Dynamic Data Driven Applications System Concept for Information Fusion

AbstractWe present a framework of Information Fusion (IF) using the Dynamic Data Driven Applications Systems (DDDAS) concept. Existing literature at the intersection of these two topics supports environmental modeling (e.g., terrain understanding) for context enhanced applications. Taking advantage of sensor models, statistical methods, and situation- specific spatio-temporal fusion products derived from wide area sensor networks, DDDAS demonstrates robust multi-scale and multi-resolution geographical terrain computations. We highlight the complementary nature of these seemingly parallel approaches and propose a more integrated analytical framework in the context of a cooperative multimodal sensing application. In particular, we use a Wide-Area Motion Imagery (WAMI) application to draw parallels and contrasts between IF and DDDAS systems that warrants an integrated perspective. This elementary work is aimed at triggering a sequence of deeper insightful research towards exploiting sparsely sampled piecewise dense WAMI measurements – an application where the challenges of big-data with regards to mathematical fusion relationships and high-performance computations remain significant and will persist. Dynamic data-driven adaptive computations are required to effectively handle the challenges with exponentially increasing data volume for advanced information fusion systems solutions such as simultaneous target tracking and identification

Desarrollo de aplicaciones paralelo/distribuidas orientadas a la predicción de incendios forestales

La problemática existente a raíz de la falta de exactitud presente en los parámetros de entrada en cualquier modelo científico o físico, puede producir consecuencias dramáticas en la salida del mismo si se trata éste de algún sistema crítico. Además, al citado problema deben sumarse las limitaciones impuestas por los propios modelos, las restricciones que agregan las soluciones numéricas y, por qué no, las provenientes de las propias implementaciones y versiones informáticas. Por tal motivo, resulta de gran interés el desarrollo de métodos y herramientas informáticos que se enfoquen en el tratamiento de la incertidumbre de los valores de entrada para lograr así una predicción lo más confiable posible por parte del modelo en cuestión. En el caso concreto de los incendios forestales, la simulación de la propagación constituye un desafío desde el punto de vista computacional, dada la complejidad que involucran los modelos, los métodos numéricos y la administración de los recursos. La clase de métodos que aborda nuestra línea de investigación constituye una importante herramienta para la prevención y predicción, dado que provee información acerca del posible comportamiento del fuego y las zonas que corren mayor peligro.Eje: Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informática (RedUNCI

A Dynamic Data Driven Application System for Vehicle Tracking

AbstractTracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle's movements by repeatedly identifying the vehicle under investigation from live image and video data, predicting probable future locations, and repositioning sensors or retargeting requests for information in order to reacquire the vehicle. An overview of the envisioned system is described that includes image processing algorithms to detect and recapture the vehicle from live image data, a computational framework to predict probable vehicle locations at future points in time, and a power aware data distribution management system to disseminate data and requests for information over ad hoc wireless communication networks. A testbed under development in the midtown area of Atlanta, Georgia in the United States is briefly described

A wildland fire model with data assimilation

A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients that can be approximated from prior measurements of wildfires. An ensemble Kalman filter technique with regularization is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one.Comment: 35 pages, 12 figures; minor revision January 2008. Original version available from http://www-math.cudenver.edu/ccm/report

Método de Reducción de Incertidumbre basado en HPC

La problemática existente a raíz de la falta de exactitud que se encuentra en los parámetros de entrada en cualquier modelo científi co o físico, puede producir graves consecuencias en la salida del mismo si éste se trata de algún sistema crítico. Además, al citado problema deben sumarse las limitaciones impuestas por los propios modelos, las restricciones que agregan las soluciones numéricas y, por qué no, las provenientes de las propias implementaciones y versiones informáticas. Por tal motivo, resulta de gran interés el desarrollo de métodos informáticos que se enfoquen en el tratamiento de la incertidumbre de dichos valores de entrada para lograr así una predicción lo más confi able posible por parte del modelo en cuestión. En el presente trabajo se presenta un método basado en High Performance Computing en combinación con Cálculo Estadístico, el cual se ha evaluado y veri cado en casos reales aplicándolo a un modelo de comportamiento de incendios forestales.Presentado en el IX Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

DATA ASSIMILATION AND VISUALIZATION FOR ENSEMBLE WILDLAND FIRE MODELS

This thesis describes an observation function for a dynamic data driven application system designed to produce short range forecasts of the behavior of a wildland fire. The thesis presents an overview of the atmosphere-fire model, which models the complex interactions between the fire and the surrounding weather and the data assimilation module which is responsible for assimilating sensor information into the model. Observation plays an important role in data assimilation as it is used to estimate the model variables at the sensor locations. Also described is the implementation of a portable and user friendly visualization tool which displays the locations of wildfires in the Google Earth virtual globe

Data Assimilation for Spatial Temporal Simulations Using Localized Particle Filtering

As sensor data becomes more and more available, there is an increasing interest in assimilating real time sensor data into spatial temporal simulations to achieve more accurate simulation or prediction results. Particle Filters (PFs), also known as Sequential Monte Carlo methods, hold great promise in this area as they use Bayesian inference and stochastic sampling techniques to recursively estimate the states of dynamic systems from some given observations. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. As the state space dimension increases, the number of particles must increase exponentially in order to converge to the true system state. The purpose of this dissertation work is to develop localized particle filtering to support PFs-based data assimilation for large-scale spatial temporal simulations. We develop a spatially dependent particle-filtering framework that breaks the system state and observation data into sub-regions and then carries out localized particle filtering based on these spatial regions. The developed framework exploits the spatial locality property of system state and observation data, and employs the divide-and-conquer principle to reduce state dimension and data complexity. Within this framework, we propose a two-level automated spatial partitioning method to provide optimized and balanced spatial partitions with less boundary sensors. We also consider different types of data to effectively support data assimilation for spatial temporal simulations. These data include both hard data, which are measurements from physical devices, and soft data, which are information from messages, reports, and social network. The developed framework and methods are applied to large-scale wildfire spread simulations and achieved improved results. Furthermore, we compare the proposed framework to existing particle filtering based data assimilation frameworks and evaluate the performance for each of them

A Framework For Process Data Collection, Analysis, And Visualization In Construction Projects

Automated data collection, simulation and visualization can substantially enhance the process of designing, analysis, planning, and control of many engineering processes. In particular, managing processes that are dynamic in nature can significantly benefit from such techniques. Construction projects are good examples of such processes where a variety of equipment and resources constantly interact inside an evolving environment. Management of such settings requires a platform capable of providing decision-makers with updated information about the status of project entities and assisting site personnel making critical decisions under uncertainty. To this end, the current practice of using historical data or expert judgments as static inputs to create empirical formulations, bar chart schedules, and simulation networks to study project activities, resource operations, and the environment under which a project is taking place does not seem to offer reliable results. The presented research investigates the requirements and applicability of a data-driven modeling framework capable of collecting and analyzing real time field data from construction equipment. In the developed data collection scheme, a stream of real time data is continuously transferred to a data analysis module to calculate the input parameters required to create dynamic 3D visualizations of ongoing engineering activities, and update the contents of a discrete event simulation (DES) model representing the real engineering process. The generated data-driven simulation model is iv an effective tool for projecting future progress based on existing performance. Ultimately, the developed framework can be used by project decision-makers for shortterm project planning and control since the resulting simulation and visualization are completely based on the latest status of project entities