Introduction to Dynamic Data Driven Applications Systems Erik Blasch 1, Dennis Bernstein 2 , Murali Rangaswamy 3 1 Air Force Office of Scientific Research, Arlington, VA, USA 2 Dept. of Aerospace Eng., University of Michigan, Ann Arbor, MI, USA 3 Air Force Research Laboratory, WPAFB, OH, USA {erik.blasch.1, murali.rangaswamy}@us.af.mil, [email protected]Abstract. Dynamic Data Driven Application Systems (DDDAS) is a systems design framework that focuses on developments that incorporate high-dimensional physical models, run-time measurements, statistical methods, and computation architectures. One of the foremost applications of DDDAS successes was environmental assessment of natural disasters such as wild fire monitoring and volcanic plume detection. Monitoring the atmosphere with DDDAS principles has evolved into applications for space situational awareness, unmanned aerial vehicle (UAV) design, and biomedical applications. Recent efforts reflect the digital age of information management such as multimedia analysis, power grid control, and biohealth concerns. Underlying a majority of the DDDAS developments are advances in sensor design, signal processing and filtering, as well as computational architectures. The book highlights some of these advances for the reader, with more information available at the DDDAS society’s website: www.1dddas.org . Keywords: Dynamic Data Driven Application Systems 1
Transcript
Introduction to Dynamic Data Driven Applications Systems
Erik Blasch 1, Dennis Bernstein 2, Murali Rangaswamy3
1 Air Force Office of Scientific Research, Arlington, VA, USA2 Dept. of Aerospace Eng., University of Michigan, Ann Arbor, MI, USA
3 Air Force Research Laboratory, WPAFB, OH, USA{erik.blasch.1, murali.rangaswamy}@us.af.mil, [email protected]
Abstract. Dynamic Data Driven Application Systems (DDDAS) is a systems design framework that focuses on developments that incorporate high-dimensional physical models, run-time measurements, statistical methods, and computation architectures. One of the foremost applications of DDDAS successes was environmental assessment of natural disasters such as wild fire monitoring and volcanic plume detection. Monitoring the atmosphere with DDDAS principles has evolved into applications for space situational awareness, unmanned aerial vehicle (UAV) design, and biomedical applications. Recent efforts reflect the digital age of information management such as multimedia analysis, power grid control, and biohealth concerns. Underlying a majority of the DDDAS developments are advances in sensor design, signal processing and filtering, as well as computational architectures. The book highlights some of these advances for the reader, with more information available at the DDDAS society’s website: www.1dddas.org.
Keywords: Dynamic Data Driven Application Systems
1 Introduction
The methods in the book capture the essence of DDDAS systems design. Invariably, the DDDAS framework from Dr. Frederica Darema inspires many researchers for engineering and science advances.
As articulated by Dr. Darema who pioneered the DDDAS paradigm [, 2]:
“in DDDAS instrumentation data and executing application models of these systems become a dynamic feedback control loop, whereby measurement data are dynamically incorporated into an executing model of the system in order to improve the accuracy of the model (or simulation), or to speed-up the simulation, and in reverse the executing application model controls the instrumentation process to guide the measurement process. DDDAS presents opportunities to create new capabilities through more accurate
understanding, analysis, and prediction of the behavior of complex systems, be they natural, engineered, or societal, and to create decision support methods which can have the accuracy of full-scale simulations, as well as to create more efficient and effective instrumentation methods, such as intelligent management of Big Data, and dynamic and adaptive management of networked collections of heterogeneous sensors and controllers. DDDAS is a unifying paradigm, bringing together computational and instrumentation aspects of an application system, which extends the notion of Big Computing to span from the high-end to the real-time data acquisition and control, and it’s a key methodology in managing and intelligently exploiting Big Data.”
DDDAS (Dynamic Data Driven Applications Systems), beginning in 1998 [3], is a paradigm in which computation and instrumentation aspects of an application system are dynamically integrated in a feedback control loop, in the sense that instrumentation data can be dynamically incorporated into the executing model of the application, and in reverse the executing model can control the instrumentation [4]. Such approaches have shown to enable more accurate and faster modeling and analysis of the characteristics and behaviors of a system. Methods based on the DDDAS paradigm can exploit data in intelligent ways to provide new capabilities, including decision support systems with the accuracy of full-scale modeling, efficient data collection, resource management, and data mining.
The DDDAS paradigm, and opportunities and challenges in exploiting the DDDAS paradigm have been discussed in a series of workshops, starting in 2000 from the National Science Foundation (NSF) [4]. The reports from these workshops, identified new science and technology capabilities, inspired by and enabled through the DDDAS paradigm. New capabilities include modeling approaches, algorithm developments, systems software, and instrumentation methods, and well as the need for synergistic multidisciplinary research among these areas [5]. DDDAS brings together practitioners of application domains, researchers in mathematics, statistics, electrical engineering, and computer sciences, as well as well as designers involved in the development of instrumentation systems and methods. Through a series of workshops, research efforts commenced to address the challenges and create new frontiers. As shown through the increasing body of work, DDDAS is applicable to many areas: such as (1) engineering: aerospace, biomedical, civil, electrical and mechanical engineering, (2) systems: manufacturing, transportation, and energy design, (3) science: environmental, weather, and climate science, as well as (4) decision support: medical diagnosis and treatment, multimedia analysis, and cyber security evaluation. This book presents examples of advances through DDDAS to motivate future developers interested in the DDDAS paradigm.
The rest of the chapter helps the reader better understand DDDAS paradigm. Section 2 discusses the aspects of DDDAS. Section 3 highlights the methods of estimation and assimilation for processing data. Section 4 includes DDDAS methods. Section 5 provides a review of the major areas where DDDAS has been applied in the last 20 years. Section 6 concludes with an overview of the book.
2 What is DDDAS?
Consider an approaching hurricane. A meteorological model of the storm can be constructed, but this has limited predictive value without knowledge of initial conditions, boundary conditions, inputs, parameters, and states (such as velocities and accelerations). In order to make predictions, data is needed to estimate unknown quantities. Although the storm can be imaged at low resolution by satellite, measurements by aircraft with high resolution are expensive and limited in range, and therefore the size of the storm makes it impossible to obtain detailed measurements over a large area.
In a scenario of this type, it may be possible to use the model to guide and reconfigure the sensors so that the information content of the data is enhanced for the ultimate objective of predicting the path and intensity of the storm. At the same time, the data collected by the sensors enhances the accuracy of the model by providing estimates of initial conditions, boundary conditions, inputs, parameters, and states. The integration of on-line data with the off-line model creates a positive feedback loop, where the model judiciously guides the sensor selection, sensor data collection, from which the sensor data improves the accuracy of the model.
The hurricane example illustrates the essence of Dynamic Data-Driven Application Systems (DDDAS). DDDAS is a conceptual framework that synergistically combines models and data in order to facilitate the analysis and prediction of physical phenomena. In a broader context, DDDAS is a variation of adaptive state estimation that uses a sensor reconfiguration loop as shown in Figure 1 [6]. This loop seeks to reconfigure the sensors in order to enhance the information content of the measurements. The sensor reconfiguration is guided by the simulation of the physical process. Consequently, the sensor reconfiguration is dynamic, and the overall process is data driven.
Fig. 1. Dynamic Data-Driven Application Systems (DDDAS) feedback loop.
The core of DDDAS is the data assimilation loop, which uses sensor data error to drive the physical system simulation so that the trajectory of the simulation more closely follows the trajectory of the physical system. The data assimilation loop uses
input data if input sensors are available. The innovative feature of DDDAS is the additional sensor reconfiguration loop, which guides the physical sensors in order to enhance the information content of the collected data. The data assimilation and sensor reconfiguration feedback loops are computational rather than physical feedback loops. The simulation guides the sensor reconfiguration and the collected data, and in turn, improves the accuracy of the physical system simulation. This “meta” positive feedback loop is the essence of DDDAS.
Key aspects of DDDAS include the algorithmic and statistical methods that incorporate the measurement data with that of the high-fidelity modeling and simulation.
3 State Estimation and Data Assimilation
The goal of state estimation is to combine models with data in order to estimate model states that are not directly measured. State estimation is a foundational area of research in systems and control. Relevant techniques date from the 1960’s in the form of the Kalman filter and the Luenberger observer. An observer is a model that emulates the dynamics of a physical system and is driven by sensor data in order to approximate unmeasured states. The Kalman filter is a stochastically optimal observer that estimates unmeasured states. In large-scale physics applications, such as applications involving structures or fluids, state estimation is called data assimilation.
The Kalman filter was developed for linear systems. However, most real applications involve nonlinear dynamics, and the development of observers and filters for nonlinear systems is a challenging problem that remains largely unsolved. Numerous techniques, which can be described as suboptimal, ad hoc, application-based, or approximate, have been developed, and many of these methods are widely used. These techniques include the extended Kalman filter (KF), ensemble Kalman filter (EKF), ensemble adjustment Kalman filter (EnAKF), unscented Kalman filter (UKF), stochastic integration filter (SIF), and particle filters (PF) [7, 8].
3.1 DDDAS and Adaptive State Estimation
State estimation algorithms are based on prior information about the physical system [9]. The information typically includes a model of the physical system as well as knowledge of the initial state, inputs (such as disturbances), and sensor noise. Likewise, stochastic representation, for example, as a statistical description of the disturbances and sensor noise, is one method to process the information. An adaptive state estimation algorithm may attempt to learn and update the information, states, and parameters online.
DDDAS uses adaptation in a different sense. In particular, DDDAS seeks to reconfigure the sensors during operation. Sensor reconfiguration, driven by the model, enhances the information content of the measurements. The sensor reconfiguration loop is shown in Figure 1. Together, the integration of the data assimilation loop and the sensor reconfiguration loop are central to methods using DDDAS.
3.2 Does DDDAS Use Feedback Control?
DDDAS uses computational feedback, but not physical feedback. As Figure 1 shows, state estimation is a feedback process, where the sensor error corrects the simulation of the physical system. The data assimilation feedback loop is implemented in computation, and thus has no effect on the physical system.
DDDAS employs an additional feedback loop by reconfiguring the sensors based on the sensor error data. The sensor reconfiguration feedback loop is also computational, and thus does not affect the response of the physical system. In contrast, feedback control uses physical inputs (such as forces and moments) in order to affect the behavior of a physical system, such as an aircraft autopilot that drives the control surfaces and modifies the aircraft trajectory. Consequently, DDDAS employs two computational feedback loops, but does not use only use physical feedback control. The power of DDDAS to use simulated data from a high-dimensional model to augment measurement systems for systems design to leverage statistical methods, simulation, and computation architectures.
4 DDDAS Methods
The DDDAS framework, as it name implies, has been applied to many applications where modeling and data collection are utilized in engineering and scientific analysis. Hence, four attributes of DDDAS include: (1) instrumentation methods, (2) real-world applications, (3) modeling and simulation, and (4) systems software , as shown in Figure 2.
Instrumentation methods include multidomain components in real-world situations such as space sensors monitoring the atmosphere; avionics sensors detected the air movements, computer vision detecting vehicles on a terrain road network, as well as, water properties in the ocean. Complementing the application is high-fidelity simulation models such as the space Global ionosphere–thermosphere model (GITM) model, the National Climate Atmospheric Reference (NCAR) model, ground-based vehicle traffic models, and oceanic radar scatter models. Together the integration of the modeling and data collection requires software systems to process the large data sources and model parameters. The coordination of high-end with real-time computing requires new hardware and software approaches in the fields of optimization, data flow, and architectures to being together modeling and instrumentation methods for real world applications.
The key developments of the integration of the instrumentation, models, and software to enable the development of DDDAS include: theory, algorithms, and computation for which the book seeks to highlight. The theory includes mathematical advances (retrospective cost modeling – check); while the algorithms support new methods (e.g., ensemble Kalman filter, Particle filter, optimization techniques). The computational considerations align with the developments in the continuing networked society such as non-convex optimization, data flow architectures, and systems design.
Fig. 3. DDDAS Challenges and processes.
The challenges DDDAS seeks to advance include data modeling, context processing, and content application. To bring together data, context and content requires addressing issues in model fidelity, dimensions, and usability such as how many parameters are needed for system control. When data is collected, it needs to be preprocessed to determine whether its inherent information matches the context. One example includes clutter reduction, sensor registration, and confuser analysis in vehicle tracking. Finally, another key challenge is that of sampling, as shown in Figure 3. Sampling is the multiresolution needed to monitor the situation, environment and network context to explain the content desired.
Three examples are presented in Figure 4 which demonstrates DDDAS methods applied to enhance awareness. The examples are air, space, and cyber examples where
instrumentation, modeling, and software have been designed for real platforms. On the left is weather modeling with nonlinear tracking methods for unmanned aerial vehicle (UAV) flight routing. The middle includes multi-domain robotics of space and ground vehicles with filtering methods for distributed autonomous coordinated control. Finally, the cyber example comes from power grids performance that integrates cyber physical systems (CPS) with the internet of things (IoT).
Fig. 4. DDDAS Awareness Examples
5 DDDAS Research Areas of Historical Development
The concepts for DDDAS have developed for almost two decades starting with an initial NSF workshop in 2000 that brought together researchers, engineers, scientists and developers. The initial workshop focused on harnessing the power of theory, modeling, sensing, and hardware advances to instantiate systems-level opportunities. The explosion of DDDAS is demonstrated in the literature, as shown in Figure 5. The statistics from Figure 5 only capture those papers that call out DDDAS as the underlying paradigm; while many other papers have briefly acknowledged DDDAS are not included in Figure 5. There is a growing trend in approaches using DDDAS, which is established through the website.
2002 2004 2006 2008 2010 2012 2014 2016 201805
10152025303540
Year
DDDA
S Pa
pers
Per
Yea
r
Fig. 5. DDDAS Papers per Year.
Many forums have provided opportunities for showcasing advances in DDDAS. The primary meetings that highlighted the advances include:
• IEEE International Parallel and Distributed Processing Symposium (IPDPS) [10];• International Conference on Computational Science (ICCS) [11]; and• Winter Simulation Conference (WSC) [12].
The opportunities have expanded into engineering conferences:
• IEEE American Controls Conference (ACC) [13];• ISIF International Conference on Information Fusion (Fusion) [14]; and• AIAA Aviation [15].
Other science forums include: Data Stream (STREAM), American Geophysical Union (AGU), and Society for Industrial and Applied Mathematics (SIAM).
Along the way, there have been countless meetings and workshops with the first archive being the Dynamic Data-driven Environmental Systems Science Conference (DyDESS) (2014). DyDESS focused on scientific methods such as (1) Perspectives from Ocean State Estimation, (b) Imaging Earth's interior with active and passive source seismic data, (3) Objective Detection of Lagrangian Vortices in Unsteady Velocity Data, and (4) Data Assimilation and Controls for atmospheric mutiscale dimensional processing. The DDDAS/InfoSymbiotics conference (2016) is the genesis of this book.
Over the years, many researchers have embraced the DDDAS concept with a variety of applications as shown in Figure 6. Areas of interest shown in the illustrations include data assimilation, UAV swarms, decision support, simulations, and wildfire analysis; among others. The DDDAS community is dedicated to showcasing scientific and technological advances in complex systems modeling and instrumentation methods. The next section organizes many of the papers in the last 20 years into the areas of theory, methods, and design.
The history of DDDAS extends from two decades of developments. To organize the diverse set of applications, we highlight three areas: (1) theory, (2) methods, and (3) designs. Key areas for theory are based in the scientific areas with large data collections and complex models. Methods include various engineering designs for various domains – space, air, and ground, where DDDAS supports dynamic response and control. Finally, examples are presented that include elements needed to support applications that require systems design and computational architectures. Given the large size of the DDDAS literature, various taxonomies could be highlighted; however, the organization is an effort to provide the reader with the wide-ranging influence the DDDAS paradigm has had on the scientific, development, and design communities.
Atlantic Dr.
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W. Peachtree St.
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2-20 years 1 -10 years 1 week -2 years Minut es - 1 week Time frame
La ye r 3 : D ata co mmu nica tio n an d int eg ration
La ye r 4 : D ata proces sin g an d transf ormation
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Desi red m aintenance & ins pec tion frequenc y
Facility planning
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planning
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La yer 5 : Simula ti on & d eci sion
Infor mation valuation
Laye r 2 :
Con d iti on sens ors
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Qu ant umCh emist ry
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M anuf actu ri ngSyst em s
M ili ta ryLogi sti cs
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Rea li ty
Vi rt ualPr ot otyp es
Com pu tat ion alSt eer ing
Sci ent if ic Vi suali zat ion
M ult im edi aCol lab ora ti onTools
CA D
Geno mePro ces si ng
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La rge- s cal eDat a Min ing
Int el li gentAgen ts
Int el li gentSear ch
Cry pt og rap hy
Num be r Theo ry
Ecosy stem sEcon omi csM odels
Ast r ophysi cs
Si gnalPr ocessi ng
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Di ff r act ion & Inv ersi onPr obl em s
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Phyl ogene ti c T reesCr yst all ogr aphyTom ogr aphi cRec onst ruc ti on
Che mi calRea ctor s
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Sour ce: Ric k Stev ens , Argonne Nat ional Lab and The Uni ver si ty o f Chi c ago
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Fig. 6. DDDAS results (From: Report of the August 2010 Multi-Agency Workshop on Info/Symbiotics/DDDAS: The power of Dynamic Data Driven Application Systems, AFOSR, 2010.)
5.1 Theory – Modeling and Analysis
The DDDAS paradigm began with enhancing the phenomenology of science models such that measurement information would enhance the resulting model. In 2003, key attributes included measurement information, data assimilation, and adaptive sampling incorporated into multiphysics [16], ocean forecasting [17], and atmospheric modeling [18]. An application that benefited from the DDDAS principles using science models was oil well placement [19].
As the DDDAS methods showed promise in science applications, a key area was in weather forecasting [20]. Researchers assessed tornado prediction [21], climate analysis [22], and chemical transport models [23]. Simultaneously, DDDAS began addressing theoretical uncertainty and quantifying error minimization [24]. Years later, Ravela, et al. [25] and others began to use the information from weather forecasting (e.g., coherent fluid analysis) for advances in applications controls for UAVs and aircraft routing.
Along with weather forecasting was another related application for wildfire monitoring such as agent-based simulations for fire propagation modeling [26], which is still valid today. A set of researchers, lead by Coen [27], continued to use the DDDAS paradigm for inclusion of advanced physical models of wildfire prediction with that of real-time sensing. Within the CAWFE® (Coupled Atmosphere-Wildland Fire Environment) modeling system, various sensors such as the Visible Infrared Imaging Radiometer Suite (VIIRS), provided analysis of smoke plume detection [28] in the United States. The wildfire assessment method was extended to other geographic locations such as Europe [29]. Furthermore, fire detection and mitigation sought to understand the management of water distribution [30].
A recent example is that of volcanic ash detection by Bursik and Singla, et al. [31]. Atmospheric analysis can have impacts on commercial air transport, such as the recent eruption in Iceland. The particulates in the air from the eruption could have disastrous effects on combustion engines moving an aircraft through the sky. Likewise, with the detection of changes in the weather content, environmental wind context, and navigational data could be used to alter the air traffic management of the networked skies. Advances in uncertainty quantification were incorporated into the ash movement modeling so as to prepare aviation for future events and provide passenger safety [32]. Uncertainty quantification helps in estimate error reduction in complex modeling and estimation methods [33].
Science applications also include areas for bio-sensing and analysis for medical applications. One example is using image recognition for tracking human responses to stress and expressions. Metaxas, et al. [34] developed DDDAS methods using image recognition and face tracking [35]. Other examples include using the sensing to update models of humans in support of neurosurgery [36]. As a third example, Oden, et al., [37, 38] utilized DDDAS principles for laser treatment of cancer. In each of these cases, DDDAS supported enhancements in medical treatment through advanced modeling. Further DDDAS developments in this book include diagnostics, chemical treatment, and pandemics.
5.2 Methods – Domain Applications
Building upon the DDDAS principles for science applications influenced another area of development which moved from data assimilation analysis to that of control and filtering. As highlighted earlier, an extension of the scientific modeling of the air environment was extended to the atmospheric environment for orbital awareness. Bernstein, et al., [39] utilized the DDDAS principles for data assimilation using the global ionosphere-thermosphere model (GITM). While it was a scientific analysis, it moved the DDDAS community towards adaptive control and sensing. Simulations were conducted to determine the effects on planetary motion [40] and movement of atmospheric elements [41]. A third example extends these developments for the Retrospective Cost Model Refinement (RCMR) that includes modeling, sensing and control [42]. The developments provide for advances in satellite protection, orbital sensing, and understanding the far earth environment.
Protection of platforms, such as satellites, is also a key area for DDAS including structural health monitoring (SHM). Farhat et al., [43] utilized the DDDAS
principles towards SHM of materials assessments of equipment, while Chang, et al. has followed with aircraft composite structures which is featured in the book. Having an accurate model, with embedding sensing, supports real time response to a dynamically changing environment. Additionally developments include reduced-order modeling (ROM) such that the ensemble of models can be refined over model parameters, uncertainty estimation, and sensing bias [44]. Oden et al., [45] provided additional benefits of SHM for damage assessment and others highlighted modeling updates that account for materials damage [46]. The book highlights recent advancements in SHM using the DDDAS paradigm such as for aerospace systems.
Recently, Wilcox and Allaire et al., [47] have utilized online/offline modeling in support of self-aware vehicles which paves the way for autonomous systems. Included in their research is a focus on the model dimensionality for operational performance [48]. As a second example, Mohseni et al. has a wide variety of air and water autonomous systems and applied DDDAS for control and atmospheric sensing [49]. The monitoring of the environment supported the health monitoring of the vehicles with a changing environment. These developments have been incorporated into the control of soaring vehicles [50]. The third example includes onboard avionics to sense fault detection [51]. Varela et al., has led a group to bring together the computations with that of electronics health assessment for safe flight [52]. Typically, the theory employed for self-aware vehicles is in estimation.
To achieve the efforts in analysis over multiple domains requires the coordination and estimation of the techniques. Using the ensemble Kalman filter, Sandu et al. [53] addressed the computational aspects of data assimilation for aerosol in the atmosphere while Ravela et al. [54] devised methods for air platform positioning. Other methods looked at the methods to use in forecasting prediction [55]. If the DDDAS methods are able to forecast the movements, they can be use field alignment to estimate vehicle locations such as with quadrature information [56, 57]. Likewise, the fidelity of the parameters affects the estimation of model accuracy [58], which enables a mixture of ensembles [59].
Estimation methods are elements of data fusion techniques. The integration of measurements includes data, sensor, and information fusion. Information fusion aligns well with the DDDAS principals [60]. Such an example is an array of sensors for target detection and classification [61]. DDDAS hence can improve pattern recognition [62] or classification especially if data analysis is completed over features [63]. Recent methods have combined heterogeneous data in support of nonlinear classification of moving objects using signal and pixel data [64].
Moving entity analysis includes object estimation. Hoffman et al. [65] used DDDAS in analysis of hyperspectral data to gather relevant features of the moving object. Fujimoto et al. [66] used these methods for ground vehicle analysis, while others advanced the methods for multidimensional assignment in support of aerial vehicle monitoring [67].
The DDDAS concept leverages models such as scene, roads, or other terrain information. Context aware approaches were investigated [68], along with the need to learn the measurement models [69]. These methods were furthered by the information fusion community for context-enhanced information fusion which shows how DDDAS techniques can improved tracking over many operating conditions for robust performance [70].
Building on the theory and methods, efforts also include design.
5.3 Design – Systems and Architectures
The third section of the review includes systems architecture, energy networks, systems design, and cyber network analysis, with recent efforts in cloud computing. In the early methods of DDDAS, there was a need for scalable architectures and agent-based systems where evaluated [71]. DDDAS showed promise for supply chain analysis to improve the logistic and movement of parts [72]. Likewise web-based methods provide a use case for distributed simulations for computer data streaming [73]. Web-based methods afford query languages for DDDAS designs [74] and analysis [75].
The distributed aspects of network analysis were adapted and applied for power system and energy analysis [76 , 77]. Power analysis as a function of microgrids can support the power and energy available for aircraft which requires an adequate model of the energy distribution [78]. As for ground vehicles, the energy consumption can be improved both locally for a car and globally for traffic [79].
The networks, whether power grids or equipment, the effective global analysis can improve situation awareness for disaster management [80]. The systems approach applied to smart cities and urban infrastructures supported assessment of emissions on the climate [81]. Likewise, with the systems analysis, methods can support the design of embedded electronics for signal processing [82]. These methods were further analyzed for adaptive video stream processing [83, 84].
Recent trends have changed the network application to include communication and cyber networks. While traditionally, DDDAS looked at these methods for web services [85], DDDAS revised trust monitoring on a network [86], such that trust and privacy relied on the trust analysis for sensing control and assignment [87]. Recent efforts include extending these and a comprehensive analysis of DDDAS and the coordination of trust was explored by Blasch and Hariri et al.[88, 89].
Finally, the integration of DDDAS with computing has shown promise for the advancement of systems and software solutions. Quality of Service (QoS) optimization improved using DDDAS [90]. The use of a cloud-based system was successful for real-time tracking of targets from Wide Area Motion Imagery (WAMI) streaming data [91]. Another approach used cloud computing for cyber physical systems (CPS) to manage the data streams between CPS networked devices and those of sensors at the edge [92]. Darema et al. [93] combined these methods in a review of the benefits of DDDAS in support of a variety of applications such as Distributed behavior model orchestration in cognitive internet of things (IoT).
6 Book Overview
This book emerged from the 2016 DDDAS workshop in Hartford Connecticut from which presentations are available to support the written chapters on the DDDAS website. The website also hosts some software methods and data to support the
DDDAS analysis. To overview some contributions, the table below briefly highlights the chapter theory, simulation, data and application. The reader can use to table to focus on those chapters where the theoretical content and application context is of interest to their research and analysis.
Theory Simulation Data ApplicationMeasurement Aware: Assimilation, Uncertainty
QuantificationTractable Non-
Gaussian Representations in
Dynamic Data Driven Coherent Fluid
Mapping
Reduced Order Modeling with Ensemble Filtering
Atmospheric Plumes
UAV Tracking plume detection
Unmanned Aerial Systems
Dynamic Data-Driven Adaptive
Observations in Data Assimilation for
Multi-Scale Systems
Information-theoretic Particle Filtering
Lorenz 1963 Weather data
Weather augmented nonlinear flight
Sensor Selection in Dynamic Flight
Dynamic Data-Driven Uncertainty
Quantification via Generalized
Polynomial Chaos
Polynomial Chaos and GMM Uncertainty Quantification
Isogeometric Analysis (IGA) and finite element modeling
Surrogate management framework of beam displacement
Structural health monitoring data
Wing composite sturctures
A Dynamic Data-driven Stochastic State-Awareness
Framework for the Next Generation of Bio-inspired Fly-by-
feel Aerospace Vehicles
AutoRegressive pooling of sensor data
Vector-dependent Functionally Pooled models
piezoelectric lead-zirconate titanate (PZT) , strain gauges, and temp sensors
Fly-by-feel aerospace vehicle
7 DDDAS Future
Three future areas of DDDAS include (1) data science learning, (2) autonomy through adaptation, and (3) systems design with smart sensing – as shown in Figure 7.
Data movement and data science are future efforts aligned with the growth of artificial intelligence (AI), machine learning, and deep networks. These growing areas of interest follow from the recent trend in big data. The original DDDAS paradigm calls out big data as an emerging theme that utilize algorithms, models, and computation to harness data availability. Algorithms still need to be adapted to the dynamic environment. For example, neural networks do interpolation through modeling, but are not good at extrapolation to changing environments nor explanation of resolved decisions. If, on the other hand, instrumentation methods capturing data are integrated with high-dimensional modeling of the situation context, then such constructs within AI can be realizable for design efforts.
Fig. 7. DDDAS Future Areas.
Autonomy includes traditional vehicles [94] to recent data science methods such as data at rest, data in motion, and data in use concepts. While the data concepts (i.e., at rest, in motion, and in use) were promoted by the software community, these labels were mostly for the movement of data and not the processing of the data. The processing of the data, augmented with modeling can be a hallmark of future autonomous systems. Autonomy at rest (AAR) leverages data science to combine or fuse the data, while autonomy in motion (AIM) supports the interaction among platforms such as UAVs. DDDAS is focused on autonomy in use (AIU). Autonomous systems dynamically interact with the environment, so there is a need for not only complex modeling, but also methods in which real-time distributed sensing updates the models. Together, modeling, sensing, and data movement are future trends in DDDAS to achieve autonomous solutions.
The third growth area is in smart sensing through networked systems and software, or architectures to move and process data with high performance computing over a wide variety of sensors. The coordination of social modeling, internet of things (IoT), cyber physical systems, and power grids require systems and software developments to coordinate the dynamic data. The DDDAS efforts will expand from DDDAS principles, while leveraging the developments in autonomy and learning. Additionally, the sensing includes not only the data exploitation, but also information collection, processing, fusion, and analysis [95].
Fundamental basic research in DDDAS will be gathered from, and contribute to, scientific applications, mathematical foundations, and infrastructure architectures. Specifically, advances in theory, methods, and design will continue to expand the science and engineering from the DDDAS principles. The book highlights recent accomplishments while future meetings will showcase emerging developments.
8 Summary
The book organizes DDDAS developments in three parts of the different areas that are prominent in DDDAS methods: theory, methods, and design. The first part, theory, discusses some of the key fundamental approaches researchers have used including data assimilation, process modeling and filtering, and estimation. The second part, methods, includes key interactions between the theory and the use cases such as structural analysis for structural health monitoring, systems control for component processing, and image computing for situation evaluation. Finally, the third part, design, includes recent domain applications including situation awareness through environmental assessment, energy awareness such as power grids, and cyber awareness concerning privacy and security protections.
Fig. 8. DDDAS Book Overview
The readers of this book should appreciate some of the DDDAS developments to include theory such as object estimation, information fusion, and sensor management. The recent interest in UAVs provided a construct for methods including command and control, swarm analysis, and structural health monitoring. Finally, the basis of the applications are examples leveraging environmental science where big data modeling extends from the DDDAS foundations in weather forecasting, volcanic ash assessment, and wildfire monitoring. In the last two decades, DDDAS has resulted in many systems currently used by academics, researchers, practitioners, and industrialists. This book helps to capture and organize these results for the reader. The DDDAS community encourages any discussion, comments, and contributions through the website: www.1dddas.org.
This work is supported by the DDDAS program of the Air Force Office of Scientific Research (AFOSR).
References
. A. Aved, E. Blasch, “Dynamic Data Driven Applications Systems (DDDAS).” Website, www.1dddas.org, 2014.
2. F. Darema, “Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems.” Proceedings of the IEEE, 93 (3):692-697, 2005.
3. F. Darema, “The Next Generation Program,” 1998. http://www.nsf.gov/pubs/1999/nsf998/nsf998.htm
4. F. Darema, “New software architecture for complex applications development and runtime support, “Int. J. High-Performance Computation, Special Issue on Programming Environments, Clusters, and Computational Grids for Scientific Computing, Vol. 14, No. 3, 2000.
5. F. Darema, “The Next Generation Software Program,” International Journal of Parallel Programming 33 (2–3), pp. 73–79, June 2005. doi:10.1007/s10766-005-4785-6.
6. D. S. Bernstein, A. Ridley, J. Cutler, A. Cohn, “Transformative Advances in DDDAS with Application to Space Weather Monitoring,” Project Report, Univ. Michigan, 2015.
7. C. Yang, M. Bakich, et al., “Pose Angular-Aiding for Maneuvering Target Tracking”, Int. Conf. on Info Fusion, 2005.
8. J. Dunık, O. Straka, et al., “Random-Point-Based Filters: Analysis and Comparison in Target Tracking,” IEEE Tr. on Aerospace and Electronics Sys., 51(2): 1403-1421, 2015.
9. E. P. Blasch, E. Bosse, D. A. Lambert, High-Level Information Fusion Management and Systems Design, Artech House, Norwood, MA, 2012.
10. F. Darema, “The Next Generation Software Workshop – IPDPS’07,” IEEE Int’l Parallel and Distributed Processing Symposium (IPDPS), 2007.
11. F. Darema, “Cyberinfrastructures of Cyber-applications-systems.” Procedia Computer Science, 1 (1): 1287-1296, 2010. doi:10.1016/j.procs.2010.04.143.
12. A. R. Chaturvedi, “Society of simulation approach to dynamic integration of simulations,” IEEE Winter Simulation Conference 2006.
13. S. Sarkar, P. Chattopdhyay, A. Ray, S. Phoha, M. Levi, “Alphabet size selection for symbolization of dynamic data-driven systems: An information-theoretic approach,” American Control Conference (ACC), Pages: 5194 - 5199, 2015.
14. V. Maroulas, K. Kang, I. D. Schizas, M. W. Berry, “A learning drift homotopy particle filter,” International Conference on Information Fusion, pp. 1930-1937, 2015.
15. E. Blasch, “Enhanced air operations using JView for an air-ground fused situation awareness udop,” IEEE/AIAA Digital Avionics Systems Conference (DASC), 2013. DOI: 10.1109/DASC.2013.6712597
16. J. Michopoulos, “Ddema: A data driven environment for multiphysics applications,” International Conference Computational Science, 2003.
17. G. Carmichael, D. N. Daescu, Sandu, T. Chai, “Computational aspects of chemical data assimilation into atmosphere models,” International Conference Computational Science, 2003.
18. C. Evangelinos, R. Chang, P. F.J. Lermusiaux, N. M. Patrikalakis, “Rapid real-time interdisciplinary ocean forecasting using adaptive sampling and adaptive modeling and legacy codes: Component ecapsulation using xml,” International Conference Computational Science, 2003.
19. M. Parashar, V. Matossian, W. Bangerth, H. Klie, B. Rutt, T. Kurc, U. Catalyurek, J. Saltz, M. F. Wheeler, “Towards dynamic data-driven optimization of oil well placement,” International Conference Computational Science, 2005.
20. B. Plale, D. Gannon, D. Reed, S. Graves, K. Droegemeier, B. Wilhelmson, M. Ramamurthy, “Towards dynamically adaptive weather analysis and forecasting in LEAD,” International Conference Computational Science, 2005.
21. T. B. Trafalis, I.Adrianto, M. B. Richman, “Active learning with support vector machines for tornado prediction,” International Conference Computational Science, 2007.
22. L. Ramakrishnan, Y. Simmhan, B. Plale, “Realization of dynamically adaptive weather analysis and forecasting in LEAD: Four years down the road,” International Conference Computational Science, 2007.
23. L. Zhang, A. Sandu, “Data assimilation in multiscale chemical transport models,” International Conference Computational Science, 2007.
24. N. Roy, H.-L. Choi, D. Gombos, J. Hansen, J. How, S. Park, “Adaptive observation strategies for forecast error minimization,” International Conference Computational Science, 2007.
25. S. Ravela, “Quantifying uncertainty for coherent structures,” Procedia Computer Science, 9, pp. 1187-1196, 2012.
26. J. Michopoulos, P. Tsompanopoulou, E. Houstis, A. Joshi, “Agent-based simulation of data-driven fire propagation dynamics,” International Conference Computational Science, 2004.
27. J. Mandel, J. D. Beezley, L. S. Bennethum, S. Chakraborty, J. L. Coen, C. C. Douglas, J. Hatcher, M. Kim, A. Vodacek, “A dynamic data driven wildland fire model,” International Conference Computational Science, 2007.
28. J. D. Beezley, S. Chakraborty, J. L. Coen, C. C.Douglas, J. Mandel, A. Vodacek, Z. Wang, “Real-time data driven wildland fire modeling,” International Conference Computational Science, 2008.
29. R. Rodriguez-Aseretto, M. Di Leo, A. Cortés, J. S. Miguel-Ayanz, “A data-driven model for big forest fires behavior prediction in Europe,” Procedia Computer Science, 18, pp. 186-1870, 2013.
30. L. Wang, D. Chen, W. Liu, Y. Ma, Y. Wu, Z. Deng, “DDDAS-Based Parallel Simulation of Threat Management for Urban Water Distribution Systems,” Computing in Science & Engineering, Vol. 16 (1), pp. 8-17, 2014, DOI: 10.1109/MCSE.2012.89
31. A. K. Patra, M. I. Bursik, J.Dehn, M. Jones, M. Pavolonis, E. B. Pitman, T. Singh, P. Singla, E. R. Stefanescu, S. Pouget, P. Webley, “Challenges in developing DDDAS based methodology for volcanic ash hazard analysis - effect of numerical weather prediction variability and parameter estimation,” Procedia Computer Science, 18, pp. 1871-1880, 2013.
32. A. K. Patra, E. R. Stefanescu, R. M. Madankan, M . I. Bursik, E. B. Pitman, P. Singla, T. Singh, P. Webley, “Fast construction of surrogates for UQ central to DDDAS application to volcanic ash transport,” Procedia Computer Science, 29, pp. 1227- 1235, 2014.
33. V. H. V. S. Rao, A. Sandu, “A posteriori error estimates for DDDAS inference problems,” Procedia Computer Science, 29, pp. 1256-1265, 2014.
34. D. Metaxas, S. Venkataraman, C. Vogler, “Image-based stress recognition using a model-based dynamic face tracking system,” International Conference Computational Science, 2004.
35. D. Metaxas, G. Tsechpenakis, Z. Li, Y. Huang, A. Kanaujia, “Dynamically adaptive tracking of gestures and facial expressions,” International Conference Computational Science, 2006.
36. A. Majumdar, A. Birnbaum, D. Choi, A. Trivedi, S, K. Warfield, K. Baldridge, P. Krysl, “A dynamic data driven grid system for intra-operative image guided neurosurgery,” International Conference Computational Science, 2005.
37. J. T. Oden, K. R. Diller, C. Bajaj, J. C. Browne, J. Hazle, I. Babuska, J. Bass, L. Demkowicz, Y. Feng, D.Fuentes, S. Prudhomme, M. N. Rylander, R. J. Stafford, Y. Zhang, “Development of a computational paradigm for laser treatment of cancer,” International Conference Computational Science, 2006.
38. C. Bajaj, J. T. Oden, K. R. Diller, J. C. Browne, J. Hazle, I. Babuska, J. Bass, L. Bidaut, L. Demkowicz, A. Elliott, Y. Feng, D. Fuentes, B. Kwon, S. Prudhomme, R. J. Staord, Y. Zhang, “Using cyber-infrastructure for dynamic data driven laser treatment of cancer,” International Conference Computational Science, 2007.
39. I. S. Kim, J. Chandrasekar, A. Ridley, D. S. Bernstein, “Data assimilation using the global ionosphere-thermosphere model,” International Conference Computational Science, 2006.
40. S. Ravela, J. Marshall, C. Hill, A. Wong, S. Stransky, “Real-time observatory for laboratory simulation of planetary circulation,” International Conference Computational Science, 2007.
41. A. V. Morozov, A. J. Ridley, D. S. Bernstein, N. Collins, T. J. Hoar, J. L. Anderson, “Data assimilation and driver estimation for the Global Ionosphere–Thermosphere Model using the Ensemble Adjustment Kalman Filter,” Journal of Atmospheric and Solar-Terrestrial Physics, Vol. 104, pp. 126–136, 2013.
42. A. G. Burrell, A. Goel, A. J. Ridley, D. S. Bernstein, “Correction of the Photoelectron Heating Efficiency Within the Global Ionosphere-Thermosphere Model Using Retrospective Cost Model Refinement,” Journal of Atmospheric and Solar-Terrestrial Physics, Vol. 104, 2015.
43. C. Farhat, J.G. Michopoulos, F.K. Chang, L.J. Guibas, and A.J. Lew, “Towards a dynamic data driven system for structural and material health monitoring,” International Conference Computational Science, 2006.
44. J. Cortial, C. Farhat, L. J. Guibas, M. Rajashekhar, “Time-parallel exploitation of reduced-order modeling and sensor data reduction for structural and material health monitoring DDDAS,” International Conference Computational Science, 2007.
45. E. E. Prudencio, P. T. Bauman, D. Faghihi, J. T. Oden, K. Ravi-Chandar, S. V. Williams, “A dynamic data driven application system for real-time monitoring of stochastic damage,” Procedia Computer Science, 18, pp. 2056-2065, 2013.
46. E. E. Prudencio, P. T. Bauman, D. Faghihi, K. Ravi-Chandar, J. T. Oden, “A Computational Framework for Dynamic Data Driven Material Damage Control, Based on Bayesian Inference and Model Selection,” International Journal for Numerical Methods in Engineering, Vol. 102, Issue 3-4, pp. 379–403, April 2015. DOI: 10.1002/nme.4669
47. D. Allaire, J. Chambers, R. Cowlagi, D. Kordonowy, M. Lecerf, L. Mainini, F. Ulker, K. Willcox, “A baseline offine/online DDDAS capability for self-aware aerospace vehicles,” Procedia Computer Science, 18, pp. 1959-1968, 2013.
48. D. Allaire, D. Kordonowy, M. Lecerf, L. Mainini, K. Willcox, “Multi-fidelity DDDAS methods with application to a self-aware aerospace vehicle,” Procedia Computer Science, 29, pp. 1182-1192, 2014.
49. L. Peng, K. Mohseni, “Sensor driven feedback for puff estimation using unmanned aerial vehicles,” International Conference on Unmanned Aircraft Systems (ICUAS), pp: 562 - 569, 2014. DOI: 10.1109/ICUAS.2014.6842298.
50. E. Blasch, P. Paces, P. Kostek, K. Kramer, “Summary of Avionics Technologies,” IEEE Aerospace and Electronics Systems Magazine, Vol. 30 (9), pp. 6-11, Sept. 2015
51. W. Silva, E. W. Frew, W. Shaw-Cortez, “Implementing path planning and guidance layers for dynamic soaring and persistence missions,” International Conference on
Unmanned Aircraft Systems (ICUAS), pp. 92-101, 2015 DOI: 10.1109/ICUAS.2015.7152279
52. S. Imai, E. Blasch, A. Galli, F. Lee, C. A. Varela, “Airplane Flight Safety Using Error-Tolerant Data Stream Processing,” IEEE Aerospace and Electronics Systems Magazine, Vol. 32, No. 4, April 2017.
53. A. Sandu, W. Liao, G. R. Carmichael, D. Henze, J. H. Seinfeld, T. Chai, D. Daescu, Computational aspects of data assimilation for aerosol dynamics,” International Conference Computational Science, 2004.
54. S. Ravela, “Amplitude-position formulation of data assimilation,” International Conference Computational Science, 2006.
55. B. Jia, K. D. Pham, E. Blasch, D. Shen, Z. Wang, G. Chen , “Cooperative Space Object Tracking using Space-based Optical Sensors via Consensus-based Filters,” IEEE Tr. on Aerospace and Electronics Systems, Vol. 52, No. 3, pp. 1908-1936, 2016.
56. S. Ravela, “Two extensions of data assimilation by field alignment,” International Conference Computational Science, 2007.
57. P. Tagade, S. Ravela, “On a quadratic information measure for data assimilation,” American Control Conf., pp. 598 – 603, 2014.
58. T. C. Henderson, N. Boonsirisumpun, “The impact of parameter estimation on model accuracy assessment,” Procedia Computer Science, 18, pp. 1969-1978, 2013.
59. P. Tagade, H. Seybold, S. Ravela, “Mixture ensembles for data assimilation in dynamic data-driven environmental systems,” Procedia Computer Science, 29, pp. 1266-1276, 2014.
60. E. P. Blasch, “Dynamic data driven applications system concept for information fusion,” Procedia Computer Science, 18, pp. 1999-2007, 2013.
61. N. Virani, S. Marcks, S. Sarkar, K. Mukherjee, A. Ray, S. Phoha, “Dynamic data driven sensor array fusion for target detection and classification, Procedia Computer Science, 18, pp. 2046-2055, 2013.
62. E. Blasch, G. Seetharaman, F. Darema, “Dynamic Data Driven Applications Systems (DDDAS) modeling for Automatic Target Recognition,” Proc. SPIE, Vol. 8744, 2013.
63. B. Smith, P. Chattopadhyay, A. Ray, T. R. Damarla, “Performance robustness of feature extraction for target detection & classification,” IEEE American Control Conference, 2014.
64. T. Chin, K. Xiong, E. Blasch, “Nonlinear target tracking for threat detection using RSSI and optical fusion,” International Conference on Information Fusion, pp. 1946-1953, 2015.
65. B. Uzkent, M. J. Hoffman, A. Vodacek, J. P. Kerekes, B. Chen, “Feature matching and adaptive prediction models in an object tracking DDDAS,” Procedia Computer Science, 18, pp. 1939-1948, 2013.
66. R. Fujimoto, A. Guin, M. Hunter, H. Park, R. Kannan, G. Kanitkar, M. Milholen, S. Neal, P. Pecher, “A dynamic data driven application system for vehicle tracking, Procedia Computer Science, 29, pp. 1203-1215, 2014.
67. B. Uzkent, M. J. Hoffman, A. Vodacek, “Integrating Hyperspectral Likelihoods in a Multidimensional Assignment Algorithm for Aerial Vehicle Tracking,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, Issue 9, pp. 4325 - 4333, 2016. DOI: 10.1109/JSTARS.2016.2560220
68. N. Nguyen, M. H. H. Khan, “Context Aware Data Acquisition Framework for Dynamic Data Driven Applications Systems (DDDAS),” IEEE Military Communications Conf., pp. 334-341. 2013. DOI: 10.1109/MILCOM.2013.65
69. N. Virani, J-W. Lee, S. Phoha, A. Ray, “Learning context-aware measurement models,” American Control Conference (ACC), pp. 4491-4496, 2015. DOI: 10.1109/ACC.2015.7172036
70. L. Snidaro, J. Garcia Herrero, J. Llinas, E. Blasch, Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge, Springer, 2016.
71. A. Chaturvedi, J. Chi, S. Mehta, D. Dolk, “SAMAS: Scalable architecture for multi-resolution agent-based simulation,” International Conference Computational Science, 2004.
72. Koyuncu, N., Lee, S., Vasudevan, K. K., Son, Y-J., Sarfare, P., “DDDAS-based multi-fidelity simulation for online preventive maintenance scheduling in semiconductor supply chain,” Winter Simulation Conference, pp. 1915-1923, 2007. DOI: 10.1109/WSC.2007.4419819
73. A. Boukerche, F. M. Iwasaki, R. B. Araujo, E. B. Pizzolato, “Web-Based Distributed Simulations Visualization and Control with HLA and Web Services,” 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications, pp. 17 - 23, 2008. DOI: 10.1109/DS-RT.2008.30
74. A. J. Aved, E. Blasch, “Multi-INT Query Language for DDDAS Designs,” Procedia Computer Science, Vol. 51, pp. 2518-2523, 2015.
75. E. Blasch, S. Phoha, “Special Issue: Dynamic Data-Driven Applications Systems (DDDAS) concepts in Signal Processing,” J. Signal Processing Systems, 24 May 2017, (DOI 10.1007/s11265-017-1253-7)
76. E. H. Abed, N. S. Namachchivaya, T. J. Overbye, M. A. Pai, P. W. Sauer, A. Sussman, “Data driven power system operations,” International Conference Computational Science, 2006.
77. N. Celik, A. E. Thanos, J. P. Saenz, “DDDAMS-based dispatch control in power networks,” Procedia Computer Science, 18, pp. 1899 – 1908, 2013.
78. E. Frew, B. Argrow, A. Houston, C. Weiss, J. Elston, “An energy-aware airborne dynamic data-driven application system for persistent sampling and surveillance,” Procedia Computer Science, 18, pp. 2008-2017, 2013.
79. S. Neal, R. Fujimoto, M. Hunter, “Energy consumption of Data Driven traffic simulations,” Winter Simulation Conference (WSC), pp. 1119 - 1130, 2016. DOI: 10.1109/WSC.2016.7822170
80. G. R. Madey, A.-L. Barabsi, N. V. Chawla, M. Gonzalez, D. Hachen, B. Lantz, A. Pawling, T. Schoenharl, G. Szabo, P. Wang, P. Yan, “Enhanced situational awareness: Application of DDDAS concepts to emergency and disaster management,” International Conference Computational Science, 2007.
81. R. M. Fujimoto, N. Celik, H. Damgacioglu, M. Hunter, D. Jin, Y-J. Son, J. Xu, “Dynamic data driven application systems for smart cities and urban infrastructures,” Winter Simulation Conference (WSC), pp. 1143 - 1157, 2016. DOI: 10.1109/WSC.2016.7822172
82. K. Sudusinghe, I. Cho, M. Van der Schaar, S. S. Bhattacharyya, “Model based design environment for data-driven embedded signal processing systems,” Procedia Computer Science, 29, pp. 1193-1202, 2014.
83. S. Chakravarthy, A. Aved, S. Shirvani, M. Annappa, E. Blasch, “Adapting Stream Processing Framework for Video Analysis,” Procedia Computer Science, Vol. 51, pp. 2648-2657, 2015.
84. H. Li, K. Sudusinghe, Y. Liu, J. Yoon, M. Van Der Schaar, E. Blasch, S. S. Bhattacharyya, “Dynamic, Data-Driven Processing of Multispectral Video Streams,” IEEE Aerospace and Electronics Systems Magazine, June 2017.
85. P. Chew, N. Chrisochoides, S. Gopalsamy, G. Heber, T. Ingraffea, E. Luke, J. Neto, K. Pingali, A. Shih, B. Soni, P. Stodghill, D. Thompson, S. Vavasis, P. Wawrzynek, “Computational science simulations based on web services,” International Conference Computational Science, 2003.
86. O. Onolaja, R. Bahsoon, G. Theodoropoulos, “Conceptual framework for dynamic trust monitoring and prediction,” Procedia Computer Science, 1, pp. 1241-1250, 2010.
87. L. Pournajaf, L. Xiong, V. Sunderam, “Dynamic data driven crowd sensing task assignment,” Procedia Computer Science, 29, pp. 1314-1323, 2014.
88. E. Blasch, Y. Al-Nashif, S. Hariri, “Static versus dynamic data information fusion analysis using DDDAS for cyber trust,” Procedia Computer Science, 29, pp. 1299-1313, 2014.
89. Y. Badr, S. Hariri, Y. Al-Nashif, E. Blasch,“Resilient and Trustworthy Dynamic Data-Driven Application Systems (DDDAS) Services for Crisis Management Environments,” Procedia Computer Science, Vol. 51, pp. 2623-2637, 2015.
90. Chen, T., Bahsoon, R., Theodoropoulos, G., “Dynamic qos optimization architecture for cloud-based DDDAS,” Procedia Computer Science, 18, pp. 1881-1890, 2013.
91. Wu, R., Liu, B., Chen, Y., Blasch, E., Ling, H., Chen, G., “A Container-based Elastic Cloud Architecture for Pseudo Real-time Exploitation of Wide Area Motion Imagery (WAMI) Stream,” The Journal of Signal Processing Systems, pp 1-13, Nov. 2016. DOI 10.1007/s11265-016-1206-6.
92. Shekar, S., “Dynamic Data Driven Cloud Systems for Cloud-Hosted CPS,” IEEE International Conference on Cloud Engineering Workshop (IC2EW), pp. 195 - 197, 2016. DOI: 10.1109/IC2EW.2016.38
93. Li, C-S., Darema, F., Chang, V., “Distributed behavior model orchestration in cognitive internet of things solution,” Enterprise Information Systems, 2017. (https://doi.org/10.1080/17517575.2017.1355984)
94. G. Seetharaman, A. Lakhotia, et al., “Unmanned Vehicles Come of Age: The DARPA Grand Challenge,” IEEE Computer Society Magazine, 39(12): 26-29, Dec 2006.
95. Y. Zheng. E. Blasch, Z. Liu, Multispectral Image Fusion and Colorization, SPIE, 2018.
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