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A proper generalized decomposition strategy for dynamic data driven application systems

REVISTA INTERNACIONAL DE METODOS NUMERICOS PARA CALCULO Y DISENO EN INGENIERIA 2013年第2期29卷 104-113页

作者： Masson, F. Gonzalez, D. Cueto, E. Chinesta, F. Univ Zaragoza Aragon Inst Engn Res Zaragoza 50018 Spain Ecole Cent Nantes EADS Corp Int Chair Nantes France

dynamic data driven application systems (DDDAS) appear as a new paradigm in the field of applied sciences and engineering, and in particular in simulation-based engineering sciences. By DDDAS we mean a set of techniques that allow the linkage of simulation tools with measurement devices for real-time control of systems and processes. DDDAS entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process. DDDAS needs for accurate and fast simulation tools making use if possible of off-line computations for limiting as possible the on-line computations. We could define efficient solvers by introducing all the sources of variability as extra-coordinates in order to solve off-line only once the model to obtain its most general solution to be then considered in on-line purposes. However, such models result defined in highly multidimensional spaces suffering the so-called curse of dimensionality. We proposed recently a technique, the Proper Generalized Decomposition (PGD), able to circumvent the redoubtable curse of dimensionality. The marriage of DDDAS concepts and tools and PGD off-line computations could open unimaginable possibilities in the field of dynamics data-driven application systems. In this work we explore some possibilities in the context of parameter estimation. 2010 CIMNE (Universitat Politecnica de Catalunya). Published by Elsevier Espana, S.L. All rights reserved.

关键词： Proper generalized decomposition dynamic data driven application systems Real time Model order reduction

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Service-Oriented dynamic data driven application systems to Traffic Signal Control

Service-Oriented Dynamic Data Driven Application Systems to ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Lin, Szu-Yin Chao, Kuo-Ming Lo, Chi-Chun Natl Chiao Tung Univ Inst Informat Management Hsinchu Taiwan Coventry Univ Fac Comp & Engn Coventry West Midlands England

ISBN: (纸本)9781424465880

dynamic traffic signal control in Intelligent Transportation systems (ITS) has been widely developed in major cities' urban areas around the world to provide more efficient way for solving traffic congestion problem. However, the problems in dynamic traffic signal control systems such as system flexibility, data standard common interface, transmission of required information, prediction performance, and real-time measurement data are all important issues. Therefore, in this paper, we proposed a new framework to integrate dynamic data driven application systems (DDDAS) with service-oriented architecture (SOA) and web services technology, for a decentralized traffic signal control system. An efficient and effective distributed data-driven optimization algorithm is designed in this framework to produce an optimal result and best strategy for signal control.

关键词： Web Services Service-Oriented Architecture dynamic data driven application systems dynamic Traffic Signal Control

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Reinforcement learning-based prediction approach for distributed dynamic data-driven application systems

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INFORMATION TECHNOLOGY & MANAGEMENT 2015年第4期16卷 313-326页

作者： Lin, Szu-Yin Chung Yuan Christian Univ Jhongli Taiwan

With the current advances in cloud and distributed system technology, data have become ubiquitous and their dynamics has increased. It is an extreme challenge to find the interdependencies among distributed data in order to dynamically manage and predict the trend within large amounts of data sources. This paper proposes a new distributed dynamic data-driven model and strategy to direct and evaluate the interlinked data sets in dynamic data-driven application systems (DDDAS). The underlying technique involves the introduction of a reinforcement Q-Learning approach which includes search strategies to determine how to drill and drive a series of highly dependent data in order to enhance prediction accuracy and efficiency. It can tackle dynamic data issues in a real-time, dynamic and resource-bounded environment. The proposed framework is a comprehensive skeleton for modeling complex, flexible and dynamic tasks in a distributed environment for solving DDDAS problems. In simulation, the new model utilizes individual sensors, distributed databases and predictors in dynamic data Stream Nodes with multiple dimensional variables which can be instantiated to explore the search space, thereby improving the search convergence. This study shows the effectiveness and applicability of using the technique in the analysis of typhoon rainfall data. The result shows that the proposed approach performed better than traditional linear regression approaches, reducing the error rate by 36.34 %.

关键词： dynamic data driven application systems Reinforcement learning Distributed computing

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Building a dynamic data driven application system for hurricane forecasting

Building a dynamic data driven application system for hurric...

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7th International Conference on Computational Science (ICCS 2007)

作者： Allen, Gabrielle Louisiana State Univ Ctr Comp & Technol Baton Rouge LA 70803 USA

ISBN: (纸本)9783540725831

The Louisiana Coastal Area presents an array of rich and urgent scientific problems that require new computational approaches. These problems are interconnected with common components: hurricane activity is aggravated by ongoing wetland erosion;water circulation models are used in hurricane forecasts, ecological planning and emergency response;environmental sensors provide information for models of different processes with varying spatial and time scales. This has prompted programs to build an integrated, comprehensive, computational framework for meteorological, coastal, and ecological models. dynamic and adaptive capabilities are crucially important for such a framework, providing the ability to integrate coupled models with real-time sensor information, or to enable deadline based scenarios and emergency decision control systems. This paper describes the ongoing development of a dynamic data driven application System for coastal and environmental applications (DynaCode), highlighting the challenges of providing accurate and timely forecasts for hurricane events.

关键词： dynamic data driven application systems DDDAS hurricane forecasting event driven computing priority computing coastal modeling computational frameworks

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An Introduction to a Porous Shape Memory Alloy dynamic data driven application System

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Procedia Computer Science 2012年 9卷 1081-1089页

作者： Craig C. Douglas Yalchin Efendiev Peter Popov Victor M. Calo Univerity of Wyoming School of Energy Resources and Mathematics Department Laramie WY 82072-3036 USA Texas A&M University Mathematics Department College Station TX 77843-3368 USA King Abdullah University of Science & Technology Thuwal Saudi Arabia

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

关键词： dynamic data driven application systems DDDAS SMAs homogenization modeling data assimilation

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An Intelligent Controlling System for Greenhouse Environment Based on the Architecture of the Internet of Things

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SENSOR LETTERS 2012年第1-2期10卷 514-522页

作者： Luo, Hao Yang, Peiling Li, Yunkai Xu, Feipeng China Agr Univ Coll Hydraul & Civil Engn Beijing 100083 Peoples R China

Greenhouse farming possesses substantial potential in high-efficiency use of water, land and energy in food production. It is a critical issue to develop the methodology for better environment control of greenhouses. This paper proposes an intelligent controlling system for greenhouses based on the architecture of the Internet of Things. The wireless sensor nodes and relay nodes are designed associating the physical world with the information world. Using 3G or TCP/IP network as the channel of the network of the Internet of Things, users can completely control the systems, treat and analyze data via browsers in any place and at any time without need for installing any special device or software. The system carries out the paradigm of the dynamic data driven application System enabling the simulation to receive the continuous data injected from the perception layer of the system and to dynamically make response. The faster-than-real-time simulation is implemented based on the established self-adaptation model. The factors about the environments of crop growing are predicted and evaluated. At the same time, the results produced by the simulation are fed back to the system software to control the actions of executive devices in the real-time mode forming a closed controlling loop. The management function of the system software allows to flexibly set the executive devises of the greenhouses and to establish the completely new controlling rules with high degree of freedom. Consequently, the software system combined with the expert system can adapt to various greenhouses in different types and sizes.

关键词： Internet of Things dynamic data driven application systems Wireless Sensor Network Intelligent Greenhouse Zigbee

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Decentralized data-driven Control of Cooperating Sensor-Carrying UAVs in a Multi-Objective Monitoring Scenario

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IFAC-PapersOnLine 2017年第1期50卷 15828-15834页

作者： Euler, Juliane Stryk, Oskar von Technische Universität Darmstadt Germany

For estimating atmospheric dispersion of harmful material, the use of multiple sensor-equipped UAVs for information gathering offers great flexibility, but requires an efficient adaptive sampling strategy that exploits multi-vehicle cooperation. For this purpose, a novel decentralized data-driven online control scheme for cooperating vehicles in multi-objective monitoring scenarios is presented in this paper. In the considered use case, multiple UAVs are to adaptively gather measurements for estimating the parameters of an atmospheric dispersion model. At the same time, they are required to cooperatively patrol predefined checkpoints. Vehicle-specific optimal waypoints for each UAV are determined by sequential optimum design. Following these waypoints leads to a maximized information gain of the acquired measurements, such that the parameter estimate is iteratively improved. On the other hand, checkpoint allocation as well as trajectory planning is provided by a decentralized model-predictive controller based on a discrete-time mixed-integer linear problem formulation. By permanent interaction of parameter estimation, waypoint calculation, and cooperative control, a fully optimization-based, yet efficient and adaptive feedback control approach is obtained. Simulations successfully demonstrate its effectiveness. © 2017

关键词： Feedback Actuators Distributed parameter control systems Hybrid systems Iterative methods Model predictive control Parameter estimation Vehicles Adaptive feedback control Adaptive sampling strategies Atmospheric dispersion modeling Decentralized Distributed control dynamic data driven application systems Multi vehicle cooperation Multi vehicle systems

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Resilient Machine Learning (rML) Against Adversarial Attacks on Industrial Control systems 20

Resilient Machine Learning (rML) Against Adversarial Attacks...

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20th ACS/IEEE International Conference on Computer systems and applications (AICCSA)

作者： Yao, Likai Shao, Sicong Hariri, Salim Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA Univ North Dakota Sch Elect Engn & Comp Sci Grand Forks ND 58202 USA

ISBN: (纸本)9798350319439

Machine learning (ML) algorithms have been widely used in many critical automated systems, including as a technique in dynamic data driven applications systems (DDDAS)-based methods and areas such as financial trading, autonomous vehicles, and intrusion detection systems. However, malicious adversaries have strong interests in manipulating the operations of machine learning algorithms to achieve their objectives of gaining financial, social, or political influence. Adversarial ML (AML) users can be classified based on their capabilities and goals into three types: Adversary who has full knowledge of the ML models and parameters (white-box scenario), partial knowledge of ML models (gray-box scenario), and one who does not have any knowledge and uses guessing techniques to figure out the ML model and its parameters (black-box scenario). In these scenarios, the adversaries attempt to maliciously manipulate the model/data either during training or testing. Defending against these AML attacks can be successful by following methods such as making the ML model robust, validating and verifying inputs and outputs, and changing the ML architecture. This paper presents a resilient machine learning (rML) against adversarial attacks by dynamically conducting feature space anonymization and model randomization in ML services such that the adversaries lack knowledge about the feature space and model used and consequently prevent them from maliciously manipulating the ML operations during the runtime. In our approach, the rML utilizes autoencoders as an anonymization technique for encoding feature space to minimize the effect of adversarial samples. The rML method is evaluated using the benchmarking Industrial Control systems (ICS) data and the corresponding adversarial data generated using the Jacobian-based Saliency Map Attack (JSMA) method. The experiment demonstrated that the proposed approach can detect attacks targeting ICS and prevent adversarial attacks compromising ML mode

关键词： dynamic data driven application systems resiliency adversarial machine learning moving target defense resilient decision support Anonymization

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An Ensemble Kalman-Particle Predictor-Corrector Filter for Non-Gaussian data Assimilation

An Ensemble Kalman-Particle Predictor-Corrector Filter for N...

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9th International Conference on Computational Science

作者： Mandel, Jan Beezley, Jonathan D. Univ Colorado Denver CO 80217 USA

ISBN: (纸本)9783642019722

An Ensemble Kalman Filter (EnKF, the predictor) is used make a large change in the state, followed by a Particle Filer (PF, the Corrector), which assigns importance weights to describe a non-Gaussian distribution. The importance weights are obtained by nonparametric density estimation. It is demonstrated on several numerical examples that the new predictor-corrector filter combines the advantages of the EnKF and the PF and that it is suitable for high dimensional states which are discretizations of solutions of partial differential equations.

关键词： dynamic data driven application systems data assimilation ensemble Kalman filter particle filter tracking non-parametric density estimation Bayesian statistics

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Global Challenge Governance: Time for Big Modelling? 18

Global Challenge Governance: Time for Big Modelling?

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18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Toth, Tibor Theodoropoulos, Georgios Boland, Samuel Kureshi, Ibad Ghandar, Adam Comprehens Nucl Test Ban Treaty Org PC Vienna Austria Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China London Sch Hyg & Trop Med Dept Global Hlth & Dev London England Inlecom Syst Brussels Belgium

ISBN: (纸本)9781728114194

Global emergencies such as epidemics present immense governance challenges to national, political and operational decision-makers. Modelling and Simulation has been identified as a crucial force multiplier in the development and implementation of preparedness and response measures for epidemics and pandemics outbreaks. Recent years have witnessed an explosion in modelling and simulation tools for this domain while emerging technologies such as IoT and remote sensing enable data collection as an unprecedented scale. However fragmentation and siloing of these efforts hamper their effectiveness. This paper argues that the complexity and scale of the challenge calls for an integrated "Big Modelling" approach which would bring all the different elements together to enable a holistic view and analysis and outlines a computation framework that can act as a catalyst in this direction.

关键词： disaster management epidemics global governance agent based models dynamic data driven application systems distributed simulation

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