Yin, Shuai

2018-04-01

The conventional Kibble-Zurek scaling describes the scaling behavior in the driven dynamics across a single critical region. In this paper, we study the driven dynamics across an overlapping critical region, in which a critical region (Region A) is overlaid by another critical region (Region B). We develop a hybridized Kibble-Zurek scaling (HKZS) to characterize the scaling behavior in the driven process. According to the HKZS, the driven dynamics in the overlapping region can be described by the critical theories for both Region A and Region B simultaneously. This results in a constraint on the scaling function in the overlapping critical region. We take the quantum Ising chain in an imaginary longitudinal field as an example. In this model, the critical region of the Yang-Lee edge singularity and the critical region of the ferromagnetic-paramagnetic phase transition overlap with each other. We numerically confirm the HKZS by simulating the driven dynamics in this overlapping critical region. The HKZSs in other models are also discussed.

Error Analysis of Satellite Precipitation-Driven Modeling of Flood Events in Complex Alpine Terrain

Directory of Open Access Journals (Sweden)

Yiwen Mei

2016-03-01

Full Text Available The error in satellite precipitation-driven complex terrain flood simulations is characterized in this study for eight different global satellite products and 128 flood events over the Eastern Italian Alps. The flood events are grouped according to two flood types: rain floods and flash floods. The satellite precipitation products and runoff simulations are evaluated based on systematic and random error metrics applied on the matched event pairs and basin-scale event properties (i.e., rainfall and runoff cumulative depth and time series shape. Overall, error characteristics exhibit dependency on the flood type. Generally, timing of the event precipitation mass center and dispersion of the time series derived from satellite precipitation exhibits good agreement with the reference; the cumulative depth is mostly underestimated. The study shows a dampening effect in both systematic and random error components of the satellite-driven hydrograph relative to the satellite-retrieved hyetograph. The systematic error in shape of the time series shows a significant dampening effect. The random error dampening effect is less pronounced for the flash flood events and the rain flood events with a high runoff coefficient. This event-based analysis of the satellite precipitation error propagation in flood modeling sheds light on the application of satellite precipitation in mountain flood hydrology.

Single stock dynamics on high-frequency data: from a compressed coding perspective.

Directory of Open Access Journals (Sweden)

Hsieh Fushing

Full Text Available High-frequency return, trading volume and transaction number are digitally coded via a nonparametric computing algorithm, called hierarchical factor segmentation (HFS, and then are coupled together to reveal a single stock dynamics without global state-space structural assumptions. The base-8 digital coding sequence, which is capable of revealing contrasting aggregation against sparsity of extreme events, is further compressed into a shortened sequence of state transitions. This compressed digital code sequence vividly demonstrates that the aggregation of large absolute returns is the primary driving force for stimulating both the aggregations of large trading volumes and transaction numbers. The state of system-wise synchrony is manifested with very frequent recurrence in the stock dynamics. And this data-driven dynamic mechanism is seen to correspondingly vary as the global market transiting in and out of contraction-expansion cycles. These results not only elaborate the stock dynamics of interest to a fuller extent, but also contradict some classical theories in finance. Overall this version of stock dynamics is potentially more coherent and realistic, especially when the current financial market is increasingly powered by high-frequency trading via computer algorithms, rather than by individual investors.

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

Temporal changes in vegetation of a virgin beech woodland remnant: stand-scale stability with intensive fine-scale dynamics governed by stand dynamic events

Directory of Open Access Journals (Sweden)

Tibor Standovár

2017-03-01

Full Text Available The aim of this resurvey study is to check if herbaceous vegetation on the forest floor exhibits overall stability at the stand-scale in spite of intensive dynamics at the scale of individual plots and stand dynamic events (driven by natural fine scale canopy gap dynamics. In 1996, we sampled a 1.5 ha patch using 0.25 m² plots placed along a 5 m × 5 m grid in the best remnant of central European montane beech woods in Hungary. All species in the herbaceous layer and their cover estimates were recorded. Five patches representing different stand developmental situations (SDS were selected for resurvey. In 2013, 306 plots were resurveyed by using blocks of four 0.25 m² plots to test the effects of imperfect relocation. We found very intensive fine-scale dynamics in the herbaceous layer with high species turnover and sharp changes in ground layer cover at the local-scale (< 1 m2. A decrease in species richness and herbaceous layer cover, as well as high species turnover, characterized the closing gaps. Colonization events and increasing species richness and herbaceous layer cover prevailed in the two newly created gaps. A pronounced decrease in the total cover, but low species turnover and survival of the majority of the closed forest specialists was detected by the resurvey at the stand-scale. The test aiming at assessing the effect of relocation showed a higher time effect than the effect of imprecise relocation. The very intensive fine-scale dynamics of the studied beech forest are profoundly determined by natural stand dynamics. Extinction and colonisation episodes even out at the stand-scale, implying an overall compositional stability of the herbaceous vegetation at the given spatial and temporal scale. We argue that fine-scale gap dynamics, driven by natural processes or applied as a management method, can warrant the survival of many closed forest specialist species in the long-run. Nomenclature: Flora Europaea (Tutin et al. 2010 for

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

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

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

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

A Full Parallel Event Driven Readout Technique for Area Array SPAD FLIM Image Sensors

Directory of Open Access Journals (Sweden)

Kaiming Nie

2016-01-01

Full Text Available This paper presents a full parallel event driven readout method which is implemented in an area array single-photon avalanche diode (SPAD image sensor for high-speed fluorescence lifetime imaging microscopy (FLIM. The sensor only records and reads out effective time and position information by adopting full parallel event driven readout method, aiming at reducing the amount of data. The image sensor includes four 8 × 8 pixel arrays. In each array, four time-to-digital converters (TDCs are used to quantize the time of photons’ arrival, and two address record modules are used to record the column and row information. In this work, Monte Carlo simulations were performed in Matlab in terms of the pile-up effect induced by the readout method. The sensor’s resolution is 16 × 16. The time resolution of TDCs is 97.6 ps and the quantization range is 100 ns. The readout frame rate is 10 Mfps, and the maximum imaging frame rate is 100 fps. The chip’s output bandwidth is 720 MHz with an average power of 15 mW. The lifetime resolvability range is 5–20 ns, and the average error of estimated fluorescence lifetimes is below 1% by employing CMM to estimate lifetimes.

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.

Dynamic posturography using a new movable multidirectional platform driven by gravity.

NARCIS (Netherlands)

Commissaris, D.A.C.M.; Nieuwenhuijzen, P.H.J.A.; Overeem, S.; Vos, A. de; Duysens, J.E.J.; Bloem, B.R.

2002-01-01

Human upright balance control can be quantified using movable platforms driven by servo-controlled torque motors (dynamic posturography). We introduce a new movable platform driven by the force of gravity acting upon the platform and the subject standing on it. The platform consists of a 1 m2 metal

Dynamic posturography using a new movable multidirectional platform driven by gravity

NARCIS (Netherlands)

Commissaris, D.A.C.M.; Nieuwenhuijzen, P.H.J.A.; Overeem, S.; Vos, A. de; Duysens, J.E.J.; Bloem, B.R.

2002-01-01

Human upright balance control can be quantified using movable platforms driven by servo-controlled torque motors (dynamic posturography). We introduce a new movable platform driven by the force of gravity acting upon the platform and the subject standing on it. The platform consists of a 1 m(2)

Dynamic signatures of driven vortex motion.

Energy Technology Data Exchange (ETDEWEB)

Crabtree, G. W.; Kwok, W. K.; Lopez, D.; Olsson, R. J.; Paulius, L. M.; Petrean, A. M.; Safar, H.

1999-09-16

We probe the dynamic nature of driven vortex motion in superconductors with a new type of transport experiment. An inhomogeneous Lorentz driving force is applied to the sample, inducing vortex velocity gradients that distinguish the hydrodynamic motion of the vortex liquid from the elastic and-plastic motion of the vortex solid. We observe elastic depinning of the vortex lattice at the critical current, and shear induced plastic slip of the lattice at high Lorentz force gradients.

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

Ballooning-mirror instability and internally driven Pc 4--5 wave events

International Nuclear Information System (INIS)

Cheng, C.Z.; Qian, Q.; Takahashi, K.; Lui, A.T.Y.

1994-03-01

A kinetic-MHD field-aligned eigenmode stability analysis of low frequency ballooning-mirror instabilities has been performed for anisotropic pressure plasma sin the magnetosphere. The ballooning mode is mainly a transverse wave driven unstable by pressure gradient in the bad curvature region. The mirror mode with a dominant compressional magnetic field perturbation is excited when the product of plasma beta and pressure anisotropy (P perpendicular /P parallel > 1) is large. From the AMPTE/CCE particle and magnetic field data observed during Pc 4--5 wave events the authors compute the ballooning-mirror instability parameters and perform a correlation study with the theoretical instability threshold. They find that compressional Pc 5 waves approximately satisfy the ballooning-mirror instability condition, and transverse Pc 4--5 waves are probably related to resonant ballooning instabilities with small pressure anisotropy

Vlasov dynamics of periodically driven systems

Science.gov (United States)

Banerjee, Soumyadip; Shah, Kushal

2018-04-01

Analytical solutions of the Vlasov equation for periodically driven systems are of importance in several areas of plasma physics and dynamical systems and are usually approximated using ponderomotive theory. In this paper, we derive the plasma distribution function predicted by ponderomotive theory using Hamiltonian averaging theory and compare it with solutions obtained by the method of characteristics. Our results show that though ponderomotive theory is relatively much easier to use, its predictions are very restrictive and are likely to be very different from the actual distribution function of the system. We also analyse all possible initial conditions which lead to periodic solutions of the Vlasov equation for periodically driven systems and conjecture that the irreducible polynomial corresponding to the initial condition must only have squares of the spatial and momentum coordinate. The resulting distribution function for other initial conditions is aperiodic and can lead to complex relaxation processes within the plasma.

FIREDATA, Nuclear Power Plant Fire Event Data Base

International Nuclear Information System (INIS)

Wheelis, W.T.

2001-01-01

1 - Description of program or function: FIREDATA contains raw fire event data from 1965 through June 1985. These data were obtained from a number of reference sources including the American Nuclear Insurers, Licensee Event Reports, Nuclear Power Experience, Electric Power Research Institute Fire Loss Data and then collated into one database developed in the personal computer database management system, dBASE III. FIREDATA is menu-driven and asks interactive questions of the user that allow searching of the database for various aspects of a fire such as: location, mode of plant operation at the time of the fire, means of detection and suppression, dollar loss, etc. Other features include the capability of searching for single or multiple criteria (using Boolean 'and' or 'or' logical operations), user-defined keyword searches of fire event descriptions, summary displays of fire event data by plant name of calendar date, and options for calculating the years of operating experience for all commercial nuclear power plants from any user-specified date and the ability to display general plant information. 2 - Method of solution: The six database files used to store nuclear power plant fire event information, FIRE, DESC, SUM, OPEXPER, OPEXBWR, and EXPERPWR, are accessed by software to display information meeting user-specified criteria or to perform numerical calculations (e.g., to determine the operating experience of a nuclear plant). FIRE contains specific searchable data relating to each of 354 fire events. A keyword concept is used to search each of the 31 separate entries or fields. DESC contains written descriptions of each of the fire events. SUM holds basic plant information for all plants proposed, under construction, in operation, or decommissioned. This includes the initial criticality and commercial operation dates, the physical location of the plant, and its operating capacity. OPEXPER contains date information and data on how various plant locations are

Tool for Constructing Data Albums for Significant Weather Events

Science.gov (United States)

Kulkarni, A.; Ramachandran, R.; Conover, H.; McEniry, M.; Goodman, H.; Zavodsky, B. T.; Braun, S. A.; Wilson, B. D.

2012-12-01

Case study analysis and climatology studies are common approaches used in Atmospheric Science research. Research based on case studies involves a detailed description of specific weather events using data from different sources, to characterize physical processes in play for a given event. Climatology-based research tends to focus on the representativeness of a given event, by studying the characteristics and distribution of a large number of events. To gather relevant data and information for case studies and climatology analysis is tedious and time consuming; current Earth Science data systems are not suited to assemble multi-instrument, multi mission datasets around specific events. For example, in hurricane science, finding airborne or satellite data relevant to a given storm requires searching through web pages and data archives. Background information related to damages, deaths, and injuries requires extensive online searches for news reports and official storm summaries. We will present a knowledge synthesis engine to create curated "Data Albums" to support case study analysis and climatology studies. The technological challenges in building such a reusable and scalable knowledge synthesis engine are several. First, how to encode domain knowledge in a machine usable form? This knowledge must capture what information and data resources are relevant and the semantic relationships between the various fragments of information and data. Second, how to extract semantic information from various heterogeneous sources including unstructured texts using the encoded knowledge? Finally, how to design a structured database from the encoded knowledge to store all information and to support querying? The structured database must allow both knowledge overviews of an event as well as drill down capability needed for detailed analysis. An application ontology driven framework is being used to design the knowledge synthesis engine. The knowledge synthesis engine is being

Minimizing cache misses in an event-driven network server: A case study of TUX

DEFF Research Database (Denmark)

Bhatia, Sapan; Consel, Charles; Lawall, Julia Laetitia

2006-01-01

We analyze the performance of CPU-bound network servers and demonstrate experimentally that the degradation in the performance of these servers under high-concurrency workloads is largely due to inefficient use of the hardware caches. We then describe an approach to speeding up event-driven network...... servers by optimizing their use of the L2 CPU cache in the context of the TUX Web server, known for its robustness to heavy load. Our approach is based on a novel cache-aware memory allocator and a specific scheduling strategy that together ensure that the total working data set of the server stays...

Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

Directory of Open Access Journals (Sweden)

Xiaolu Zhou

2017-03-01

Full Text Available Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation and a social event (Pope Francis’ visit to the US in the New York City—Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information, this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information.

Distributed Data Collection For Next Generation ATLAS EventIndex Project

CERN Document Server

Fernandez Casani, Alvaro; The ATLAS collaboration

2018-01-01

The ATLAS EventIndex currently runs in production in order to build a complete catalogue of events for experiments with large amounts of data. The current approach is to index all final produced data files at CERN Tier0, and at hundreds of grid sites, with a distributed data collection architecture using Object Stores to temporary maintain the conveyed information, with references to them sent with a Messaging System. The final backend of all the indexed data is a central Hadoop infrastructure at CERN; an Oracle relational database is used for faster access to a subset of this information. In the future of ATLAS, instead of files, the event should be the atomic information unit for metadata. This motivation arises in order to accommodate future data processing and storage technologies. Files will no longer be static quantities, possibly dynamically aggregating data, and also allowing event-level granularity processing in heavily parallel computing environments. It also simplifies the handling of loss and or e...

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...

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

Event-Driven Control for Networked Control Systems With Quantization and Markov Packet Losses.

Science.gov (United States)

Yang, Hongjiu; Xu, Yang; Zhang, Jinhui

2016-05-23

In this paper, event-driven is used in a networked control system (NCS) which is subjected to the effect of quantization and packet losses. A discrete event-detector is used to monitor specific events in the NCS. Both an arbitrary region quantizer and Markov jump packet losses are also considered for the NCS. Based on zoom strategy and Lyapunov theory, a complete proof is given to guarantee mean square stability of the closed-loop system. Stabilization of the NCS is ensured by designing a feedback controller. Lastly, an inverted pendulum model is given to show the advantages and effectiveness of the proposed results.

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

Dynamics of a strongly driven two-component Bose-Einstein condensate

International Nuclear Information System (INIS)

Salmond, G.L.; Holmes, C.A.; Milburn, G.J.

2002-01-01

We consider a two-component Bose-Einstein condensate in two spatially localized modes of a double-well potential, with periodic modulation of the tunnel coupling between the two modes. We treat the driven quantum field using a two-mode expansion and define the quantum dynamics in terms of the Floquet Operator for the time periodic Hamiltonian of the system. It has been shown that the corresponding semiclassical mean-field dynamics can exhibit regions of regular and chaotic motion. We show here that the quantum dynamics can exhibit dynamical tunneling between regions of regular motion, centered on fixed points (resonances) of the semiclassical dynamics

Implementing a Dynamic Database-Driven Course Using LAMP

Science.gov (United States)

Laverty, Joseph Packy; Wood, David; Turchek, John

2011-01-01

This paper documents the formulation of a database driven open source architecture web development course. The design of a web-based curriculum faces many challenges: a) relative emphasis of client and server-side technologies, b) choice of a server-side language, and c) the cost and efficient delivery of a dynamic web development, database-driven…

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.

On the Role of Ionospheric Ions in Sawtooth Events

Science.gov (United States)

Lund, E. J.; Nowrouzi, N.; Kistler, L. M.; Cai, X.; Frey, H. U.

2018-01-01

Simulations have suggested that feedback of heavy ions originating in the ionosphere is an important mechanism for driving sawtooth injections. However, this feedback may only be necessary for events driven by coronal mass ejections (CMEs), whereas in events driven by streaming interaction regions (SIRs), solar wind variability may suffice to drive these injections. Here we present case studies of two sawtooth events for which in situ data are available in both the magnetotail (Cluster) and the nightside auroral region (FAST), as well as global auroral images (IMAGE). One event, on 1 October 2001, was driven by a CME; the other, on 24 October 2002, was driven by an SIR. The available data do not support the hypothesis that heavy ion feedback is necessary to drive either event. This result is consistent with simulations of the SIR-driven event but disagrees with simulation results for a different CME-driven event. We also find that in an overwhelming majority of the sawtooth injections for which Cluster tail data are available, the O+ observed in the tail comes from the cusp rather than the nightside auroral region, which further casts doubt on the hypothesis that ionospheric heavy ion feedback is the cause of sawtooth injections.

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

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

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.

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.

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)

Dynamic Systems Driven by Non-Poissonian Impulses

DEFF Research Database (Denmark)

Nielsen, Søren R.K.; Iwankiewicz, R.

interarrival times. The moment equations for the augmented Poisson driven system are derived and closed by an ordinary cumulant neglect closure at the order N=4. The obtained moments are compared with these obtained by Monte Carlo simulations for both the original process with lognormally distributed......Dynamic systems under random trains of impulses driven by renewal point processes are studied. Then the system state variables no longer form a Markov vector as it is in the case of Poisson impulses. A general format is given for the replacing an ordinary renewal process by an equivalent Poisson...... process at the expense of the introduction of auxiliary state variables. A technique is devised for truncating the hierarchy of stochastic equations governing the auxiliary state variables. For the generalized Erlang process, suitable for approximating a wide class of renewal processes, the technique...

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.

Quantum recurrence and fractional dynamic localization in ac-driven perfect state transfer Hamiltonians

International Nuclear Information System (INIS)

Longhi, Stefano

2014-01-01

Quantum recurrence and dynamic localization are investigated in a class of ac-driven tight-binding Hamiltonians, the Krawtchouk quantum chain, which in the undriven case provides a paradigmatic Hamiltonian model that realizes perfect quantum state transfer and mirror inversion. The equivalence between the ac-driven single-particle Krawtchouk Hamiltonian H -hat (t) and the non-interacting ac-driven bosonic junction Hamiltonian enables to determine in a closed form the quasi energy spectrum of H -hat (t) and the conditions for exact wave packet reconstruction (dynamic localization). In particular, we show that quantum recurrence, which is predicted by the general quantum recurrence theorem, is exact for the Krawtchouk quantum chain in a dense range of the driving amplitude. Exact quantum recurrence provides perfect wave packet reconstruction at a frequency which is fractional than the driving frequency, a phenomenon that can be referred to as fractional dynamic localization

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.

Network evolution driven by dynamics applied to graph coloring

International Nuclear Information System (INIS)

Wu Jian-She; Li Li-Guang; Yu Xin; Jiao Li-Cheng; Wang Xiao-Hua

2013-01-01

An evolutionary network driven by dynamics is studied and applied to the graph coloring problem. From an initial structure, both the topology and the coupling weights evolve according to the dynamics. On the other hand, the dynamics of the network are determined by the topology and the coupling weights, so an interesting structure-dynamics co-evolutionary scheme appears. By providing two evolutionary strategies, a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics. The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors. In this way, a co-evolution phenomenon is applied to the graph coloring problem. The proposed scheme is tested on several benchmark graphs for graph coloring

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

Discovering governing equations from data by sparse identification of nonlinear dynamics

Science.gov (United States)

Brunton, Steven

The ability to discover physical laws and governing equations from data is one of humankind's greatest intellectual achievements. A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technology, including aircraft, combustion engines, satellites, and electrical power. There are many more critical data-driven problems, such as understanding cognition from neural recordings, inferring patterns in climate, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in these efforts. This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions. This perspective, combining dynamical systems with machine learning and sparse sensing, is explored with the overarching goal of real-time closed-loop feedback control of complex systems. This is joint work with Joshua L. Proctor and J. Nathan Kutz. Video Abstract: https://www.youtube.com/watch?v=gSCa78TIldg

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.

Dynamic Performance of the Standalone Wind Power Driven Heat Pump

OpenAIRE

H. Li; P.E. Campana; S. Berretta; Y. Tan; J. Yan

2016-01-01

Reducing energy consumption and increasing use of renewable energyin the building sector arecrucial to the mitigation of climate change. Wind power driven heat pumps have been considered as a sustainable measure to supply heat for detached houses, especially those that even don’t have access to the grid. This work is to investigate the dynamic performance of a heat pump system directly driven by a wind turbine. The heat demand of a detached single family house was simulated in details. Accord...

Authentic execution of distributed event-driven applications with a small TCB

OpenAIRE

Noorman, Job; Mühlberg, Tobias; Piessens, Frank

2017-01-01

This paper presents an approach to provide strong assurance of the secure execution of distributed event-driven applications on shared infrastructures, while relying on a small Trusted Computing Base. We build upon and extend security primitives provided by a Protected Module Architecture (PMA) to guarantee authenticity and integrity properties of applications, and to secure control of input and output devices used by these applications. More specifically, we want to guarantee that if an outp...

A thermostatted kinetic theory model for event-driven pedestrian dynamics

Science.gov (United States)

Bianca, Carlo; Mogno, Caterina

2018-06-01

This paper is devoted to the modeling of the pedestrian dynamics by means of the thermostatted kinetic theory. Specifically the microscopic interactions among pedestrians and an external force field are modeled for simulating the evacuation of pedestrians from a metro station. The fundamentals of the stochastic game theory and the thermostatted kinetic theory are coupled for the derivation of a specific mathematical model which depicts the time evolution of the distribution of pedestrians at different exits of a metro station. The perturbation theory is employed in order to establish the stability analysis of the nonequilibrium stationary states in the case of a metro station consisting of two exits. A general sensitivity analysis on the initial conditions, the magnitude of the external force field and the number of exits is presented by means of numerical simulations which, in particular, show how the asymptotic distribution and the convergence time are affected by the presence of an external force field. The results show how, in evacuation conditions, the interaction dynamics among pedestrians can be negligible with respect to the external force. The important role of the thermostat term in allowing the reaching of the nonequilibrium stationary state is stressed out. Research perspectives are underlined at the end of paper, in particular for what concerns the derivation of frameworks that take into account the definition of local external actions and the introduction of the space and velocity dynamics.

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Ahmadreza Vajdi

2018-05-01

Full Text Available We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP. Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks.

Science.gov (United States)

Vajdi, Ahmadreza; Zhang, Gongxuan; Zhou, Junlong; Wei, Tongquan; Wang, Yongli; Wang, Tianshu

2018-05-04

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

Science.gov (United States)

Zhang, Gongxuan; Wang, Yongli; Wang, Tianshu

2018-01-01

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach. PMID:29734718

Analysis of current-driven oscillatory dynamics of single-layer homoepitaxial islands on crystalline conducting substrates

Science.gov (United States)

Dasgupta, Dwaipayan; Kumar, Ashish; Maroudas, Dimitrios

2018-03-01

We report results of a systematic study on the complex oscillatory current-driven dynamics of single-layer homoepitaxial islands on crystalline substrate surfaces and the dependence of this driven dynamical behavior on important physical parameters, including island size, substrate surface orientation, and direction of externally applied electric field. The analysis is based on a nonlinear model of driven island edge morphological evolution that accounts for curvature-driven edge diffusion, edge electromigration, and edge diffusional anisotropy. Using a linear theory of island edge morphological stability, we calculate a critical island size at which the island's equilibrium edge shape becomes unstable, which sets a lower bound for the onset of time-periodic oscillatory dynamical response. Using direct dynamical simulations, we study the edge morphological dynamics of current-driven single-layer islands at larger-than-critical size, and determine the actual island size at which the migrating islands undergo a transition from steady to time-periodic asymptotic states through a subcritical Hopf bifurcation. At the highest symmetry of diffusional anisotropy examined, on {111} surfaces of face-centered cubic crystalline substrates, we find that more complex stable oscillatory states can be reached through period-doubling bifurcation at island sizes larger than those at the Hopf points. We characterize in detail the island morphology and dynamical response at the stable time-periodic asymptotic states, determine the range of stability of these oscillatory states terminated by island breakup, and explain the morphological features of the stable oscillating islands on the basis of linear stability theory.

Science to Support Management of Receiving Waters in an Event-Driven Ecosystem: From Land to River to Sea

Directory of Open Access Journals (Sweden)

Stuart E. Bunn

2013-06-01

Full Text Available Managing receiving-water quality, ecosystem health and ecosystem service delivery is challenging in regions where extreme rainfall and runoff events occur episodically, confounding and often intensifying land-degradation impacts. We synthesize the approaches used in river, reservoir and coastal water management in the event-driven subtropics of Australia, and the scientific research underpinning them. Land-use change has placed the receiving waters of Moreton Bay, an internationally-significant coastal wetland, at risk of ecological degradation through increased nutrient and sediment loads. The event-driven climate exacerbates this issue, as the waterways and ultimately Moreton Bay receive large inputs of nutrients and sediment during events, well above those received throughout stable climatic periods. Research on the water quality and ecology of the region’s rivers and coastal waters has underpinned the development of a world-renowned monitoring program and, in combination with catchment-source tracing methods and modeling, has revealed the key mechanisms and management strategies by which receiving-water quality, ecosystem health and ecosystem services can be maintained and improved. These approaches provide a useful framework for management of water bodies in other regions driven by episodic events, or where novel stressors are involved (e.g., climate change, urbanization, to support sustained ecosystem service delivery and restoration of aquatic ecosystems.

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.

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).

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

Multi-day activity scheduling reactions to planned activities and future events in a dynamic model of activity-travel behavior

Science.gov (United States)

Nijland, Linda; Arentze, Theo; Timmermans, Harry

2014-01-01

Modeling multi-day planning has received scarce attention in activity-based transport demand modeling so far. However, new dynamic activity-based approaches are being developed at the current moment. The frequency and inflexibility of planned activities and events in activity schedules of individuals indicate the importance of incorporating those pre-planned activities in the new generation of dynamic travel demand models. Elaborating and combining previous work on event-driven activity generation, the aim of this paper is to develop and illustrate an extension of a need-based model of activity generation that takes into account possible influences of pre-planned activities and events. This paper describes the theory and shows the results of simulations of the extension. The simulation was conducted for six different activities, and the parameter values used were consistent with an earlier estimation study. The results show that the model works well and that the influences of the parameters are consistent, logical, and have clear interpretations. These findings offer further evidence of face and construct validity to the suggested modeling approach.

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.

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.

Temporal integration: intentional sound discrimination does not modulate stimulus-driven processes in auditory event synthesis.

Science.gov (United States)

Sussman, Elyse; Winkler, István; Kreuzer, Judith; Saher, Marieke; Näätänen, Risto; Ritter, Walter

2002-12-01

Our previous study showed that the auditory context could influence whether two successive acoustic changes occurring within the temporal integration window (approximately 200ms) were pre-attentively encoded as a single auditory event or as two discrete events (Cogn Brain Res 12 (2001) 431). The aim of the current study was to assess whether top-down processes could influence the stimulus-driven processes in determining what constitutes an auditory event. Electroencepholagram (EEG) was recorded from 11 scalp electrodes to frequently occurring standard and infrequently occurring deviant sounds. Within the stimulus blocks, deviants either occurred only in pairs (successive feature changes) or both singly and in pairs. Event-related potential indices of change and target detection, the mismatch negativity (MMN) and the N2b component, respectively, were compared with the simultaneously measured performance in discriminating the deviants. Even though subjects could voluntarily distinguish the two successive auditory feature changes from each other, which was also indicated by the elicitation of the N2b target-detection response, top-down processes did not modify the event organization reflected by the MMN response. Top-down processes can extract elemental auditory information from a single integrated acoustic event, but the extraction occurs at a later processing stage than the one whose outcome is indexed by MMN. Initial processes of auditory event-formation are fully governed by the context within which the sounds occur. Perception of the deviants as two separate sound events (the top-down effects) did not change the initial neural representation of the same deviants as one event (indexed by the MMN), without a corresponding change in the stimulus-driven sound organization.

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

Elastically driven intermittent microscopic dynamics in soft solids

Science.gov (United States)

Bouzid, Mehdi; Colombo, Jader; Barbosa, Lucas Vieira; Del Gado, Emanuela

2017-06-01

Soft solids with tunable mechanical response are at the core of new material technologies, but a crucial limit for applications is their progressive aging over time, which dramatically affects their functionalities. The generally accepted paradigm is that such aging is gradual and its origin is in slower than exponential microscopic dynamics, akin to the ones in supercooled liquids or glasses. Nevertheless, time- and space-resolved measurements have provided contrasting evidence: dynamics faster than exponential, intermittency and abrupt structural changes. Here we use 3D computer simulations of a microscopic model to reveal that the timescales governing stress relaxation, respectively, through thermal fluctuations and elastic recovery are key for the aging dynamics. When thermal fluctuations are too weak, stress heterogeneities frozen-in upon solidification can still partially relax through elastically driven fluctuations. Such fluctuations are intermittent, because of strong correlations that persist over the timescale of experiments or simulations, leading to faster than exponential dynamics.

Dynamics of electrostatically driven granular media: Effects of humidity

International Nuclear Information System (INIS)

Howell, D. W.; Aronson, Igor S.; Crabtree, G. W.

2001-01-01

We performed experimental studies of the effect of humidity on the dynamics of electrostatically driven granular materials. Both conducting and dielectric particles undergo a phase transition from an immobile state (granular solid) to a fluidized state (granular gas) with increasing applied field. Spontaneous precipitation of solid clusters from the gas phase occurs as the external driving is decreased. The clustering dynamics in conducting particles is primarily controlled by screening of the electric field but is aided by cohesion due to humidity. It is shown that humidity effects dominate the clustering process with dielectric particles

Self-sorting of dynamic metallosupramolecular libraries (DMLs) via metal-driven selection.

Science.gov (United States)

Kocsis, Istvan; Dumitrescu, Dan; Legrand, Yves-Marie; van der Lee, Arie; Grosu, Ion; Barboiu, Mihail

2014-03-11

"Metal-driven" selection between finite mononuclear and polymeric metallosupramolecular species can be quantitatively achieved in solution and in a crystalline state via coupled coordination/stacking interactional algorithms within dynamic metallosupramolecular libraries - DMLs.

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...

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.

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....

Product quality driven design of bakery operations using dynamic optimization

NARCIS (Netherlands)

Hadiyanto, M.; Esveld, D.C.; Boom, R.M.; Straten, van G.; Boxtel, van A.J.B.

2008-01-01

Abstract Quality driven design uses specified product qualities as a starting point for process design. By backward reasoning the required process conditions and processing system were found. In this work dynamic optimization was used as a tool to generate processing solutions for baking processes

Molecular dynamics for irradiation driven chemistry: application to the FEBID process*

Science.gov (United States)

Sushko, Gennady B.; Solov'yov, Ilia A.; Solov'yov, Andrey V.

2016-10-01

A new molecular dynamics (MD) approach for computer simulations of irradiation driven chemical transformations of complex molecular systems is suggested. The approach is based on the fact that irradiation induced quantum transformations can often be treated as random, fast and local processes involving small molecules or molecular fragments. We advocate that the quantum transformations, such as molecular bond breaks, creation and annihilation of dangling bonds, electronic charge redistributions, changes in molecular topologies, etc., could be incorporated locally into the molecular force fields that describe the classical MD of complex molecular systems under irradiation. The proposed irradiation driven molecular dynamics (IDMD) methodology is designed for the molecular level description of the irradiation driven chemistry. The IDMD approach is implemented into the MBN Explorer software package capable to operate with a large library of classical potentials, many-body force fields and their combinations. IDMD opens a broad range of possibilities for modelling of irradiation driven modifications and chemistry of complex molecular systems ranging from radiotherapy cancer treatments to the modern technologies such as focused electron beam deposition (FEBID). As an example, the new methodology is applied for studying the irradiation driven chemistry caused by FEBID of tungsten hexacarbonyl W(CO)6 precursor molecules on a hydroxylated SiO2 surface. It is demonstrated that knowing the interaction parameters for the fragments of the molecular system arising in the course of irradiation one can reproduce reasonably well experimental observations and make predictions about the morphology and molecular composition of nanostructures that emerge on the surface during the FEBID process.

Classical and quantum dynamics of driven elliptical billiards

Energy Technology Data Exchange (ETDEWEB)

Lenz, Florian

2009-12-09

Subject of this thesis is the investigation of the classical dynamics of the driven elliptical billiard and the development of a numerical method allowing the propagation of arbitrary initial states in the quantum version of the system. In the classical case, we demonstrate that there is Fermi acceleration in the driven billiard. The corresponding transport process in momentum space shows a surprising crossover from sub- to normal diffusion. This crossover is not parameter induced, but rather occurs dynamically in the evolution of the ensemble. The four-dimensional phase space is analyzed in depth, especially how its composition changes in different velocity regimes. We show that the stickiness properties, which eventually determine the diffusion, are intimately connected with this change of the composition of the phase space with respect to velocity. In the course of the evolution, the accelerating ensemble thus explores regions of varying stickiness, leading to the mentioned crossover in the diffusion. In the quantum case, a series of transformations tailored to the elliptical billiard is applied to circumvent the time-dependent Dirichlet boundary conditions. By means of an expansion ansatz, this eventually yields a large system of coupled ordinary differential equations, which can be solved by standard techniques. (orig.)

Classical and quantum dynamics of driven elliptical billiards

International Nuclear Information System (INIS)

Lenz, Florian

2009-01-01

Subject of this thesis is the investigation of the classical dynamics of the driven elliptical billiard and the development of a numerical method allowing the propagation of arbitrary initial states in the quantum version of the system. In the classical case, we demonstrate that there is Fermi acceleration in the driven billiard. The corresponding transport process in momentum space shows a surprising crossover from sub- to normal diffusion. This crossover is not parameter induced, but rather occurs dynamically in the evolution of the ensemble. The four-dimensional phase space is analyzed in depth, especially how its composition changes in different velocity regimes. We show that the stickiness properties, which eventually determine the diffusion, are intimately connected with this change of the composition of the phase space with respect to velocity. In the course of the evolution, the accelerating ensemble thus explores regions of varying stickiness, leading to the mentioned crossover in the diffusion. In the quantum case, a series of transformations tailored to the elliptical billiard is applied to circumvent the time-dependent Dirichlet boundary conditions. By means of an expansion ansatz, this eventually yields a large system of coupled ordinary differential equations, which can be solved by standard techniques. (orig.)

Dynamic informational system for control and monitoring the tritium removal pilot plant with data transfer and process analyses

International Nuclear Information System (INIS)

Retevoi, Carmen Maria; Stefan, Iuliana; Balteanu, Ovidiu; Stefan, Liviu

2005-01-01

The dynamic informational system with datalogging and supervisory control module includes a motion control module and is a new conception used in tritium removal installation with isotopic exchange and cryogenic distillation. The control system includes an event-driven engine that maintains a real-time database, logs historical data, processes alarm information, and communicates with I/O devices. Also, it displays the operator interfaces and performs tasks that are defined for advanced control algorithms, supervisory control, analysis, and display with data transfer from data acquisition room to the control room. By using the parameters, we compute the deuterium and tritium concentration, respectively, of the liquid at the inlet of the isotopic exchange column and, consequently, we can compute at the outlet of the column, the tritium concentration in the water vapors. (authors)

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

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

Wealth dynamics in a sentiment-driven market

Science.gov (United States)

Goykhman, Mikhail

2017-12-01

We study dynamics of a simulated world with stock and money, driven by the externally given processes which we refer to as sentiments. The considered sentiments influence the buy/sell stock trading attitude, the perceived price uncertainty, and the trading intensity of all or a part of the market participants. We study how the wealth of market participants evolves in time in such an environment. We discuss the opposite perspective in which the parameters of the sentiment processes can be inferred a posteriori from the observed market behavior.

Induced photoemission from driven nonadiabatic dynamics in an avoided crossing system

Energy Technology Data Exchange (ETDEWEB)

Arasaki, Yasuki; Mizuno, Yuta; Takatsuka, Kazuo, E-mail: kaztak@mns2.c.u-tokyo.ac.jp [Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Komaba, 153-8902 Tokyo (Japan); Scheit, Simona [Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Komaba, 153-8902 Tokyo (Japan); Theoretische Chemie, Universität Heidelberg, Im Neuneheimer Feld 229, 69120 Heidelberg (Germany)

2014-12-21

When vibrational dynamics on an ionic state (large dipole moment) is coupled to that on a neutral state (small dipole moment) such as at an avoided crossing in the alkali halide system, the population transfer between the states cause oscillation of the molecular dipole, leading to dipole emission. Such dynamics may be driven by an external field. We study how the coupled wavepacket dynamics is affected by the parameters (intensity, frequency) of the driving field with the aim of making use of the photoemission as an alternative detection scheme of femtosecond and subfemtosecond vibrational and electronic dynamics or as a characteristic optical source.

Effect of thermal fluctuations in spin-torque driven magnetization dynamics

International Nuclear Information System (INIS)

Bonin, R.; Bertotti, G.; Serpico, C.; Mayergoyz, I.D.; D'Aquino, M.

2007-01-01

Nanomagnets with uniaxial symmetry driven by an external field and spin-polarized currents are considered. Anisotropy, applied field, and spin polarization are all aligned along the symmetry axis. Thermal fluctuations are described by adding a Gaussian white noise stochastic term to the Landau-Lifshitz-Gilbert equation for the deterministic dynamics. The corresponding Fokker-Planck equation is derived. It is shown that deterministic dynamics, thermal relaxation, and transition rate between stable states are governed by an effective potential including the effect of current injection

Effect of thermal fluctuations in spin-torque driven magnetization dynamics

Energy Technology Data Exchange (ETDEWEB)

Bonin, R. [INRiM, I-10135 Turin (Italy)]. E-mail: bonin@inrim.it; Bertotti, G. [INRiM, I-10135 Turin (Italy); Serpico, C. [Dipartimento di Ingegneria Elettrica, Universita di Napoli ' Federico II' I-80125 Naples (Italy); Mayergoyz, I.D. [Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742 (United States); D' Aquino, M. [Dipartimento per le Tecnologie, Universita di Napoli ' Parthenope' , I-80133 Naples (Italy)

2007-09-15

Nanomagnets with uniaxial symmetry driven by an external field and spin-polarized currents are considered. Anisotropy, applied field, and spin polarization are all aligned along the symmetry axis. Thermal fluctuations are described by adding a Gaussian white noise stochastic term to the Landau-Lifshitz-Gilbert equation for the deterministic dynamics. The corresponding Fokker-Planck equation is derived. It is shown that deterministic dynamics, thermal relaxation, and transition rate between stable states are governed by an effective potential including the effect of current injection.

Spatially and time-resolved magnetization dynamics driven by spin-orbit torques

Science.gov (United States)

Baumgartner, Manuel; Garello, Kevin; Mendil, Johannes; Avci, Can Onur; Grimaldi, Eva; Murer, Christoph; Feng, Junxiao; Gabureac, Mihai; Stamm, Christian; Acremann, Yves; Finizio, Simone; Wintz, Sebastian; Raabe, Jörg; Gambardella, Pietro

2017-10-01

Current-induced spin-orbit torques are one of the most effective ways to manipulate the magnetization in spintronic devices, and hold promise for fast switching applications in non-volatile memory and logic units. Here, we report the direct observation of spin-orbit-torque-driven magnetization dynamics in Pt/Co/AlOx dots during current pulse injection. Time-resolved X-ray images with 25 nm spatial and 100 ps temporal resolution reveal that switching is achieved within the duration of a subnanosecond current pulse by the fast nucleation of an inverted domain at the edge of the dot and propagation of a tilted domain wall across the dot. The nucleation point is deterministic and alternates between the four dot quadrants depending on the sign of the magnetization, current and external field. Our measurements reveal how the magnetic symmetry is broken by the concerted action of the damping-like and field-like spin-orbit torques and the Dzyaloshinskii-Moriya interaction, and show that reproducible switching events can be obtained for over 1012 reversal cycles.

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

Quantum dynamics of a strongly driven Josephson Junction

Energy Technology Data Exchange (ETDEWEB)

Gosner, Jennifer; Kubala, Bjoern; Ankerhold, Joachim [Institute for Complex Quantum Systems, University of Ulm (Germany)

2015-07-01

A Josephson Junction embedded in a dissipative circuit can be driven to exhibit non-linear oscillations. Classically the non-linear oscillator shows under sufficient strong driving and weak damping dynamical bifurcations and a bistable region similar to the conventional Duffing-oscillator. These features depend sensitively on initial conditions and parameters. The sensitivity of this circuit, called Josephson Bifurcation Amplifier, can be used to amplify an incoming signal, to form a sensing device or even for measuring a quantum system. The quantum dynamics can be described by a dissipative Lindblad master equation. Signatures of the classical bifurcation phenomena appear in the Wigner representation, used to characterize and visualize the resulting behaviour. In order to compare this quantum dynamics to that of the conventional Duffing-oscillator, the complete cosine-nonlinearity of the Josephson Junction is kept for the quantum description while going into a rotating frame.

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.

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

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...

A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

Science.gov (United States)

Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

Buckling Causes Nonlinear Dynamics of Filamentous Viruses Driven through Nanopores.

Science.gov (United States)

McMullen, Angus; de Haan, Hendrick W; Tang, Jay X; Stein, Derek

2018-02-16

Measurements and Langevin dynamics simulations of filamentous viruses driven through solid-state nanopores reveal a superlinear rise in the translocation velocity with driving force. The mobility also scales with the length of the virus in a nontrivial way that depends on the force. These dynamics are consequences of the buckling of the leading portion of a virus as it emerges from the nanopore and is put under compressive stress by the viscous forces it encounters. The leading tip of a buckled virus stalls and this reduces the total viscous drag force. We present a scaling theory that connects the solid mechanics to the nonlinear dynamics of polyelectrolytes translocating nanopores.

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...

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.

Application of process monitoring to anomaly detection in nuclear material processing systems via system-centric event interpretation of data from multiple sensors of varying reliability

International Nuclear Information System (INIS)

Garcia, Humberto E.; Simpson, Michael F.; Lin, Wen-Chiao; Carlson, Reed B.; Yoo, Tae-Sic

2017-01-01

Highlights: • Process monitoring can strengthen nuclear safeguards and material accountancy. • Assessment is conducted at a system-centric level to improve safeguards effectiveness. • Anomaly detection is improved by integrating process and operation relationships. • Decision making is benefited from using sensor and event sequence information. • Formal framework enables optimization of sensor and data processing resources. - Abstract: In this paper, we apply an advanced safeguards approach and associated methods for process monitoring to a hypothetical nuclear material processing system. The assessment regarding the state of the processing facility is conducted at a system-centric level formulated in a hybrid framework. This utilizes architecture for integrating both time- and event-driven data and analysis for decision making. While the time-driven layers of the proposed architecture encompass more traditional process monitoring methods based on time series data and analysis, the event-driven layers encompass operation monitoring methods based on discrete event data and analysis. By integrating process- and operation-related information and methodologies within a unified framework, the task of anomaly detection is greatly improved. This is because decision-making can benefit from not only known time-series relationships among measured signals but also from known event sequence relationships among generated events. This available knowledge at both time series and discrete event layers can then be effectively used to synthesize observation solutions that optimally balance sensor and data processing requirements. The application of the proposed approach is then implemented on an illustrative monitored system based on pyroprocessing and results are discussed.

An optical study of multiple NEIAL events driven by low energy electron precipitation

Directory of Open Access Journals (Sweden)

J. M. Sullivan

2008-08-01

Full Text Available Optical data are compared with EISCAT radar observations of multiple Naturally Enhanced Ion-Acoustic Line (NEIAL events in the dayside cusp. This study uses narrow field of view cameras to observe small-scale, short-lived auroral features. Using multiple-wavelength optical observations, a direct link between NEIAL occurrences and low energy (about 100 eV optical emissions is shown. This is consistent with the Langmuir wave decay interpretation of NEIALs being driven by streams of low-energy electrons. Modelling work connected with this study shows that, for the measured ionospheric conditions and precipitation characteristics, growth of unstable Langmuir (electron plasma waves can occur, which decay into ion-acoustic wave modes. The link with low energy optical emissions shown here, will enable future studies of the shape, extent, lifetime, grouping and motions of NEIALs.

Design, Dynamics, and Workspace of a Hybrid-Driven-Based Cable Parallel Manipulator

Directory of Open Access Journals (Sweden)

Bin Zi

2013-01-01

Full Text Available The design, dynamics, and workspace of a hybrid-driven-based cable parallel manipulator (HDCPM are presented. The HDCPM is able to perform high efficiency, heavy load, and high-performance motion due to the advantages of both the cable parallel manipulator and the hybrid-driven planar five-bar mechanism. The design is performed according to theories of mechanism structure synthesis for cable parallel manipulators. The dynamic formulation of the HDCPM is established on the basis of Newton-Euler method. The workspace of the manipulator is analyzed additionally. As an example, a completely restrained HDCPM with 3 degrees of freedom is studied in simulation in order to verify the validity of the proposed design, workspace, and dynamic analysis. The simulation results, compared with the theoretical analysis, and the case study previously performed show that the manipulator design is reasonable and the mathematical models are correct, which provides the theoretical basis for future physical prototype and control system design.

Floquet–Magnus theory and generic transient dynamics in periodically driven many-body quantum systems

International Nuclear Information System (INIS)

Kuwahara, Tomotaka; Mori, Takashi; Saito, Keiji

2016-01-01

This work explores a fundamental dynamical structure for a wide range of many-body quantum systems under periodic driving. Generically, in the thermodynamic limit, such systems are known to heat up to infinite temperature states in the long-time limit irrespective of dynamical details, which kills all the specific properties of the system. In the present study, instead of considering infinitely long-time scale, we aim to provide a general framework to understand the long but finite time behavior, namely the transient dynamics. In our analysis, we focus on the Floquet–Magnus (FM) expansion that gives a formal expression of the effective Hamiltonian on the system. Although in general the full series expansion is not convergent in the thermodynamics limit, we give a clear relationship between the FM expansion and the transient dynamics. More precisely, we rigorously show that a truncated version of the FM expansion accurately describes the exact dynamics for a certain time-scale. Our theory reveals an experimental time-scale for which non-trivial dynamical phenomena can be reliably observed. We discuss several dynamical phenomena, such as the effect of small integrability breaking, efficient numerical simulation of periodically driven systems, dynamical localization and thermalization. Especially on thermalization, we discuss a generic scenario on the prethermalization phenomenon in periodically driven systems. -- Highlights: •A general framework to describe transient dynamics for periodically driven systems. •The theory is applicable to generic quantum many-body systems including long-range interacting systems. •Physical meaning of the truncation of the Floquet–Magnus expansion is rigorously established. •New mechanism of the prethermalization is proposed. •Revealing an experimental time-scale for which non-trivial dynamical phenomena can be reliably observed.

Floquet–Magnus theory and generic transient dynamics in periodically driven many-body quantum systems

Energy Technology Data Exchange (ETDEWEB)

Kuwahara, Tomotaka, E-mail: tomotaka.phys@gmail.com [Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033 (Japan); WPI, Advanced Institute for Materials Research, Tohoku University, Sendai 980-8577 (Japan); Mori, Takashi [Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033 (Japan); Saito, Keiji [Department of Physics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522 (Japan)

2016-04-15

This work explores a fundamental dynamical structure for a wide range of many-body quantum systems under periodic driving. Generically, in the thermodynamic limit, such systems are known to heat up to infinite temperature states in the long-time limit irrespective of dynamical details, which kills all the specific properties of the system. In the present study, instead of considering infinitely long-time scale, we aim to provide a general framework to understand the long but finite time behavior, namely the transient dynamics. In our analysis, we focus on the Floquet–Magnus (FM) expansion that gives a formal expression of the effective Hamiltonian on the system. Although in general the full series expansion is not convergent in the thermodynamics limit, we give a clear relationship between the FM expansion and the transient dynamics. More precisely, we rigorously show that a truncated version of the FM expansion accurately describes the exact dynamics for a certain time-scale. Our theory reveals an experimental time-scale for which non-trivial dynamical phenomena can be reliably observed. We discuss several dynamical phenomena, such as the effect of small integrability breaking, efficient numerical simulation of periodically driven systems, dynamical localization and thermalization. Especially on thermalization, we discuss a generic scenario on the prethermalization phenomenon in periodically driven systems. -- Highlights: •A general framework to describe transient dynamics for periodically driven systems. •The theory is applicable to generic quantum many-body systems including long-range interacting systems. •Physical meaning of the truncation of the Floquet–Magnus expansion is rigorously established. •New mechanism of the prethermalization is proposed. •Revealing an experimental time-scale for which non-trivial dynamical phenomena can be reliably observed.

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

Configuring and Characterizing X-Rays for Laser-Driven Compression Experiments at the Dynamic Compression Sector

Science.gov (United States)

Li, Y.; Capatina, D.; D'Amico, K.; Eng, P.; Hawreliak, J.; Graber, T.; Rickerson, D.; Klug, J.; Rigg, P. A.; Gupta, Y. M.

2017-06-01

Coupling laser-driven compression experiments to the x-ray beam at the Dynamic Compression Sector (DCS) at the Advanced Photon Source (APS) of Argonne National Laboratory requires state-of-the-art x-ray focusing, pulse isolation, and diagnostics capabilities. The 100J UV pulsed laser system can be fired once every 20 minutes so precise alignment and focusing of the x-rays on each new sample must be fast and reproducible. Multiple Kirkpatrick-Baez (KB) mirrors are used to achieve a focal spot size as small as 50 μm at the target, while the strategic placement of scintillating screens, cameras, and detectors allows for fast diagnosis of the beam shape, intensity, and alignment of the sample to the x-ray beam. In addition, a series of x-ray choppers and shutters are used to ensure that the sample is exposed to only a single x-ray pulse ( 80ps) during the dynamic compression event and require highly precise synchronization. Details of the technical requirements, layout, and performance of these instruments will be presented. Work supported by DOE/NNSA.

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

Discovering Event Structure in Continuous Narrative Perception and Memory.

Science.gov (United States)

Baldassano, Christopher; Chen, Janice; Zadbood, Asieh; Pillow, Jonathan W; Hasson, Uri; Norman, Kenneth A

2017-08-02

During realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of cortical event dynamics, we investigate how cortical structures generate event representations during narrative perception and how these events are stored to and retrieved from memory. Our data-driven approach allows us to detect event boundaries as shifts between stable patterns of brain activity without relying on stimulus annotations and reveals a nested hierarchy from short events in sensory regions to long events in high-order areas (including angular gyrus and posterior medial cortex), which represent abstract, multimodal situation models. High-order event boundaries are coupled to increases in hippocampal activity, which predict pattern reinstatement during later free recall. These areas also show evidence of anticipatory reinstatement as subjects listen to a familiar narrative. Based on these results, we propose that brain activity is naturally structured into nested events, which form the basis of long-term memory representations. Copyright © 2017 Elsevier Inc. All rights reserved.

The potential of satellite data to study individual wildfire events

Science.gov (United States)

Benali, Akli; López-Saldana, Gerardo; Russo, Ana; Sá, Ana C. L.; Pinto, Renata M. S.; Nikos, Koutsias; Owen, Price; Pereira, Jose M. C.

2014-05-01

Large wildfires have important social, economic and environmental impacts. In order to minimize their impacts, understand their main drivers and study their dynamics, different approaches have been used. The reconstruction of individual wildfire events is usually done by collection of field data, interviews and by implementing fire spread simulations. All these methods have clear limitations in terms of spatial and temporal coverage, accuracy, subjectivity of the collected information and lack of objective independent validation information. In this sense, remote sensing is a promising tool with the potential to provide relevant information for stakeholders and the research community, by complementing or filling gaps in existing information and providing independent accurate quantitative information. In this work we show the potential of satellite data to provide relevant information regarding the dynamics of individual large wildfire events, filling an important gap in wildfire research. We show how MODIS active-fire data, acquired up to four times per day, and satellite-derived burnt perimeters can be combined to extract relevant information wildfire events by describing the methods involved and presenting results for four regions of the world: Portugal, Greece, SE Australia and California. The information that can be retrieved encompasses the start and end date of a wildfire event and its ignition area. We perform an evaluation of the information retrieved by comparing the satellite-derived parameters with national databases, highlighting the strengths and weaknesses of both and showing how the former can complement the latter leading to more complete and accurate datasets. We also show how the spatio-temporal distribution of wildfire spread dynamics can be reconstructed using satellite-derived active-fires and how relevant descriptors can be extracted. Applying graph theory to satellite active-fire data, we define the major fire spread paths that yield

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.

The Intense Arctic Cyclone of Early August 2012: A Dynamically Driven Cyclogenesis Event

Science.gov (United States)

Bosart, L. F.; Turchioe, A.; Adamchcik, E.

2013-12-01

A series of surface cyclones formed along an anomalously strong northeast-southwest oriented baroclinic zone over north-central Russia on 1-3 August 2012. These cyclones moved northeastward, intensified slowly, and crossed the coast of Russia by 4 August. The last cyclone in the series strengthened rapidly as it moved poleward over the Arctic Ocean on 5-6 August, achieved a minimum sea level pressure of life cycle of this Arctic Ocean cyclone from a multiscale perspective. Anticyclonic wave breaking in the upper troposphere across Russia in late July and very early August 2012 created an anomalously strong baroclinic zone across northern Asia between 60-80°N. During 1-5 August, negative 850 hPa temperature anomalies between -2° and -4°C were found poleward of 70-75°N between 90°E and the Dateline over the Arctic Ocean while positive 850 hPa temperature anomalies of 8-9°C were found over eastern Russia near 60°N. The associated anomalously strong 850 hPa meridional temperature gradient of ~10°C (2000 km)-1 helped to sustain an anomalously strong (20-30 m s-1) 250 hPa jet along the coast of northeastern Russia. A local wind speed maximum (~50 m s-1 ) embedded in this 250 hPa jet corridor contributed to the extreme intensity of the trailing (last) surface cyclone in the series. Although the dominant surface cyclone in the series of surface cyclones intensified most rapidly over the relatively ice free Arctic Ocean, the impact of surface heat and moisture fluxes appeared to be secondary to jet-driven dynamical processes in the deepening process. Anomalously high observed 1000-500 hPa thickness values between 564-570 dam, precipitable water values between 30-40 mm, and CAPE values between 500-1000 J kg-1 in the warm sector of the developing cyclone over north-central Russia were indicative of the enhanced baroclinicity and instability in the cyclone warm sector and the ability of lower tropospheric warm-air advection to sustain deep ascent in the intensifying

Field- and current-driven domain wall dynamics: An experimental picture

International Nuclear Information System (INIS)

Beach, G.S.D.; Knutson, C.; Tsoi, M.; Erskine, J.L.

2007-01-01

Field- and current-driven domain wall velocities are measured and discussed in terms of existing spin-torque models. A reversal in the roles of adiabatic and non-adiabatic spin-torque is shown to arise in those models below and above Walker breakdown. The measured dependence of velocity on current is the same in both regimes, indicating both spin-torque components have similar magnitude. However, the models on which these conclusions are based have serious quantitative shortcomings in describing the observed field-driven wall dynamics, for which they were originally developed. Hence, the applicability of simple one-dimensional models to most experimental conditions may be limited

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.

Long-Range Coulomb Effect in Intense Laser-Driven Photoelectron Dynamics.

Science.gov (United States)

Quan, Wei; Hao, XiaoLei; Chen, YongJu; Yu, ShaoGang; Xu, SongPo; Wang, YanLan; Sun, RenPing; Lai, XuanYang; Wu, ChengYin; Gong, QiHuang; He, XianTu; Liu, XiaoJun; Chen, Jing

2016-06-03

In strong field atomic physics community, long-range Coulomb interaction has for a long time been overlooked and its significant role in intense laser-driven photoelectron dynamics eluded experimental observations. Here we report an experimental investigation of the effect of long-range Coulomb potential on the dynamics of near-zero-momentum photoelectrons produced in photo-ionization process of noble gas atoms in intense midinfrared laser pulses. By exploring the dependence of photoelectron distributions near zero momentum on laser intensity and wavelength, we unambiguously demonstrate that the long-range tail of the Coulomb potential (i.e., up to several hundreds atomic units) plays an important role in determining the photoelectron dynamics after the pulse ends.

Two-rate periodic protocol with dynamics driven through many cycles

Science.gov (United States)

Kar, Satyaki

2017-02-01

We study the long time dynamics in closed quantum systems periodically driven via time dependent parameters with two frequencies ω1 and ω2=r ω1 . Tuning of the ratio r there can unleash plenty of dynamical phenomena to occur. Our study includes integrable models like Ising and X Y models in d =1 and the Kitaev model in d =1 and 2 and can also be extended to Dirac fermions in graphene. We witness the wave-function overlap or dynamic freezing that occurs within some small/ intermediate frequency regimes in the (ω1,r ) plane (with r ≠0 ) when the ground state is evolved through a single cycle of driving. However, evolved states soon become steady with long driving, and the freezing scenario gets rarer. We extend the formalism of adiabatic-impulse approximation for many cycle driving within our two-rate protocol and show the near-exact comparisons at small frequencies. An extension of the rotating wave approximation is also developed to gather an analytical framework of the dynamics at high frequencies. Finally we compute the entanglement entropy in the stroboscopically evolved states within the gapped phases of the system and observe how it gets tuned with the ratio r in our protocol. The minimally entangled states are found to fall within the regime of dynamical freezing. In general, the results indicate that the entanglement entropy in our driven short-ranged integrable systems follow a genuine nonarea law of scaling and show a convergence (with a r dependent pace) towards volume scaling behavior as the driving is continued for a long time.

Numerical simulation of nonlinear dynamical systems driven by commutative noise

International Nuclear Information System (INIS)

Carbonell, F.; Biscay, R.J.; Jimenez, J.C.; Cruz, H. de la

2007-01-01

The local linearization (LL) approach has become an effective technique for the numerical integration of ordinary, random and stochastic differential equations. One of the reasons for this success is that the LL method achieves a convenient trade-off between numerical stability and computational cost. Besides, the LL method reproduces well the dynamics of nonlinear equations for which other classical methods fail. However, in the stochastic case, most of the reported works has been focused in Stochastic Differential Equations (SDE) driven by additive noise. This limits the applicability of the LL method since there is a number of interesting dynamics observed in equations with multiplicative noise. On the other hand, recent results show that commutative noise SDEs can be transformed into a random differential equation (RDE) by means of a random diffeomorfism (conjugacy). This paper takes advantages of such conjugacy property and the LL approach for defining a LL scheme for SDEs driven by commutative noise. The performance of the proposed method is illustrated by means of numerical simulations

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.

Phase dynamics of a Josephson junction ladder driven by modulated currents

International Nuclear Information System (INIS)

Kawaguchi, T.

2011-01-01

Phase dynamics of disordered Josephson junction ladders (JJLs) driven by external currents which are spatially and temporally modulated is studied using a numerical simulation based on a random field XY model. This model is considered theoretically as an effective model of JJLs with structural disorder in a magnetic field. The spatiotemporal modulation of external currents causes peculiar dynamical effects of phases in the system under certain conditions, such as the directed motion of phases and the mode-locking in the absence of dc currents. We clarify the details of effects of the spatiotemporal modulation on the phase dynamics.

Interplay of interfacial noise and curvature-driven dynamics in two dimensions

Science.gov (United States)

Roy, Parna; Sen, Parongama

2017-02-01

We explore the effect of interplay of interfacial noise and curvature-driven dynamics in a binary spin system. An appropriate model is the generalized two-dimensional voter model proposed earlier [M. J. de Oliveira, J. F. F. Mendes, and M. A. Santos, J. Phys. A: Math. Gen. 26, 2317 (1993), 10.1088/0305-4470/26/10/006], where the flipping probability of a spin depends on the state of its neighbors and is given in terms of two parameters, x and y . x =0.5 andy =1 correspond to the conventional voter model which is purely interfacial noise driven, while x =1 and y =1 correspond to the Ising model, where coarsening is fully curvature driven. The coarsening phenomena for 0.5 x y =1 is studied in detail. The dynamical behavior of the relevant quantities show characteristic differences from both x =0.5 and 1. The most remarkable result is the existence of two time scales for x ≥xc where xc≈0.7 . On the other hand, we have studied the exit probability which shows Ising-like behavior with a universal exponent for any value of x >0.5 ; the effect of x appears in altering the value of the parameter occurring in the scaling function only.

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

Elephant movement closely tracks precipitation-driven vegetation dynamics in a Kenyan forest-savanna landscape.

Science.gov (United States)

Bohrer, Gil; Beck, Pieter Sa; Ngene, Shadrack M; Skidmore, Andrew K; Douglas-Hamilton, Ian

2014-01-01

This study investigates the ranging behavior of elephants in relation to precipitation-driven dynamics of vegetation. Movement data were acquired for five bachelors and five female family herds during three years in the Marsabit protected area in Kenya and changes in vegetation were mapped using MODIS normalized difference vegetation index time series (NDVI). In the study area, elevations of 650 to 1100 m.a.s.l experience two growth periods per year, while above 1100 m.a.s.l. growth periods last a year or longer. We find that elephants respond quickly to changes in forage and water availability, making migrations in response to both large and small rainfall events. The elevational migration of individual elephants closely matched the patterns of greening and senescing of vegetation in their home range. Elephants occupied lower elevations when vegetation activity was high, whereas they retreated to the evergreen forest at higher elevations while vegetation senesced. Elephant home ranges decreased in size, and overlapped less with increasing elevation. A recent hypothesis that ungulate migrations in savannas result from countervailing seasonally driven rainfall and fertility gradients is demonstrated, and extended to shorter-distance migrations. In other words, the trade-off between the poor forage quality and accessibility in the forest with its year-round water sources on the one hand and the higher quality forage in the low-elevation scrubland with its seasonal availability of water on the other hand, drives the relatively short migrations (the two main corridors are 20 and 90 km) of the elephants. In addition, increased intra-specific competition appears to influence the animals' habitat use during the dry season indicating that the human encroachment on the forest is affecting the elephant population.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

Science.gov (United States)

2011-01-01

Background Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways. Results This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements. Conclusions We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting

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.

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.

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.

Automated Feature and Event Detection with SDO AIA and HMI Data

Science.gov (United States)

Davey, Alisdair; Martens, P. C. H.; Attrill, G. D. R.; Engell, A.; Farid, S.; Grigis, P. C.; Kasper, J.; Korreck, K.; Saar, S. H.; Su, Y.; Testa, P.; Wills-Davey, M.; Savcheva, A.; Bernasconi, P. N.; Raouafi, N.-E.; Delouille, V. A.; Hochedez, J. F..; Cirtain, J. W.; Deforest, C. E.; Angryk, R. A.; de Moortel, I.; Wiegelmann, T.; Georgouli, M. K.; McAteer, R. T. J.; Hurlburt, N.; Timmons, R.

The Solar Dynamics Observatory (SDO) represents a new frontier in quantity and quality of solar data. At about 1.5 TB/day, the data will not be easily digestible by solar physicists using the same methods that have been employed for images from previous missions. In order for solar scientists to use the SDO data effectively they need meta-data that will allow them to identify and retrieve data sets that address their particular science questions. We are building a comprehensive computer vision pipeline for SDO, abstracting complete metadata on many of the features and events detectable on the Sun without human intervention. Our project unites more than a dozen individual, existing codes into a systematic tool that can be used by the entire solar community. The feature finding codes will run as part of the SDO Event Detection System (EDS) at the Joint Science Operations Center (JSOC; joint between Stanford and LMSAL). The metadata produced will be stored in the Heliophysics Event Knowledgebase (HEK), which will be accessible on-line for the rest of the world directly or via the Virtual Solar Observatory (VSO) . Solar scientists will be able to use the HEK to select event and feature data to download for science studies.

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.

Numerical investigation on target implosions driven by radiation ablation and shock compression in dynamic hohlraums

Energy Technology Data Exchange (ETDEWEB)

Xiao, Delong; Sun, Shunkai; Zhao, Yingkui; Ding, Ning; Wu, Jiming; Dai, Zihuan; Yin, Li; Zhang, Yang; Xue, Chuang [Institute of Applied Physics and Computational Mathematics, Beijing 100088 (China)

2015-05-15

In a dynamic hohlraum driven inertial confinement fusion (ICF) configuration, the target may experience two different kinds of implosions. One is driven by hohlraum radiation ablation, which is approximately symmetric at the equator and poles. The second is caused by the radiating shock produced in Z-pinch dynamic hohlraums, only taking place at the equator. To gain a symmetrical target implosion driven by radiation ablation and avoid asymmetric shock compression is a crucial issue in driving ICF using dynamic hohlraums. It is known that when the target is heated by hohlraum radiation, the ablated plasma will expand outward. The pressure in the shocked converter plasma qualitatively varies linearly with the material temperature. However, the ablation pressure in the ablated plasma varies with 3.5 power of the hohlraum radiation temperature. Therefore, as the hohlraum temperature increases, the ablation pressure will eventually exceed the shock pressure, and the expansion of the ablated plasma will obviously weaken the shock propagation and decrease its velocity after propagating into the ablator plasma. Consequently, longer time duration is provided for the symmetrical target implosion driven by radiation ablation. In this paper these processes are numerically investigated by changing drive currents or varying load parameters. The simulation results show that a critical hohlraum radiation temperature is needed to provide a high enough ablation pressure to decelerate the shock, thus providing long enough time duration for the symmetric fuel compression driven by radiation ablation.

Floquet-Magnus theory and generic transient dynamics in periodically driven many-body quantum systems

Science.gov (United States)

Kuwahara, Tomotaka; Mori, Takashi; Saito, Keiji

2016-04-01

This work explores a fundamental dynamical structure for a wide range of many-body quantum systems under periodic driving. Generically, in the thermodynamic limit, such systems are known to heat up to infinite temperature states in the long-time limit irrespective of dynamical details, which kills all the specific properties of the system. In the present study, instead of considering infinitely long-time scale, we aim to provide a general framework to understand the long but finite time behavior, namely the transient dynamics. In our analysis, we focus on the Floquet-Magnus (FM) expansion that gives a formal expression of the effective Hamiltonian on the system. Although in general the full series expansion is not convergent in the thermodynamics limit, we give a clear relationship between the FM expansion and the transient dynamics. More precisely, we rigorously show that a truncated version of the FM expansion accurately describes the exact dynamics for a certain time-scale. Our theory reveals an experimental time-scale for which non-trivial dynamical phenomena can be reliably observed. We discuss several dynamical phenomena, such as the effect of small integrability breaking, efficient numerical simulation of periodically driven systems, dynamical localization and thermalization. Especially on thermalization, we discuss a generic scenario on the prethermalization phenomenon in periodically driven systems.

THE USE OF EVENT DATA RECORDER (EDR – BLACK BOX

Directory of Open Access Journals (Sweden)

Gabriel Nowacki

2014-03-01

Full Text Available The paper refers to the registration of road events by a modern device called EDR – black box for all types of the motor vehicles. The device records data concerning vehicle’s technical condition, the way it was driven and RTS. The recorder may be used in private and commercial cars, taxies, buses and trucks. The recorder may serve the purpose of a neutral witness for the police, courts and insurance firms, for which it will facilitate making the reconstruction of the road accidents events and will provide a proof for those who caused them. The device will bring efficient driving, which will significantly contribute to decreasing the number of road accidents and limiting the environmental pollution. In the end in the last year German parliament backed a proposal to the European Commission to put black boxes, which gather information from vehicles involved in accidents, in all the new cars from 2015 on.

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

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.

Data-Driven Innovation through Open Government Data

DEFF Research Database (Denmark)

Jetzek, Thorhildur; Avital, Michel; Bjørn-Andersen, Niels

2014-01-01

The exponentially growing production of data and the social trend towards openness and sharing are power-ful forces that are changing the global economy and society. Governments around the world have become active participants in this evolution, opening up their data for access and reuse by public...... and private agents alike. The phenomenon of Open Government Data has spread around the world in the last four years, driven by the widely held belief that use of Open Government Data has the ability to generate both economic and social value. However, a cursory review of the popular press, as well...... as an investigation of academic research and empirical data, reveals the need to further understand the relationship between Open Government Data and value. In this paper, we focus on how use of Open Government Data can bring about new innovative solutions that can generate social and economic value. We apply...

Hydraulically driven control rod concept for integral reactors: fluid dynamic simulation and preliminary test

International Nuclear Information System (INIS)

Ricotti, M.E.; Cammi, A.; Lombardi, C.; Passoni, M.; Rizzo, C.; Carelli, M.; Colombo, E.

2003-01-01

The paper deals with the preliminary study of the Hydraulically Driven Control Rod concept, tailored for PWR control rods (spider type) with hydraulic drive mechanism completely immersed in the primary water. A specific solution suitable for advanced versions of the IRIS integral reactor is under investigation. The configuration of the Hydraulic Control Rod device, made up by an external movable piston and an internal fixed cylinder, is described. After a brief description of the whole control system, particular attention is devoted to the Control Rod characterization via Computational Fluid Dynamics (CFD) analysis. The investigation of the system behavior, including dynamic equilibrium and stability properties, has been carried out. Finally, preliminary tests were performed in a low pressure, low temperature, reduced length experimental facility. The results are compared with the dynamic control model and CFD simulation model, showing good agreement between simulations and experimental data. During these preliminary tests, the control system performs correctly, allowing stable dynamic equilibrium positions for the Control Rod and stable behavior during withdrawal and insertion steps. (author)

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.

Dynamic Axle Load of an Automotive Vehicle When Driven on a Mobile Measurement Platform

OpenAIRE

Jagiełowicz-Ryznar C.

2014-01-01

An analysis of the dynamic axle load of an automotive vehicle (AV) when it is driven on a mobile measurement platform is presented in this paper. During the ride, the time characteristic of the dynamic force N(t), acting on the axle, was recorded. The effect of the vehicle axle mass on the maximum dynamic force value and the dynamic coefficient were studied. On this basis it was attempted to calculate the total vehicle’s weight. Conclusions concerning the dynamic loads of the vehicle axles in...

Detection of unusual events and trends in complex non-stationary data streams

International Nuclear Information System (INIS)

Charlton-Perez, C.; Perez, R.B.; Protopopescu, V.; Worley, B.A.

2011-01-01

The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for diverse applications, ranging from power plant operation to homeland security. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden events inside intermittent signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method.

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.

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

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

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

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.

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

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?

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....

Baseline Preferences for Daily, Event-Driven, or Periodic HIV Pre-Exposure Prophylaxis among Gay and Bisexual Men in the PRELUDE Demonstration Project

Directory of Open Access Journals (Sweden)

Stefanie J. Vaccher

2017-12-01

Full Text Available IntroductionThe effectiveness of daily pre-exposure prophylaxis (PrEP is well established. However, there has been increasing interest in non-daily dosing schedules among gay and bisexual men (GBM. This paper explores preferences for PrEP dosing schedules among GBM at baseline in the PRELUDE demonstration project.Materials and methodsIndividuals at high-risk of HIV were enrolled in a free PrEP demonstration project in New South Wales, Australia, between November 2014 and April 2016. At baseline, they completed an online survey containing detailed behavioural, demographic, and attitudinal questions, including their ideal way to take PrEP: daily (one pill taken every day, event-driven (pills taken only around specific risk events, or periodic (daily dosing during periods of increased risk.ResultsOverall, 315 GBM (98% of study sample provided a preferred PrEP dosing schedule at baseline. One-third of GBM expressed a preference for non-daily PrEP dosing: 20% for event-driven PrEP, and 14% for periodic PrEP. Individuals with a trade/vocational qualification were more likely to prefer periodic to daily PrEP [adjusted odds ratio (aOR = 4.58, 95% confidence intervals (95% CI: (1.68, 12.49], compared to individuals whose highest level of education was high school. Having an HIV-positive main regular partner was associated with strong preference for daily, compared to event-driven PrEP [aOR = 0.20, 95% CI: (0.04, 0.87]. Participants who rated themselves better at taking medications were more likely to prefer daily over periodic PrEP [aOR = 0.39, 95% CI: (0.20, 0.76].DiscussionIndividuals’ preferences for PrEP schedules are associated with demographic and behavioural factors that may impact on their ability to access health services and information about PrEP and patterns of HIV risk. At the time of data collection, there were limited data available about the efficacy of non-daily PrEP schedules, and clinicians only recommended daily PrEP to

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.

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…

Water regime history drives responses of soil Namib Desert microbial communities to wetting events

Science.gov (United States)

Frossard, Aline; Ramond, Jean-Baptiste; Seely, Mary; Cowan, Don A.

2015-07-01

Despite the dominance of microorganisms in arid soils, the structures and functional dynamics of microbial communities in hot deserts remain largely unresolved. The effects of wetting event frequency and intensity on Namib Desert microbial communities from two soils with different water-regime histories were tested over 36 days. A total of 168 soil microcosms received wetting events mimicking fog, light rain and heavy rainfall, with a parallel “dry condition” control. T-RFLP data showed that the different wetting events affected desert microbial community structures, but these effects were attenuated by the effects related to the long-term adaptation of both fungal and bacterial communities to soil origins (i.e. soil water regime histories). The intensity of the water pulses (i.e. the amount of water added) rather than the frequency of wetting events had greatest effect in shaping bacterial and fungal community structures. In contrast to microbial diversity, microbial activities (enzyme activities) showed very little response to the wetting events and were mainly driven by soil origin. This experiment clearly demonstrates the complexity of microbial community responses to wetting events in hyperarid hot desert soil ecosystems and underlines the dynamism of their indigenous microbial communities.

Anisotropic relaxation dynamics in a dipolar Fermi gas driven out of equilibrium

DEFF Research Database (Denmark)

Aikawa, K.; Frisch, A.; Mark, M.

2014-01-01

We report on the observation of a large anisotropy in the rethermalization dynamics of an ultracold dipolar Fermi gas driven out of equilibrium. Our system consists of an ultracold sample of strongly magnetic $^{167}$Er fermions, spin-polarized in the lowest Zeeman sublevel. In this system, elastic...

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.

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...

Dynamics of Laser-Driven Shock Waves in Solid Targets

Science.gov (United States)

Aglitskiy, Y.; Karasik, M.; Velikovich, A. L.; Serlin, V.; Weaver, J.; Schmitt, A. J.; Obenschain, S. P.; Grun, J.; Metzler, N.; Zalesak, S. T.; Gardner, J. H.; Oh, J.; Harding, E. C.

2009-11-01

Accurate shock timing is a key issue of both indirect- and direct-drive laser fusions. The experiments on the Nike laser at NRL presented here were made possible by improvements in the imaging capability of our monochromatic x-ray diagnostics based on Bragg reflection from spherically curved crystals. Side-on imaging implemented on Nike makes it possible to observe dynamics of the shock wave and ablation front in laser-driven solid targets. We can choose to observe a sequence of 2D images or a continuous time evolution of an image resolved in one spatial dimension. A sequence of 300 ps snapshots taken using vanadium backlighter at 5.2 keV reveals propagation of a shock wave in a solid plastic target. The shape of the shock wave reflects the intensity distribution in the Nike beam. The streak records with continuous time resolution show the x-t trajectory of a laser-driven shock wave in a 10% solid density DVB foam.

Complex Pattern Formation from Current-Driven Dynamics of Single-Layer Epitaxial Islands on Crystalline Conducting Substrates

Science.gov (United States)

Kumar, Ashish; Dasgupta, Dwaipayan; Maroudas, Dimitrios

We report a systematic study of complex pattern formation resulting from the driven dynamics of single-layer homoepitaxial islands on face-centered cubic (FCC) crystalline conducting substrate surfaces under the action of an externally applied electric field. The analysis is based on an experimentally validated nonlinear model of mass transport via island edge atomic diffusion, which also accounts for edge diffusional anisotropy. We analyze the morphological stability and simulate the field-driven evolution of rounded islands for an electric field oriented along the fast diffusion direction. For larger than critical island sizes on {110} and {100} FCC substrates, we show that multiple necking instabilities generate complex island patterns, including void-containing islands, mediated by sequences of breakup and coalescence events and distributed symmetrically with respect to the electric field direction. We analyze the dependence of the formed patterns on the original island size and on the duration of application of the external field. Starting from a single large rounded island, we characterize the evolution of the number of daughter islands and their average size and uniformity. The analysis reveals that the pattern formation kinetics follows a universal scaling relation. Division of Materials Sciences & Engineering, Office of Basic Energy Sciences, U.S. Department of Energy (Award No.: DE-FG02-07ER46407).

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.

A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation

Science.gov (United States)

Camuñas-Mesa, Luis A.; Domínguez-Cordero, Yaisel L.; Linares-Barranco, Alejandro; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2018-01-01

Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network. PMID:29515349

A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation

Directory of Open Access Journals (Sweden)

Luis A. Camuñas-Mesa

2018-02-01

Full Text Available Convolutional Neural Networks (ConvNets are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network.

A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation.

Science.gov (United States)

Camuñas-Mesa, Luis A; Domínguez-Cordero, Yaisel L; Linares-Barranco, Alejandro; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2018-01-01

Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network.

Analysis of Dynamic Behavior of Multiple-Stage Planetary Gear Train Used in Wind Driven Generator

Directory of Open Access Journals (Sweden)

Jungang Wang

2014-01-01

Full Text Available A dynamic model of multiple-stage planetary gear train composed of a two-stage planetary gear train and a one-stage parallel axis gear is proposed to be used in wind driven generator to analyze the influence of revolution speed and mesh error on dynamic load sharing characteristic based on the lumped parameter theory. Dynamic equation of the model is solved using numerical method to analyze the uniform load distribution of the system. It is shown that the load sharing property of the system is significantly affected by mesh error and rotational speed; load sharing coefficient and change rate of internal and external meshing of the system are of obvious difference from each other. The study provides useful theoretical guideline for the design of the multiple-stage planetary gear train of wind driven generator.

Control dynamics of interaction quenched ultracold bosons in periodically driven lattices

Science.gov (United States)

Mistakidis, Simeon; Schmelcher, Peter; Group of Fundamental Processes in Quantum Physics Team

2016-05-01

The out-of-equilibrium dynamics of ultracold bosons following an interaction quench upon a periodically driven optical lattice is investigated. It is shown that an interaction quench triggers the inter-well tunneling dynamics, while for the intra-well dynamics breathing and cradle-like processes can be generated. In particular, the occurrence of a resonance between the cradle and tunneling modes is revealed. On the other hand, the employed periodic driving enforces the bosons in the mirror wells to oscillate out-of-phase and to exhibit a dipole mode, while in the central well the cloud experiences a breathing mode. The dynamical behaviour of the system is investigated with respect to the driving frequency revealing a resonant behaviour of the intra-well dynamics. To drive the system in a highly non-equilibrium state an interaction quench upon the driving is performed giving rise to admixtures of excitations in the outer wells, an enhanced breathing in the center and an amplification of the tunneling dynamics. As a result of the quench the system experiences multiple resonances between the inter- and intra-well dynamics at different quench amplitudes. Deutsche Forschungsgemeinschaft, SFB 925 ``Light induced dynamics and control of correlated quantum systems''.

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.

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...

Event-driven control of a speed varying digital displacement machine

DEFF Research Database (Denmark)

Pedersen, Niels Henrik; Johansen, Per; Andersen, Torben O.

2017-01-01

. The controller synthesis is carried out as a discrete optimal deterministic problem with full state feedback. Based on a linear analysis of the feedback control system, stability is proven in a pre-specified operation region. Simulation of a non-linear evaluation model with the controller implemented shows great...... be treated as a Discrete Linear Time Invariant control problem with synchronous sampling rate. To make synchronous linear control theory applicable for a variable speed digital displacement machine, a method based on event-driven control is presented. Using this method, the time domain differential equations...... are converted into the spatial (position) domain to obtain a constant sampling rate and thus allowing for use of classical control theory. The method is applied to a down scaled digital fluid power motor, where the motor speed is controlled at varying references under varying pressure and load torque conditions...

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…

Using the Dynamic Model to develop an evidence-based and theory-driven approach to school improvement

NARCIS (Netherlands)

Creemers, B.P.M.; Kyriakides, L.

2010-01-01

This paper refers to a dynamic perspective of educational effectiveness and improvement stressing the importance of using an evidence-based and theory-driven approach. Specifically, an approach to school improvement based on the dynamic model of educational effectiveness is offered. The recommended

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.

Event-by-Event Elliptic Flow Fluctuations from PHOBOS

Science.gov (United States)

Wosiek, B.; Alver, B.; Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Busza, W.; Carroll, A.; Chai, Z.; Chetluru, V.; Decowski, M. P.; García, E.; Gburek, T.; George, N.; Gulbrandsen, K.; Halliwell, C.; Hamblen, J.; Harnarine, I.; Hauer, M.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Khan, N.; Kulinich, P.; Kuo, C. M.; Li, W.; Lin, W. T.; Loizides, C.; Manly, S.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Reed, C.; Richardson, E.; Roland, C.; Roland, G.; Sagerer, J.; Seals, H.; Sedykh, I.; Smith, C. E.; Stankiewicz, M. A.; Steinberg, P.; Stephans, G. S. F.; Sukhanov, A.; Szostak, A.; Tonjes, M. B.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Vaurynovich, S. S.; Verdier, R.; Veres, G. I.; Walters, P.; Wenger, E.; Willhelm, D.; Wolfs, F. L. H.; Woźniak, K.; Wyngaardt, S.; Wysłouch, B.

2009-04-01

Recently PHOBOS has focused on the study of fluctuations and correlations in particle production in heavy-ion collisions at the highest energies delivered by the Relativistic Heavy Ion Collider (RHIC). In this report, we present results on event-by-event elliptic flow fluctuations in (Au+Au) collisions at sqrt {sNN}=200 GeV. A data-driven method was used to estimate the dominant contribution from non-flow correlations. Over the broad range of collision centralities, the observed large elliptic flow fluctuations are in agreement with the fluctuations in the initial source eccentricity.

Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams

International Nuclear Information System (INIS)

Perez, Rafael B.; Protopopescu, Vladimir A.; Worley, Brian Addison; Perez, Cristina

2006-01-01

The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for a host of different applications, ranging from nuclear power plant and electric grid operation to internet traffic and implementation of non-proliferation protocols. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden intermittent events inside non-stationary signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method

Collaborative Event-Driven Coverage and Rate Allocation for Event Miss-Ratio Assurances in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Ozgur Sanli H

2010-01-01

Full Text Available Wireless sensor networks are often required to provide event miss-ratio assurance for a given event type. To meet such assurances along with minimum energy consumption, this paper shows how a node's activation and rate assignment is dependent on its distance to event sources, and proposes a practical coverage and rate allocation (CORA protocol to exploit this dependency in realistic environments. Both uniform event distribution and nonuniform event distribution are considered and the notion of ideal correlation distance around a clusterhead is introduced for on-duty node selection. In correlation distance guided CORA, rate assignment assists coverage scheduling by determining which nodes should be activated for minimizing data redundancy in transmission. Coverage scheduling assists rate assignment by controlling the amount of overlap among sensing regions of neighboring nodes, thereby providing sufficient data correlation for rate assignment. Extensive simulation results show that CORA meets the required event miss-ratios in realistic environments. CORA's joint coverage scheduling and rate allocation reduce the total energy expenditure by 85%, average battery energy consumption by 25%, and the overhead of source coding up to 90% as compared to existing rate allocation techniques.

Introduction to State Estimation of High-Rate System Dynamics.

Science.gov (United States)

Hong, Jonathan; Laflamme, Simon; Dodson, Jacob; Joyce, Bryan

2018-01-13

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer's convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model.

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

Dynamics of bloggers’ communities: Bipartite networks from empirical data and agent-based modeling

Science.gov (United States)

Mitrović, Marija; Tadić, Bosiljka

2012-11-01

We present an analysis of the empirical data and the agent-based modeling of the emotional behavior of users on the Web portals where the user interaction is mediated by posted comments, like Blogs and Diggs. We consider the dataset of discussion-driven popular Diggs, in which all comments are screened by machine-learning emotion detection in the text, to determine positive and negative valence (attractiveness and aversiveness) of each comment. By mapping the data onto a suitable bipartite network, we perform an analysis of the network topology and the related time-series of the emotional comments. The agent-based model is then introduced to simulate the dynamics and to capture the emergence of the emotional behaviors and communities. The agents are linked to posts on a bipartite network, whose structure evolves through their actions on the posts. The emotional states (arousal and valence) of each agent fluctuate in time, subject to the current contents of the posts to which the agent is exposed. By an agent’s action on a post its current emotions are transferred to the post. The model rules and the key parameters are inferred from the considered empirical data to ensure their realistic values and mutual consistency. The model assumes that the emotional arousal over posts drives the agent’s action. The simulations are preformed for the case of constant flux of agents and the results are analyzed in full analogy with the empirical data. The main conclusions are that the emotion-driven dynamics leads to long-range temporal correlations and emergent networks with community structure, that are comparable with the ones in the empirical system of popular posts. In view of pure emotion-driven agents actions, this type of comparisons provide a quantitative measure for the role of emotions in the dynamics on real blogs. Furthermore, the model reveals the underlying mechanisms which relate the post popularity with the emotion dynamics and the prevalence of negative

Stimulus-Driven Attentional Capture by a Static Discontinuity between Perceptual Groups

Science.gov (United States)

Burnham, Bryan R.; Neely, James H.; Naginsky, Yelena; Thomas, Matthew

2010-01-01

After C. L. Folk, R. W. Remington, and J. C. Johnston (1992) proposed their contingent-orienting hypothesis, there has been an ongoing debate over whether purely stimulus-driven attentional capture can occur for visual events that are salient by virtue of a distinctive static property (as opposed to a dynamic property such as abrupt onset). The…

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.

Automatic Multi-sensor Data Quality Checking and Event Detection for Environmental Sensing

Science.gov (United States)

LIU, Q.; Zhang, Y.; Zhao, Y.; Gao, D.; Gallaher, D. W.; Lv, Q.; Shang, L.

2017-12-01

With the advances in sensing technologies, large-scale environmental sensing infrastructures are pervasively deployed to continuously collect data for various research and application fields, such as air quality study and weather condition monitoring. In such infrastructures, many sensor nodes are distributed in a specific area and each individual sensor node is capable of measuring several parameters (e.g., humidity, temperature, and pressure), providing massive data for natural event detection and analysis. However, due to the dynamics of the ambient environment, sensor data can be contaminated by errors or noise. Thus, data quality is still a primary concern for scientists before drawing any reliable scientific conclusions. To help researchers identify potential data quality issues and detect meaningful natural events, this work proposes a novel algorithm to automatically identify and rank anomalous time windows from multiple sensor data streams. More specifically, (1) the algorithm adaptively learns the characteristics of normal evolving time series and (2) models the spatial-temporal relationship among multiple sensor nodes to infer the anomaly likelihood of a time series window for a particular parameter in a sensor node. Case studies using different data sets are presented and the experimental results demonstrate that the proposed algorithm can effectively identify anomalous time windows, which may resulted from data quality issues and natural events.

THE MORPHOLOGY AND DYNAMICS OF JET-DRIVEN SUPERNOVA REMNANTS: THE CASE OF W49B

Energy Technology Data Exchange (ETDEWEB)

González-Casanova, Diego F.; De Colle, Fabio [Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, A. P. 70-543, 04510 D. F. (Mexico); Ramirez-Ruiz, Enrico [Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064 (United States); Lopez, Laura A. [MIT-Kavli Institute for Astrophysics and Space Research, 77 Massachusetts Avenue, 37-664H, Cambridge, MA 02139 (United States)

2014-02-01

The circumstellar medium (CSM) of a massive star is modified by its winds before a supernova (SN) explosion occurs, and thus the evolution of the resulting supernova remnant (SNR) is influenced by both the geometry of the explosion as well as the complex structure of the CSM. Motivated by recent work suggesting the SNR W49B was a jet-driven SN expanding in a complex CSM, we explore how the dynamics and the metal distributions in a jet-driven explosion are modified by the interaction with the surrounding environment. In particular, we perform hydrodynamical calculations to study the dynamics and explosive nucleosynthesis of a jet-driven SN triggered by the collapse of a 25 M {sub ☉} Wolf-Rayet star and its subsequent interaction with the CSM up to several hundred years following the explosion. We find that although the CSM has small-scale effects on the structure of the SNR, the overall morphology and abundance patterns are reflective of the initial asymmetry of the SN explosion. Thus, we predict that jet-driven SNRs, such as W49B, should be identifiable based on morphology and abundance patterns at ages up to several hundred years, even if they expand into a complex CSM environment.

Big Data Tools as Applied to ATLAS Event Data

Science.gov (United States)

Vukotic, I.; Gardner, R. W.; Bryant, L. A.

2017-10-01

Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Logfiles, database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and associated analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data. Such modes would simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of machine learning environments and tools like Spark, Jupyter, R, SciPy, Caffe, TensorFlow, etc. Machine learning challenges such as the Higgs Boson Machine Learning Challenge, the Tracking challenge, Event viewers (VP1, ATLANTIS, ATLASrift), and still to be developed educational and outreach tools would be able to access the data through a simple REST API. In this preliminary investigation we focus on derived xAOD data sets. These are much smaller than the primary xAODs having containers, variables, and events of interest to a particular analysis. Being encouraged with the performance of Elasticsearch for the ADC analytics platform, we developed an algorithm for indexing derived xAOD event data. We have made an appropriate document mapping and have imported a full set of standard model W/Z datasets. We compare the disk space efficiency of this approach to that of standard ROOT files, the performance in simple cut flow type of data analysis, and will present preliminary results on its scaling

Information-theoretic model selection for optimal prediction of stochastic dynamical systems from data

Science.gov (United States)

Darmon, David

2018-03-01

In the absence of mechanistic or phenomenological models of real-world systems, data-driven models become necessary. The discovery of various embedding theorems in the 1980s and 1990s motivated a powerful set of tools for analyzing deterministic dynamical systems via delay-coordinate embeddings of observations of their component states. However, in many branches of science, the condition of operational determinism is not satisfied, and stochastic models must be brought to bear. For such stochastic models, the tool set developed for delay-coordinate embedding is no longer appropriate, and a new toolkit must be developed. We present an information-theoretic criterion, the negative log-predictive likelihood, for selecting the embedding dimension for a predictively optimal data-driven model of a stochastic dynamical system. We develop a nonparametric estimator for the negative log-predictive likelihood and compare its performance to a recently proposed criterion based on active information storage. Finally, we show how the output of the model selection procedure can be used to compare candidate predictors for a stochastic system to an information-theoretic lower bound.

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.

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.

A Model-Driven Methodology for Big Data Analytics-as-a-Service

OpenAIRE

Damiani, Ernesto; Ardagna, Claudio Agostino; Ceravolo, Paolo; Bellandi, Valerio; Bezzi, Michele; Hebert, Cedric

2017-01-01

The Big Data revolution has promised to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, critical issues still need to be solved in the road that leads to commodization of Big Data Analytics, such as the management of Big Data complexity and the protection of data security and privacy. In this paper, we focus on the first issue and propose a methodology based on Model Driven Engineering (MDE) that aims to substantially lowe...

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

Visual pattern discovery in timed event data

Science.gov (United States)

Schaefer, Matthias; Wanner, Franz; Mansmann, Florian; Scheible, Christian; Stennett, Verity; Hasselrot, Anders T.; Keim, Daniel A.

2011-01-01

Business processes have tremendously changed the way large companies conduct their business: The integration of information systems into the workflows of their employees ensures a high service level and thus high customer satisfaction. One core aspect of business process engineering are events that steer the workflows and trigger internal processes. Strict requirements on interval-scaled temporal patterns, which are common in time series, are thereby released through the ordinal character of such events. It is this additional degree of freedom that opens unexplored possibilities for visualizing event data. In this paper, we present a flexible and novel system to find significant events, event clusters and event patterns. Each event is represented as a small rectangle, which is colored according to categorical, ordinal or intervalscaled metadata. Depending on the analysis task, different layout functions are used to highlight either the ordinal character of the data or temporal correlations. The system has built-in features for ordering customers or event groups according to the similarity of their event sequences, temporal gap alignment and stacking of co-occurring events. Two characteristically different case studies dealing with business process events and news articles demonstrate the capabilities of our system to explore event data.

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.

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

Nonequilibriun Dynamic Phases of Driven Vortex Lattices in Superconductors with Periodic Pinning Arrays

Science.gov (United States)

Reichhardt, C.; Olson, C. J.; Nori, F.

1998-03-01

We present results from extensive simulations of driven vortex lattices interacting with periodic pinning arrays. Changing an applied driving force produces an exceptionally rich variety of distinct dynamic phases which include over a dozen well defined plastic flow phases. Transitions between different dynamical phases are marked by sharp jumps in the V(I) curves that coincide with distinct changes in the vortex trajectories and vortex lattice order. A series of dynamical phase diagrams are presented which outline the onset of the different dynamical phases (C. Reichhardt, C.J. Olson, and F. Nori, Phys. Rev. Lett. 78), 2648 (1997); and to be published. Videos are avaliable at http://www-personal.engin.umich.edu/ñori/. Using force balance arguments, several of the phase boundaries can be derived analyticaly.

Comparing the accuracy of ABC and time-driven ABC in complex and dynamic environments: a simulation analysis

OpenAIRE

S. HOOZÉE; M. VANHOUCKE; W. BRUGGEMAN; -

2010-01-01

This paper compares the accuracy of traditional ABC and time-driven ABC in complex and dynamic environments through simulation analysis. First, when unit times in time-driven ABC are known or can be flawlessly estimated, time-driven ABC coincides with the benchmark system and in this case our results show that the overall accuracy of traditional ABC depends on (1) existing capacity utilization, (2) diversity in the actual mix of productive work, and (3) error in the estimated percentage mix. ...

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.

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…

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.

Ultrafast Dynamics in Light-Driven Molecular Rotary Motors Probed by Femtosecond Stimulated Raman Spectroscopy

NARCIS (Netherlands)

Hall, Christopher R.; Conyard, Jamie; Heisler, Ismael A.; Jones, Garth; Frost, James; Browne, Wesley R.; Feringa, Ben L.; Meech, Stephen R.

2017-01-01

Photochemical isomerization in sterically crowded chiral alkenes is the driving force for molecular rotary motors in nanoscale machines. Here the excited-state dynamics and structural evolution of the prototypical light-driven rotary motor are followed on the ultrafast time scale by femtosecond

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

Surface Management System Departure Event Data Analysis

Science.gov (United States)

Monroe, Gilena A.

2010-01-01

This paper presents a data analysis of the Surface Management System (SMS) performance of departure events, including push-back and runway departure events.The paper focuses on the detection performance, or the ability to detect departure events, as well as the prediction performance of SMS. The results detail a modest overall detection performance of push-back events and a significantly high overall detection performance of runway departure events. The overall detection performance of SMS for push-back events is approximately 55%.The overall detection performance of SMS for runway departure events nears 100%. This paper also presents the overall SMS prediction performance for runway departure events as well as the timeliness of the Aircraft Situation Display for Industry data source for SMS predictions.

A User Driven Dynamic Circuit Network Implementation

Energy Technology Data Exchange (ETDEWEB)

Guok, Chin; Robertson, David; Chaniotakis, Evangelos; Thompson, Mary; Johnston, William; Tierney, Brian

2008-10-01

The requirements for network predictability are becoming increasingly critical to the DoE science community where resources are widely distributed and collaborations are world-wide. To accommodate these emerging requirements, the Energy Sciences Network has established a Science Data Network to provide user driven guaranteed bandwidth allocations. In this paper we outline the design, implementation, and secure coordinated use of such a network, as well as some lessons learned.

Population Analysis of Adverse Events in Different Age Groups Using Big Clinical Trials Data.

Science.gov (United States)

Luo, Jake; Eldredge, Christina; Cho, Chi C; Cisler, Ron A

2016-10-17

Understanding adverse event patterns in clinical studies across populations is important for patient safety and protection in clinical trials as well as for developing appropriate drug therapies, procedures, and treatment plans. The objective of our study was to conduct a data-driven population-based analysis to estimate the incidence, diversity, and association patterns of adverse events by age of the clinical trials patients and participants. Two aspects of adverse event patterns were measured: (1) the adverse event incidence rate in each of the patient age groups and (2) the diversity of adverse events defined as distinct types of adverse events categorized by organ system. Statistical analysis was done on the summarized clinical trial data. The incident rate and diversity level in each of the age groups were compared with the lowest group (reference group) using t tests. Cohort data was obtained from ClinicalTrials.gov, and 186,339 clinical studies were analyzed; data were extracted from the 17,853 clinical trials that reported clinical outcomes. The total number of clinical trial participants was 6,808,619, and total number of participants affected by adverse events in these trials was 1,840,432. The trial participants were divided into eight different age groups to support cross-age group comparison. In general, children and older patients are more susceptible to adverse events in clinical trial studies. Using the lowest incidence age group as the reference group (20-29 years), the incidence rate of the 0-9 years-old group was 31.41%, approximately 1.51 times higher (P=.04) than the young adult group (20-29 years) at 20.76%. The second-highest group is the 50-59 years-old group with an incidence rate of 30.09%, significantly higher (Pgroup. The adverse event diversity also increased with increase in patient age. Clinical studies that recruited older patients (older than 40 years) were more likely to observe a diverse range of adverse events (Page group (older

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.

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

Towards Hybrid Online On-Demand Querying of Realtime Data with Stateful Complex Event Processing

Energy Technology Data Exchange (ETDEWEB)

Zhou, Qunzhi; Simmhan, Yogesh; Prasanna, Viktor K.

2013-10-09

Emerging Big Data applications in areas like e-commerce and energy industry require both online and on-demand queries to be performed over vast and fast data arriving as streams. These present novel challenges to Big Data management systems. Complex Event Processing (CEP) is recognized as a high performance online query scheme which in particular deals with the velocity aspect of the 3-V’s of Big Data. However, traditional CEP systems do not consider data variety and lack the capability to embed ad hoc queries over the volume of data streams. In this paper, we propose H2O, a stateful complex event processing framework, to support hybrid online and on-demand queries over realtime data. We propose a semantically enriched event and query model to address data variety. A formal query algebra is developed to precisely capture the stateful and containment semantics of online and on-demand queries. We describe techniques to achieve the interactive query processing over realtime data featured by efficient online querying, dynamic stream data persistence and on-demand access. The system architecture is presented and the current implementation status reported.

Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

Science.gov (United States)

Lee, Joon; Maslove, David M; Dubin, Joel A

2015-01-01

Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to

Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

Directory of Open Access Journals (Sweden)

Joon Lee

Full Text Available Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1 to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2 to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made.We deployed a cosine-similarity-based patient similarity metric (PSM to an intensive care unit (ICU database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care.The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR systems, our novel medical data analytics

Data Science Solution to Event Prediction in Outsourced Clinical Trial Models.

Science.gov (United States)

Dalevi, Daniel; Lovick, Susan; Mann, Helen; Metcalfe, Paul D; Spencer, Stuart; Hollis, Sally; Ruau, David

2015-01-01

Late phase clinical trials are regularly outsourced to a Contract Research Organisation (CRO) while the risk and accountability remain within the sponsor company. Many statistical tasks are delivered by the CRO and later revalidated by the sponsor. Here, we report a technological approach to standardised event prediction. We have built a dynamic web application around an R-package with the aim of delivering reliable event predictions, simplifying communication and increasing trust between the CRO and the in-house statisticians via transparency. Short learning curve, interactivity, reproducibility and data diagnostics are key here. The current implementation is motivated by time-to-event prediction in oncology. We demonstrate a clear benefit of standardisation for both parties. The tool can be used for exploration, communication, sensitivity analysis and generating standard reports. At this point we wish to present this tool and share some of the insights we have gained during the development.

Collecting operational event data for statistical analysis

International Nuclear Information System (INIS)

Atwood, C.L.

1994-09-01

This report gives guidance for collecting operational data to be used for statistical analysis, especially analysis of event counts. It discusses how to define the purpose of the study, the unit (system, component, etc.) to be studied, events to be counted, and demand or exposure time. Examples are given of classification systems for events in the data sources. A checklist summarizes the essential steps in data collection for statistical analysis

Nonlinear dynamics of a driven mode near marginal stability

International Nuclear Information System (INIS)

Berk, H.L.; Breizman, B.N.; Pekker, M.

1995-09-01

The nonlinear dynamics of a linearly unstable mode in a driven kinetic system is investigated to determine scaling of the saturated fields near the instability threshold. To leading order, this problem reduces to solving an integral equation with a temporally nonlocal cubic term. This equation can exhibit a self-similar solution that blows up in a finite time. When the blow-up occurs, higher nonlinearities become important and the mode saturates due to plateau formation arising from particle trapping in the wave. Otherwise, the simplified equation gives a regular solution that leads to a different saturation scaling reflecting the closeness to the instability threshold

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....

Quantum system driven by incoherent a.c fields: Multi-crossing Landau Zener dynamics

Energy Technology Data Exchange (ETDEWEB)

Jipdi, M.N., E-mail: jmichaelnicky@yahoo.fr; Fai, L.C.; Tchoffo, M.

2016-10-23

The paper investigates the multi-crossing dynamics of a Landau–Zener (LZ) system driven by two sinusoidal a.c fields applying the Dynamic Matrix approach (DMA). The system is shown to follow one-crossing and multi-crossing dynamics for low and high frequency regime respectively. It is shown that in low frequency regime, the resonance phenomenon occurs and leads to the decoupling of basis states; the effective gap vanishes and then the complete blockage of the system. For high frequency, the system achieves multi-crossing dynamics with two fictitious crossings; the system models a Landau–Zener–Stückelberg (LZS) interferometer with critical parameters that tailor probabilities. The system is then shown to depend only on the phase that permits the easiest control with possible application in implementing logic gates.

Big Data tools as applied to ATLAS event data

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00225336; The ATLAS collaboration; Gardner, Robert; Bryant, Lincoln

2017-01-01

Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Logfiles, database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and associated analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data. Such modes would simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of machine learning environments and to...

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.

Sensitivity of Water Scarcity Events to ENSO-Driven Climate Variability at the Global Scale

Science.gov (United States)

Veldkamp, T. I. E.; Eisner, S.; Wada, Y.; Aerts, J. C. J. H.; Ward, P. J.

2015-01-01

Globally, freshwater shortage is one of the most dangerous risks for society. Changing hydro-climatic and socioeconomic conditions have aggravated water scarcity over the past decades. A wide range of studies show that water scarcity will intensify in the future, as a result of both increased consumptive water use and, in some regions, climate change. Although it is well-known that El Niño- Southern Oscillation (ENSO) affects patterns of precipitation and drought at global and regional scales, little attention has yet been paid to the impacts of climate variability on water scarcity conditions, despite its importance for adaptation planning. Therefore, we present the first global-scale sensitivity assessment of water scarcity to ENSO, the most dominant signal of climate variability. We show that over the time period 1961-2010, both water availability and water scarcity conditions are significantly correlated with ENSO-driven climate variability over a large proportion of the global land area (> 28.1 %); an area inhabited by more than 31.4% of the global population. We also found, however, that climate variability alone is often not enough to trigger the actual incidence of water scarcity events. The sensitivity of a region to water scarcity events, expressed in terms of land area or population exposed, is determined by both hydro-climatic and socioeconomic conditions. Currently, the population actually impacted by water scarcity events consists of 39.6% (CTA: consumption-to-availability ratio) and 41.1% (WCI: water crowding index) of the global population, whilst only 11.4% (CTA) and 15.9% (WCI) of the global population is at the same time living in areas sensitive to ENSO-driven climate variability. These results are contrasted, however, by differences in growth rates found under changing socioeconomic conditions, which are relatively high in regions exposed to water scarcity events. Given the correlations found between ENSO and water availability and scarcity

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

Asymmetric driven dynamics of Dzyaloshinskii domain walls in ultrathin ferromagnetic strips with perpendicular magnetic anisotropy

Energy Technology Data Exchange (ETDEWEB)

Sánchez-Tejerina, L. [Dpto. Electricidad y Electrónica, Facultad de Ciencias, Universidad de Valladolid, 47011 Valladolid (Spain); Alejos, Ó., E-mail: oscaral@ee.uva.es [Dpto. Electricidad y Electrónica, Facultad de Ciencias, Universidad de Valladolid, 47011 Valladolid (Spain); Martínez, E. [Dpto. Física Aplicada, Facultad de Ciencias, Universidad de Salamanca, 37011 Salamanca (Spain); Muñoz, J.M. [Dpto. Electricidad y Electrónica, Facultad de Ciencias, Universidad de Valladolid, 47011 Valladolid (Spain)

2016-07-01

The dynamics of domain walls in ultrathin ferromagnetic strips with perpendicular magnetic anisotropy is studied from both numerical and analytical micromagnetics. The influence of a moderate interfacial Dzyaloshinskii–Moriya interaction associated to a bi-layer strip arrangement has been considered, giving rise to the formation of Dzyaloshinskii domain walls. Such walls possess under equilibrium conditions an inner magnetization structure defined by a certain orientation angle that make them to be considered as intermediate configurations between Bloch and Néel walls. Two different dynamics are considered, a field-driven and a current-driven dynamics, in particular, the one promoted by the spin torque due to the spin-Hall effect. Results show an inherent asymmetry associated with the rotation of the domain wall magnetization orientation before reaching the stationary regime, characterized by a constant terminal speed. For a certain initial DW magnetization orientation at rest, the rotation determines whether the reorientation of the DW magnetization prior to reach stationary motion is smooth or abrupt. This asymmetry affects the DW motion, which can even reverse for a short period of time. Additionally, it is found that the terminal speed in the case of the current-driven dynamics may depend on either the initial DW magnetization orientation at rest or the sign of the longitudinally injected current. - Highlights: • The asymmetric response of domain walls in bilayer strips with PMA is studied. • Out-of-plane fields and SHE longitudinal currents are applied. • The response is associated to the rotation of the domain wall inner magnetization. • Clockwise and counter-clockwise magnetization rotations are not equivalent. • The asymmetry results in different travelled distances and/or terminal speeds.

Asymmetric driven dynamics of Dzyaloshinskii domain walls in ultrathin ferromagnetic strips with perpendicular magnetic anisotropy

International Nuclear Information System (INIS)

Sánchez-Tejerina, L.; Alejos, Ó.; Martínez, E.; Muñoz, J.M.

2016-01-01

The dynamics of domain walls in ultrathin ferromagnetic strips with perpendicular magnetic anisotropy is studied from both numerical and analytical micromagnetics. The influence of a moderate interfacial Dzyaloshinskii–Moriya interaction associated to a bi-layer strip arrangement has been considered, giving rise to the formation of Dzyaloshinskii domain walls. Such walls possess under equilibrium conditions an inner magnetization structure defined by a certain orientation angle that make them to be considered as intermediate configurations between Bloch and Néel walls. Two different dynamics are considered, a field-driven and a current-driven dynamics, in particular, the one promoted by the spin torque due to the spin-Hall effect. Results show an inherent asymmetry associated with the rotation of the domain wall magnetization orientation before reaching the stationary regime, characterized by a constant terminal speed. For a certain initial DW magnetization orientation at rest, the rotation determines whether the reorientation of the DW magnetization prior to reach stationary motion is smooth or abrupt. This asymmetry affects the DW motion, which can even reverse for a short period of time. Additionally, it is found that the terminal speed in the case of the current-driven dynamics may depend on either the initial DW magnetization orientation at rest or the sign of the longitudinally injected current. - Highlights: • The asymmetric response of domain walls in bilayer strips with PMA is studied. • Out-of-plane fields and SHE longitudinal currents are applied. • The response is associated to the rotation of the domain wall inner magnetization. • Clockwise and counter-clockwise magnetization rotations are not equivalent. • The asymmetry results in different travelled distances and/or terminal speeds.

Forecasting wind-driven wildfires using an inverse modelling approach

Directory of Open Access Journals (Sweden)

O. Rios

2014-06-01

Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

Data analysis of event tape and connection

International Nuclear Information System (INIS)

Gong Huili

1995-01-01

The data analysis on the VAX-11/780 computer is briefly described, the data is from the recorded event tape of JUHU data acquisition system on the PDP-11/44 computer. The connection of the recorded event tapes of the XSYS data acquisition system on VAX computer is also introduced

Selection-driven extinction dynamics for group II introns in Enterobacteriales.

Directory of Open Access Journals (Sweden)

Sébastien Leclercq

Full Text Available Transposable elements (TEs are one of the major driving forces of genome evolution, raising the question of the long-term dynamics underlying their evolutionary success. Some TEs were proposed to evolve under a pattern of periodic extinctions-recolonizations, in which elements recurrently invade and quickly proliferate within their host genomes, then start to disappear until total extinction. Depending on the model, TE extinction is assumed to be driven by purifying selection against colonized host genomes (Sel-DE model or by saturation of host genomes (Sat-DE model. Bacterial group II introns are suspected to follow an extinction-recolonization model of evolution, but whether they follow Sel-DE or Sat-DE dynamics is not known. Our analysis of almost 200 group II intron copies from 90 sequenced Enterobacteriales genomes confirms their extinction-recolonization dynamics: patchy element distributions among genera and even among strains within genera, acquisition of new group II introns through plasmids or other mobile genetic elements, and evidence for recent proliferations in some genomes. Distributions of recent and past proliferations and of their respective homing sites further provide strong support for the Sel-DE model, suggesting that group II introns are deleterious to their hosts. Overall, our observations emphasize the critical impact of host properties on TE dynamics.

Sequences by Metastable Attractors: Interweaving Dynamical Systems and Experimental Data

Directory of Open Access Journals (Sweden)

Axel Hutt

2017-05-01

Full Text Available Metastable attractors and heteroclinic orbits are present in the dynamics of various complex systems. Although their occurrence is well-known, their identification and modeling is a challenging task. The present work reviews briefly the literature and proposes a novel combination of their identification in experimental data and their modeling by dynamical systems. This combination applies recurrence structure analysis permitting the derivation of an optimal symbolic representation of metastable states and their dynamical transitions. To derive heteroclinic sequences of metastable attractors in various experimental conditions, the work introduces a Hausdorff clustering algorithm for symbolic dynamics. The application to brain signals (event-related potentials utilizing neural field models illustrates the methodology.

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.

Analysis of event-mode data with Interactive Data Language

International Nuclear Information System (INIS)

De Young, P.A.; Hilldore, B.B.; Kiessel, L.M.; Peaslee, G.F.

2003-01-01

We have developed an analysis package for event-mode data based on Interactive Data Language (IDL) from Research Systems Inc. This high-level language is high speed, array oriented, object oriented, and has extensive visual (multi-dimensional plotting) and mathematical functions. We have developed a general framework, written in IDL, for the analysis of a variety of experimental data that does not require significant customization for each analysis. Unlike many traditional analysis package, spectra and gates are applied after data are read and are easily changed as analysis proceeds without rereading the data. The events are not sequentially processed into predetermined arrays subject to predetermined gates

Droplet spreading driven by van der Waals force: a molecular dynamics study

KAUST Repository

Wu, Congmin

2010-07-07

The dynamics of droplet spreading is investigated by molecular dynamics simulations for two immiscible fluids of equal density and viscosity. All the molecular interactions are modeled by truncated Lennard-Jones potentials and a long-range van der Waals force is introduced to act on the wetting fluid. By gradually increasing the coupling constant in the attractive van der Waals interaction between the wetting fluid and the substrate, we observe a transition in the initial stage of spreading. There exists a critical value of the coupling constant, above which the spreading is pioneered by a precursor film. In particular, the dynamically determined critical value quantitatively agrees with that determined by the energy criterion that the spreading coefficient equals zero. The latter separates partial wetting from complete wetting. In the regime of complete wetting, the radius of the spreading droplet varies with time as R(t) ∼ √t, a behavior also found in molecular dynamics simulations where the wetting dynamics is driven by the short-range Lennard-Jones interaction between liquid and solid. © 2010 IOP Publishing Ltd.

A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

Science.gov (United States)

Turso, James A.; Litt, Jonathan S.

2004-01-01

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

Map updates in a dynamic Voronoi data structure

DEFF Research Database (Denmark)

Mioc, Darka; Antón Castro, Francesc/François; Gold, C. M.

2006-01-01

In this paper we are using local and sequential map updates in the Voronoi data structure, which allows us to automatically record each event and performed map updates within the system. These map updates are executed through map construction commands that are composed of atomic actions (geometric...... algorithms for addition, deletion, and motion of spatial objects) on the dynamic Voronoi data structure. The formalization of map commands led to the development of a spatial language comprising a set of atomic operations or constructs on spatial primitives (points and lines), powerful enough to define...

Selective Attention in Multi-Chip Address-Event Systems

Directory of Open Access Journals (Sweden)

Giacomo Indiveri

2009-06-01

Full Text Available Selective attention is the strategy used by biological systems to cope with the inherent limits in their available computational resources, in order to efficiently process sensory information. The same strategy can be used in artificial systems that have to process vast amounts of sensory data with limited resources. In this paper we present a neuromorphic VLSI device, the “Selective Attention Chip” (SAC, which can be used to implement these models in multi-chip address-event systems. We also describe a real-time sensory-motor system, which integrates the SAC with a dynamic vision sensor and a robotic actuator. We present experimental results from each component in the system, and demonstrate how the complete system implements a real-time stimulus-driven selective attention model.

Selective attention in multi-chip address-event systems.

Science.gov (United States)

Bartolozzi, Chiara; Indiveri, Giacomo

2009-01-01

Selective attention is the strategy used by biological systems to cope with the inherent limits in their available computational resources, in order to efficiently process sensory information. The same strategy can be used in artificial systems that have to process vast amounts of sensory data with limited resources. In this paper we present a neuromorphic VLSI device, the "Selective Attention Chip" (SAC), which can be used to implement these models in multi-chip address-event systems. We also describe a real-time sensory-motor system, which integrates the SAC with a dynamic vision sensor and a robotic actuator. We present experimental results from each component in the system, and demonstrate how the complete system implements a real-time stimulus-driven selective attention model.

Real-time control and data-acquisition system for high-energy neutral-beam injectors

International Nuclear Information System (INIS)

Glad, A.S.; Jacobson, V.

1981-12-01

The need for a real-time control system and a data acquisition, processing and archiving system operating in parallel on the same computer became a requirement on General Atomic's Doublet III fusion energy project with the addition of high energy neutral beam injectors. The data acquisition processing and archiving system is driven from external events and is sequenced through each experimental shot utilizing ModComp's intertask message service. This system processes, archives and displays on operator console CRTs all physics diagnostic data related to the neutral beam injectores such as temperature, beam alignment, etc. The real-time control system is data base driven and provides periodic monitoring and control of the numerous dynamic subsystems of the neutral beam injectors such as power supplies, timing, water cooling, etc

Complex Pattern Formation from Current-Driven Dynamics of Single-Layer Homoepitaxial Islands on Crystalline Conducting Substrates

Science.gov (United States)

Kumar, Ashish; Dasgupta, Dwaipayan; Maroudas, Dimitrios

2017-07-01

We report a systematic study of complex pattern formation resulting from the driven dynamics of single-layer homoepitaxial islands on surfaces of face-centered-cubic (fcc) crystalline conducting substrates under the action of an externally applied electric field. The analysis is based on an experimentally validated nonlinear model of mass transport via island edge atomic diffusion, which also accounts for edge diffusional anisotropy. We analyze the morphological stability and simulate the field-driven evolution of rounded islands for an electric field oriented along the fast edge diffusion direction. For larger-than-critical island sizes on {110 } and {100 } fcc substrates, we show that multiple necking instabilities generate complex island patterns, including not-simply-connected void-containing islands mediated by sequences of breakup and coalescence events and distributed symmetrically with respect to the electric field direction. We analyze the dependence of the formed patterns on the original island size and on the duration of application of the external field. Starting from a single large rounded island, we characterize the evolution of the number of daughter islands and their average size and uniformity. The evolution of the average island size follows a universal power-law scaling relation, and the evolution of the total edge length of the islands in the complex pattern follows Kolmogorov-Johnson-Mehl-Avrami kinetics. Our study makes a strong case for the use of electric fields, as precisely controlled macroscopic forcing, toward surface patterning involving complex nanoscale features.

Shock propagation in locally driven granular systems

Science.gov (United States)

Joy, Jilmy P.; Pathak, Sudhir N.; Das, Dibyendu; Rajesh, R.

2017-09-01

We study shock propagation in a system of initially stationary hard spheres that is driven by a continuous injection of particles at the origin. The disturbance created by the injection of energy spreads radially outward through collisions between particles. Using scaling arguments, we determine the exponent characterizing the power-law growth of this disturbance in all dimensions. The scaling functions describing the various physical quantities are determined using large-scale event-driven simulations in two and three dimensions for both elastic and inelastic systems. The results are shown to describe well the data from two different experiments on granular systems that are similarly driven.

Big Data: An Opportunity for Collaboration with Computer Scientists on Data-Driven Science

Science.gov (United States)

Baru, C.

2014-12-01

Big data technologies are evolving rapidly, driven by the need to manage ever increasing amounts of historical data; process relentless streams of human and machine-generated data; and integrate data of heterogeneous structure from extremely heterogeneous sources of information. Big data is inherently an application-driven problem. Developing the right technologies requires an understanding of the applications domain. Though, an intriguing aspect of this phenomenon is that the availability of the data itself enables new applications not previously conceived of! In this talk, we will discuss how the big data phenomenon creates an imperative for collaboration among domain scientists (in this case, geoscientists) and computer scientists. Domain scientists provide the application requirements as well as insights about the data involved, while computer scientists help assess whether problems can be solved with currently available technologies or require adaptaion of existing technologies and/or development of new technologies. The synergy can create vibrant collaborations potentially leading to new science insights as well as development of new data technologies and systems. The area of interface between geosciences and computer science, also referred to as geoinformatics is, we believe, a fertile area for interdisciplinary research.

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.

Time-Dependent Statistical Analysis of Wide-Area Time-Synchronized Data

Directory of Open Access Journals (Sweden)

A. R. Messina

2010-01-01

Full Text Available Characterization of spatial and temporal changes in the dynamic patterns of a nonstationary process is a problem of great theoretical and practical importance. On-line monitoring of large-scale power systems by means of time-synchronized Phasor Measurement Units (PMUs provides the opportunity to analyze and characterize inter-system oscillations. Wide-area measurement sets, however, are often relatively large, and may contain phenomena with differing temporal scales. Extracting from these measurements the relevant dynamics is a difficult problem. As the number of observations of real events continues to increase, statistical techniques are needed to help identify relevant temporal dynamics from noise or random effects in measured data. In this paper, a statistically based, data-driven framework that integrates the use of wavelet-based EOF analysis and a sliding window-based method is proposed to identify and extract, in near-real-time, dynamically independent spatiotemporal patterns from time synchronized data. The method deals with the information in space and time simultaneously, and allows direct tracking and characterization of the nonstationary time-frequency dynamics of oscillatory processes. The efficiency and accuracy of the developed procedures for extracting localized information of power system behavior from time-synchronized phasor measurements of a real event in Mexico is assessed.

CLARA: A Contemporary Approach to Physics Data Processing

Energy Technology Data Exchange (ETDEWEB)

V Gyurjyan, D Abbott, J Carbonneau, G Gilfoyle, D Heddle, G Heyes, S Paul, C Timmer, D Weygand, E Wolin

2011-12-01

In traditional physics data processing (PDP) systems, data location is static and is accessed by analysis applications. In comparison, CLARA (CLAS12 Reconstruction and Analysis framework) is an environment where data processing algorithms filter continuously flowing data. In CLARA's domain of loosely coupled services, data is not stored, but rather flows from one service to another, mutating constantly along the way. Agents, performing event processing, can then subscribe to particular data/events at any stage of the data transformation, and make intricate decisions (e.g. particle ID) by correlating events from multiple, parallel data streams and/or services. This paper presents a PDP application development framework based on service oriented and event driven architectures. This system allows users to design (Java, C++, and Python languages are supported) and deploy data processing services, as well as dynamically compose PDP applications using available services. The PDP service bus provides a layer on top of a distributed pub-sub middleware implementation, which allows complex service composition and integration without writing code. Examples of service creation and deployment, along with the CLAS12 track reconstruction application design will be presented.

Event-Driven Process Chains (EPC)

Science.gov (United States)

Mendling, Jan

This chapter provides a comprehensive overview of Event-driven Process Chains (EPCs) and introduces a novel definition of EPC semantics. EPCs became popular in the 1990s as a conceptual business process modeling language in the context of reference modeling. Reference modeling refers to the documentation of generic business operations in a model such as service processes in the telecommunications sector, for example. It is claimed that reference models can be reused and adapted as best-practice recommendations in individual companies (see [230, 168, 229, 131, 400, 401, 446, 127, 362, 126]). The roots of reference modeling can be traced back to the Kölner Integrationsmodell (KIM) [146, 147] that was developed in the 1960s and 1970s. In the 1990s, the Institute of Information Systems (IWi) in Saarbrücken worked on a project with SAP to define a suitable business process modeling language to document the processes of the SAP R/3 enterprise resource planning system. There were two results from this joint effort: the definition of EPCs [210] and the documentation of the SAP system in the SAP Reference Model (see [92, 211]). The extensive database of this reference model contains almost 10,000 sub-models: 604 of them non-trivial EPC business process models. The SAP Reference model had a huge impact with several researchers referring to it in their publications (see [473, 235, 127, 362, 281, 427, 415]) as well as motivating the creation of EPC reference models in further domains including computer integrated manufacturing [377, 379], logistics [229] or retail [52]. The wide-spread application of EPCs in business process modeling theory and practice is supported by their coverage in seminal text books for business process management and information systems in general (see [378, 380, 49, 384, 167, 240]). EPCs are frequently used in practice due to a high user acceptance [376] and extensive tool support. Some examples of tools that support EPCs are ARIS Toolset by IDS

Stability and economy analysis based on computational fluid dynamics and field testing of hybrid-driven underwater glider with the water quality sensor in Danjiangkou Reservoir

Directory of Open Access Journals (Sweden)

Chao Li

2015-12-01

Full Text Available Hybrid-driven underwater glider is a new kind of unmanned platform for water quality monitoring. It has advantages such as high controllability and maneuverability, low cost, easy operation, and ability to carry multiple sensors. This article develops a hybrid-driven underwater glider, PETRELII, and integrates a water quality monitoring sensor. Considering stability and economy, an optimal layout scheme is selected from four candidates by simulation using computational fluid dynamics method. Trials were carried out in Danjiangkou Reservoir—important headwaters of the Middle Route of the South-to-North Water Diversion Project. In the trials, a monitoring strategy with polygonal mixed-motion was adopted to make full use of the advantages of the unmanned platform. The measuring data, including temperature, dissolved oxygen, conductivity, pH, turbidity, chlorophyll, and ammonia nitrogen, are obtained. These data validate the practicability of the theoretical layout obtained using computational fluid dynamics method and the practical performance of PETRELII with sensor.

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.

Reynolds-number-dependent dynamical transitions on hydrodynamic synchronization modes of externally driven colloids

Science.gov (United States)

Oyama, Norihiro; Teshigawara, Kosuke; Molina, John Jairo; Yamamoto, Ryoichi; Taniguchi, Takashi

2018-03-01

The collective dynamics of externally driven Np-colloidal systems (1 ≤Np≤4 ) in a confined viscous fluid have been investigated using three-dimensional direct numerical simulations with fully resolved hydrodynamics. The dynamical modes of collective particle motion are studied by changing the particle Reynolds number as determined by the strength of the external driving force and the confining wall distance. For a system with Np=3 , we found that at a critical Reynolds number a dynamical mode transition occurs from the doublet-singlet mode to the triplet mode, which has not been reported experimentally. The dynamical mode transition was analyzed in detail from the following two viewpoints: (1) spectrum analysis of the time evolution of a tagged particle velocity and (2) the relative acceleration of the doublet cluster with respect to the singlet particle. For a system with Np=4 , we found similar dynamical mode transitions from the doublet-singlet-singlet mode to the triplet-singlet mode and further to the quartet mode.

Big Data Analytics Tools as Applied to ATLAS Event Data

CERN Document Server

Vukotic, Ilija; The ATLAS collaboration

2016-01-01

Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Log file data and database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data so as to simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of big data, statistical and machine learning tools...

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.

Transition to Collisionless Ion-Temperature-Gradient-Driven Plasma Turbulence: A Dynamical Systems Approach

International Nuclear Information System (INIS)

Kolesnikov, R.A.; Krommes, J.A.

2005-01-01

The transition to collisionless ion-temperature-gradient-driven plasma turbulence is considered by applying dynamical systems theory to a model with 10 degrees of freedom. The study of a four-dimensional center manifold predicts a 'Dimits shift' of the threshold for turbulence due to the excitation of zonal flows and establishes (for the model) the exact value of that shift

Enhancing Business Process Automation by Integrating RFID Data and Events

Science.gov (United States)

Zhao, Xiaohui; Liu, Chengfei; Lin, Tao

Business process automation is one of the major benefits for utilising Radio Frequency Identification (RFID) technology. Through readers to RFID middleware systems, the information and the movements of tagged objects can be used to trigger business transactions. These features change the way of business applications for dealing with the physical world from mostly quantity-based to object-based. Aiming to facilitate business process automation, this paper introduces a new method to model and incorporate business logics into RFID edge systems from an object-oriented perspective with emphasises on RFID's event-driven characteristics. A framework covering business rule modelling, event handling and system operation invocations is presented on the basis of the event calculus. In regard to the identified delayed effects in RFID-enabled applications, a two-block buffering mechanism is proposed to improve RFID query efficiency within the framework. The performance improvements are analysed with related experiments.

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...

A Multi-mission Event-Driven Component-Based System for Support of Flight Software Development, ATLO, and Operations first used by the Mars Science Laboratory (MSL) Project

Science.gov (United States)

Dehghani, Navid; Tankenson, Michael

2006-01-01

This viewgraph presentation reviews the architectural description of the Mission Data Processing and Control System (MPCS). MPCS is an event-driven, multi-mission ground data processing components providing uplink, downlink, and data management capabilities which will support the Mars Science Laboratory (MSL) project as its first target mission. MPCS is designed with these factors (1) Enabling plug and play architecture (2) MPCS has strong inheritance from GDS components that have been developed for other Flight Projects (MER, MRO, DAWN, MSAP), and are currently being used in operations and ATLO, and (3) MPCS components are Java-based, platform independent, and are designed to consume and produce XML-formatted data

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.

Data Prediction for Public Events in Professional Domains Based on Improved RNN- LSTM

Science.gov (United States)

Song, Bonan; Fan, Chunxiao; Wu, Yuexin; Sun, Juanjuan

2018-02-01

The traditional data services of prediction for emergency or non-periodic events usually cannot generate satisfying result or fulfill the correct prediction purpose. However, these events are influenced by external causes, which mean certain a priori information of these events generally can be collected through the Internet. This paper studied the above problems and proposed an improved model—LSTM (Long Short-term Memory) dynamic prediction and a priori information sequence generation model by combining RNN-LSTM and public events a priori information. In prediction tasks, the model is qualified for determining trends, and its accuracy also is validated. This model generates a better performance and prediction results than the previous one. Using a priori information can increase the accuracy of prediction; LSTM can better adapt to the changes of time sequence; LSTM can be widely applied to the same type of prediction tasks, and other prediction tasks related to time sequence.

Dynamic Water Storage during Flash Flood Events in the Mountainous Area of Rio de Janeiro/Brazil - Case study: Piabanha River Basin

Science.gov (United States)

Araujo, L.; Silva, F. P. D.; Moreira, D. M.; Vásquez P, I. L.; Justi da Silva, M. G. A.; Fernandes, N.; Rotunno Filho, O. C.

2017-12-01

Flash floods are characterized by a rapid rise in water levels, high flow rates and large amounts of debris. Several factors have relevance to the occurrence of these phenomena, including high precipitation rates, terrain slope, soil saturation degree, vegetation cover, soil type, among others. In general, the greater the precipitation intensity, the more likely is the occurrence of a significant increase in flow rate. Particularly on steep and rocky plains or heavily urbanized areas, relatively small rain rates can trigger a flash flood event. In addition, high rain rates in short time intervals can temporarily saturate the surface soil layer acting as waterproofing and favoring the occurrence of greater runoff rates due to non-infiltration of rainwater into the soil. Thus, although precipitation is considered the most important factor for flooding, the interaction between rainfall and the soil can sometimes be of greater importance. In this context, this work investigates the dynamic storage of water associated with flash flood events for Quitandinha river watershed, a tributary of Piabanha river, occurred between 2013 and 2014, by means of water balance analyses applied to three watersheds of varying magnitudes (9.25 km², 260 km² and 429 km²) along the rainy season under different time steps (hourly and daily) using remotely sensed and observational precipitation data. The research work is driven by the hypothesis of a hydrologically active bedrock layer, as the watershed is located in a humid region, having intemperate (fractured) rock layer, just below a shallow soil layer, in the higher part of the basin where steep slopes prevail. The results showed a delay of the variation of the dynamic storage in relation to rainfall peaks and water levels. Such behavior indicates that the surface soil layer, which is not very thick in the region, becomes rapidly saturated along rainfall events. Subsequently, the water infiltrates into the rocky layer and the water

Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data

Science.gov (United States)

Deng, Xinyi

2016-08-01

A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in

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.

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

Statistical Data Processing with R – Metadata Driven Approach

Directory of Open Access Journals (Sweden)

Rudi SELJAK

2016-06-01

Full Text Available In recent years the Statistical Office of the Republic of Slovenia has put a lot of effort into re-designing its statistical process. We replaced the classical stove-pipe oriented production system with general software solutions, based on the metadata driven approach. This means that one general program code, which is parametrized with process metadata, is used for data processing for a particular survey. Currently, the general program code is entirely based on SAS macros, but in the future we would like to explore how successfully statistical software R can be used for this approach. Paper describes the metadata driven principle for data validation, generic software solution and main issues connected with the use of statistical software R for this approach.

Data to Decisions: Creating a Culture of Model-Driven Drug Discovery.

Science.gov (United States)

Brown, Frank K; Kopti, Farida; Chang, Charlie Zhenyu; Johnson, Scott A; Glick, Meir; Waller, Chris L

2017-09-01

Merck & Co., Inc., Kenilworth, NJ, USA, is undergoing a transformation in the way that it prosecutes R&D programs. Through the adoption of a "model-driven" culture, enhanced R&D productivity is anticipated, both in the form of decreased attrition at each stage of the process and by providing a rational framework for understanding and learning from the data generated along the way. This new approach focuses on the concept of a "Design Cycle" that makes use of all the data possible, internally and externally, to drive decision-making. These data can take the form of bioactivity, 3D structures, genomics, pathway, PK/PD, safety data, etc. Synthesis of high-quality data into models utilizing both well-established and cutting-edge methods has been shown to yield high confidence predictions to prioritize decision-making and efficiently reposition resources within R&D. The goal is to design an adaptive research operating plan that uses both modeled data and experiments, rather than just testing, to drive project decision-making. To support this emerging culture, an ambitious information management (IT) program has been initiated to implement a harmonized platform to facilitate the construction of cross-domain workflows to enable data-driven decision-making and the construction and validation of predictive models. These goals are achieved through depositing model-ready data, agile persona-driven access to data, a unified cross-domain predictive model lifecycle management platform, and support for flexible scientist-developed workflows that simplify data manipulation and consume model services. The end-to-end nature of the platform, in turn, not only supports but also drives the culture change by enabling scientists to apply predictive sciences throughout their work and over the lifetime of a project. This shift in mindset for both scientists and IT was driven by an early impactful demonstration of the potential benefits of the platform, in which expert-level early discovery

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.

Event Detection Intelligent Camera: Demonstration of flexible, real-time data taking and processing

Energy Technology Data Exchange (ETDEWEB)

Szabolics, Tamás, E-mail: szabolics.tamas@wigner.mta.hu; Cseh, Gábor; Kocsis, Gábor; Szepesi, Tamás; Zoletnik, Sándor

2015-10-15

Highlights: • We present EDICAM's operation principles description. • Firmware tests results. • Software test results. • Further developments. - Abstract: An innovative fast camera (EDICAM – Event Detection Intelligent CAMera) was developed by MTA Wigner RCP in the last few years. This new concept was designed for intelligent event driven processing to be able to detect predefined events and track objects in the plasma. The camera provides a moderate frame rate of 400 Hz at full frame resolution (1280 × 1024), and readout of smaller region of interests can be done in the 1–140 kHz range even during exposure of the full image. One of the most important advantages of this hardware is a 10 Gbit/s optical link which ensures very fast communication and data transfer between the PC and the camera, enabling two level of processing: primitive algorithms in the camera hardware and high-level processing in the PC. This camera hardware has successfully proven to be able to monitoring the plasma in several fusion devices for example at ASDEX Upgrade, KSTAR and COMPASS with the first version of firmware. A new firmware and software package is under development. It allows to detect predefined events in real time and therefore the camera is capable to change its own operation or to give warnings e.g. to the safety system of the experiment. The EDICAM system can handle a huge amount of data (up to TBs) with high data rate (950 MB/s) and will be used as the central element of the 10 camera overview video diagnostic system of Wendenstein 7-X (W7-X) stellarator. This paper presents key elements of the newly developed built-in intelligence stressing the revolutionary new features and the results of the test of the different software elements.

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...

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

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

Dynamic Phases in Driven Vortex Lattices in Superconductors with Periodic Pinning Arrays.

Science.gov (United States)

Reichhardt, C.; Olson, C. J.; Nori, F.

1997-03-01

In an extensive series of simulations of driven vortices interacting with periodic pinning arrays, an extremely rich variety of novel plastic flow phases, very distinct from those observed in random arrays, are found as a function of applied driving force. We show that signatures of the transitions between these different dynamical phases appear as pronounced jumps and dips in the I-V curves, coinciding with marked changes in the microscopic structure and flow behavior of the vortex lattice. When the number of vortices is greater than the number of pinning sites, we observe up to six distinct dynamical phases, including a pinned phase, a flow of interstitial vortices between pinned vortices, a disordered flow, a 1D flow along the pinning rows, and a homogeneous flow. By varying a wide range of microscopic pinning parameters, including pinning strength, size, density, and degree of ordering, as well as varying temperature and commensurability, we obtain a series of dynamic phase diagrams. nori>A short video will also be presented to highlight these different dynamic phases.

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

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...

High Brightness, Laser-Driven X-ray Source for Nanoscale Metrology and Femtosecond Dynamics

Energy Technology Data Exchange (ETDEWEB)

Siders, C W; Crane, J K; Semenov, V; Betts, S; Kozioziemski, B; Wharton, K; Wilks, S; Barbee, T; Stuart, B; Kim, D E; An, J; Barty, C

2007-02-26

This project developed and demonstrated a new, bright, ultrafast x-ray source based upon laser-driven K-alpha generation, which can produce an x-ray flux 10 to 100 times greater than current microfocus x-ray tubes. The short-pulse (sub-picosecond) duration of this x-ray source also makes it ideal for observing time-resolved dynamics of atomic motion in solids and thin films.

Optimizing access to conditions data in ATLAS event data processing

CERN Document Server

Rinaldi, Lorenzo; The ATLAS collaboration

2018-01-01

The processing of ATLAS event data requires access to conditions data which is stored in database systems. This data includes, for example alignment, calibration, and configuration information that may be characterized by large volumes, diverse content, and/or information which evolves over time as refinements are made in those conditions. Additional layers of complexity are added by the need to provide this information across the world-wide ATLAS computing grid and the sheer number of simultaneously executing processes on the grid, each demanding a unique set of conditions to proceed. Distributing this data to all the processes that require it in an efficient manner has proven to be an increasing challenge with the growing needs and number of event-wise tasks. In this presentation, we briefly describe the systems in which we have collected information about the use of conditions in event data processing. We then proceed to explain how this information has been used to refine not only reconstruction software ...

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.

Dynamic control of laser driven proton beams by exploiting self-generated, ultrashort electromagnetic pulses

Energy Technology Data Exchange (ETDEWEB)

Kar, S., E-mail: s.kar@qub.ac.uk; Ahmed, H.; Nersisyan, G.; Hanton, F.; Naughton, K.; Lewis, C. L. S.; Borghesi, M. [Centre for Plasma Physics, School of Mathematics and Physics, Queen' s University Belfast, Belfast BT7 1NN (United Kingdom); Brauckmann, S.; Giesecke, A. L.; Willi, O. [Institut für Laser-und Plasmaphysik, Heinrich-Heine-Universität, Düsseldorf (Germany)

2016-05-15

As part of the ultrafast charge dynamics initiated by high intensity laser irradiations of solid targets, high amplitude EM pulses propagate away from the interaction point and are transported along any stalks and wires attached to the target. The propagation of these high amplitude pulses along a thin wire connected to a laser irradiated target was diagnosed via the proton radiography technique, measuring a pulse duration of ∼20 ps and a pulse velocity close to the speed of light. The strong electric field associated with the EM pulse can be exploited for controlling dynamically the proton beams produced from a laser-driven source. Chromatic divergence control of broadband laser driven protons (upto 75% reduction in divergence of >5 MeV protons) was obtained by winding the supporting wire around the proton beam axis to create a helical coil structure. In addition to providing focussing and energy selection, the technique has the potential to post-accelerate the transiting protons by the longitudinal component of the curved electric field lines produced by the helical coil lens.

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.

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.

Time-driven activity-based costing: A dynamic value assessment model in pediatric appendicitis.

Science.gov (United States)

Yu, Yangyang R; Abbas, Paulette I; Smith, Carolyn M; Carberry, Kathleen E; Ren, Hui; Patel, Binita; Nuchtern, Jed G; Lopez, Monica E

2017-06-01

Healthcare reform policies are emphasizing value-based healthcare delivery. We hypothesize that time-driven activity-based costing (TDABC) can be used to appraise healthcare interventions in pediatric appendicitis. Triage-based standing delegation orders, surgical advanced practice providers, and a same-day discharge protocol were implemented to target deficiencies identified in our initial TDABC model. Post-intervention process maps for a hospital episode were created using electronic time stamp data for simple appendicitis cases during February to March 2016. Total personnel and consumable costs were determined using TDABC methodology. The post-intervention TDABC model featured 6 phases of care, 33 processes, and 19 personnel types. Our interventions reduced duration and costs in the emergency department (-41min, -$23) and pre-operative floor (-57min, -$18). While post-anesthesia care unit duration and costs increased (+224min, +$41), the same-day discharge protocol eliminated post-operative floor costs (-$306). Our model incorporating all three interventions reduced total direct costs by 11% ($2753.39 to $2447.68) and duration of hospitalization by 51% (1984min to 966min). Time-driven activity-based costing can dynamically model changes in our healthcare delivery as a result of process improvement interventions. It is an effective tool to continuously assess the impact of these interventions on the value of appendicitis care. II, Type of study: Economic Analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric

Science.gov (United States)

Lee, Joon; Maslove, David M.; Dubin, Joel A.

2015-01-01

Background Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. Methods and Findings We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. Conclusions The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our

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.

The role of fluctuations and interactions in pedestrian dynamics

Science.gov (United States)

Corbetta, Alessandro; Meeusen, Jasper; Benzi, Roberto; Lee, Chung-Min; Toschi, Federico

Understanding quantitatively the statistical behaviour of pedestrians walking in crowds is a major scientific challenge of paramount societal relevance. Walking humans exhibit a rich (stochastic) dynamics whose small and large deviations are driven, among others, by own will as well as by environmental conditions. Via 24/7 automatic pedestrian tracking from multiple overhead Microsoft Kinect depth sensors, we collected large ensembles of pedestrian trajectories (in the order of tens of millions) in different real-life scenarios. These scenarios include both narrow corridors and large urban hallways, enabling us to cover and compare a wide spectrum of typical pedestrian dynamics. We investigate the pedestrian motion measuring the PDFs, e.g. those of position, velocity and acceleration, and at unprecedentedly high statistical resolution. We consider the dependence of PDFs on flow conditions, focusing on diluted dynamics and pair-wise interactions (''collisions'') for mutual avoidance. By means of Langevin-like models we provide models for the measured data, inclusive typical fluctuations and rare events. This work is part of the JSTP research programme ``Vision driven visitor behaviour analysis and crowd management'' with Project Number 341-10-001, which is financed by the Netherlands Organisation for Scientific Research (NWO).

Seasonal variability of stream water quality response to storm events captured using high-frequency and multi-parameter data

Science.gov (United States)

Fovet, O.; Humbert, G.; Dupas, R.; Gascuel-Odoux, C.; Gruau, G.; Jaffrezic, A.; Thelusma, G.; Faucheux, M.; Gilliet, N.; Hamon, Y.; Grimaldi, C.

2018-04-01

The response of stream chemistry to storm is of major interest for understanding the export of dissolved and particulate species from catchments. The related challenge is the identification of active hydrological flow paths during these events and of the sources of chemical elements for which these events are hot moments of exports. An original four-year data set that combines high frequency records of stream flow, turbidity, nitrate and dissolved organic carbon concentrations, and piezometric levels was used to characterize storm responses in a headwater agricultural catchment. The data set was used to test to which extend the shallow groundwater was impacting the variability of storm responses. A total of 177 events were described using a set of quantitative and functional descriptors related to precipitation, stream and groundwater pre-event status and event dynamics, and to the relative dynamics between water quality parameters and flow via hysteresis indices. This approach led to identify different types of response for each water quality parameter which occurrence can be quantified and related to the seasonal functioning of the catchment. This study demonstrates that high-frequency records of water quality are precious tools to study/unique in their ability to emphasize the variability of catchment storm responses.

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.

Dynamic phases of low-temperature low-current driven vortex matter in superconductors

International Nuclear Information System (INIS)

Benkraouda, M; Obaidat, I M; Khawaja, U Al; Mulaa, N M J

2006-01-01

Using molecular dynamics simulations of vortices in a high-temperature superconductor with square periodic arrays of pinning sites, dynamic phases of the low-current driven vortices are studied at low temperatures. A rough vortex phase diagram of three distinct regimes of vortex flow is proposed. At zero temperature, we obtain a coupled-channel regime where rows of vortices flow coherently in the direction of the driving force. As the temperature is increased, a smooth crossover into an uncoupled-channel regime occurs where the coherence between the flowing rows of vortices becomes weaker. Increasing the temperature further leads to a plastic vortex regime, where the channels of flowing vortices completely disappear. The temperatures of the crossovers between these regimes were found to decrease with the driving force

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.

Attribution of climate extreme events

Science.gov (United States)

Trenberth, Kevin E.; Fasullo, John T.; Shepherd, Theodore G.

2015-08-01

There is a tremendous desire to attribute causes to weather and climate events that is often challenging from a physical standpoint. Headlines attributing an event solely to either human-induced climate change or natural variability can be misleading when both are invariably in play. The conventional attribution framework struggles with dynamically driven extremes because of the small signal-to-noise ratios and often uncertain nature of the forced changes. Here, we suggest that a different framing is desirable, which asks why such extremes unfold the way they do. Specifically, we suggest that it is more useful to regard the extreme circulation regime or weather event as being largely unaffected by climate change, and question whether known changes in the climate system's thermodynamic state affected the impact of the particular event. Some examples briefly illustrated include 'snowmaggedon' in February 2010, superstorm Sandy in October 2012 and supertyphoon Haiyan in November 2013, and, in more detail, the Boulder floods of September 2013, all of which were influenced by high sea surface temperatures that had a discernible human component.

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.

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

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

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…

A necessary condition for dispersal driven growth of populations with discrete patch dynamics.

Science.gov (United States)

Guiver, Chris; Packman, David; Townley, Stuart

2017-07-07

We revisit the question of when can dispersal-induced coupling between discrete sink populations cause overall population growth? Such a phenomenon is called dispersal driven growth and provides a simple explanation of how dispersal can allow populations to persist across discrete, spatially heterogeneous, environments even when individual patches are adverse or unfavourable. For two classes of mathematical models, one linear and one non-linear, we provide necessary conditions for dispersal driven growth in terms of the non-existence of a common linear Lyapunov function, which we describe. Our approach draws heavily upon the underlying positive dynamical systems structure. Our results apply to both discrete- and continuous-time models. The theory is illustrated with examples and both biological and mathematical conclusions are drawn. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

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

Reliable fuzzy H∞ control for active suspension of in-wheel motor driven electric vehicles with dynamic damping

Science.gov (United States)

Shao, Xinxin; Naghdy, Fazel; Du, Haiping

2017-03-01

A fault-tolerant fuzzy H∞ control design approach for active suspension of in-wheel motor driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The controller is designed based on the quarter-car active suspension model with a dynamic-damping-in-wheel-motor-driven-system, in which the suspended motor is operated as a dynamic absorber. The Takagi-Sugeno (T-S) fuzzy model is used to model this suspension with possible sprung mass variation. The parallel-distributed compensation (PDC) scheme is deployed to derive a fault-tolerant fuzzy controller for the T-S fuzzy suspension model. In order to reduce the motor wear caused by the dynamic force transmitted to the in-wheel motor, the dynamic force is taken as an additional controlled output besides the traditional optimization objectives such as sprung mass acceleration, suspension deflection and actuator saturation. The H∞ performance of the proposed controller is derived as linear matrix inequalities (LMIs) comprising three equality constraints which are solved efficiently by means of MATLAB LMI Toolbox. The proposed controller is applied to an electric vehicle suspension and its effectiveness is demonstrated through computer simulation.

Are facial expressions of emotion produced by categorical affect programs or dynamically driven by appraisal?

Science.gov (United States)

Scherer, Klaus R; Ellgring, Heiner

2007-02-01

The different assumptions made by discrete and componential emotion theories about the nature of the facial expression of emotion and the underlying mechanisms are reviewed. Explicit and implicit predictions are derived from each model. It is argued that experimental expression-production paradigms rather than recognition studies are required to critically test these differential predictions. Data from a large-scale actor portrayal study are reported to demonstrate the utility of this approach. The frequencies with which 12 professional actors use major facial muscle actions individually and in combination to express 14 major emotions show little evidence for emotion-specific prototypical affect programs. Rather, the results encourage empirical investigation of componential emotion model predictions of dynamic configurations of appraisal-driven adaptive facial actions. (c) 2007 APA, all rights reserved.

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

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)

Data-Intensive Science Meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

Science.gov (United States)

Ramachandran, R.; Nair, U. S.; Word, A.

2014-12-01

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of data-intensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow us to analyze and fully utilize the complex and voluminous data that is being gathered. In this emerging paradigm, the scientific discovery process is driven by knowledge extracted from large volumes of data. In this presentation, we contend that this paradigm naturally lends to inquiry-driven pedagogy where knowledge is discovered through inductive engagement with large volumes of data rather than reached through traditional, deductive, hypothesis-driven analyses. In particular, data-intensive techniques married with an inductive methodology allow for exploration on a scale that is not possible in the traditional classroom with its typical

NPP unusual events: data, analysis and application

International Nuclear Information System (INIS)

Tolstykh, V.

1990-01-01

Subject of the paper are the IAEA cooperative patterns of unusual events data treatment and utilization of the operating safety experience feedback. The Incident Reporting System (IRS) and the Analysis of Safety Significant Event Team (ASSET) are discussed. The IRS methodology in collection, handling, assessment and dissemination of data on NPP unusual events (deviations, incidents and accidents) occurring during operations, surveillance and maintenance is outlined by the reports gathering and issuing practice, the experts assessment procedures and the parameters of the system. After 7 years of existence the IAEA-IRS contains over 1000 reports and receives 1.5-4% of the total information on unusual events. The author considers the reports only as detailed technical 'records' of events requiring assessment. The ASSET approaches implying an in-depth occurrences analysis directed towards level-1 PSA utilization are commented on. The experts evaluated root causes for the reported events and some trends are presented. Generally, internal events due to unexpected paths of water in the nuclear installations, occurrences related to the integrity of the primary heat transport systems, events associated with the engineered safety systems and events involving human factor represent the large groups deserving close attention. Personal recommendations on how to use the events related information use for NPP safety improvement are given. 2 tabs (R.Ts)

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

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.

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.

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…

Molecular Dynamics Simulations of a Linear Nanomotor Driven by Thermophoretic Forces

DEFF Research Database (Denmark)

Zambrano, Harvey A; Walther, Jens Honore; Jaffe, Richard L.