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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX

作者： Blasch, Erik Grewe, Lynne L. Waltz, Edward L. Bendich, Paul Pavlovic, Vladimir Kadar, Ivan Chong, Chee-Yee MOVEJ Analyt Dayton OH 43524 USA Calif State Univ East Bay 25800 Carlos Bee Blvd Hayward CA 94542 USA Naval Postgrad Sch Ctr MultiINT Studies Monterey CA USA Duke Univ Durham NC 27708 USA Rutgers State Univ Piscataway NJ 08854 USA Interlink Syst Sci Inc 1979 Marcus Ave Lake Success NY 11042 USA POB 4082 Los Altos CA 94024 USA

ISBN: (纸本)9781510636248;9781510636231

During the 2019 SPIE DSS conference, panelists were invited to highlight the trends and use of artificial intelligence and machine learning (AI/ML) for information fusion. The common themes between the panelists include leveraging AI/ML coordinated with Information Fusion for: (1) knowledge reasoning, (2) model building, (3) object recognition and tracking, (4) multimodal learning, and (5) information processing. The opportunity for machine learning exists within all the fusion levels of the data Fusion Information Group model.

关键词： Artificial Intelligence Information Fusion Multimodal Deep Learning situation assessment probabilistic models target-tracking and recognition temporal networks dynamic data driven applications systems topology

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Image Fusion for Context-Aided Automatic Target Recognition 30

Image Fusion for Context-Aided Automatic Target Recognition

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Conference on Automatic Target Recognition XXX

作者： Blasch, Erik Liu, Zheng Zheng, Yufeng MOVEJ Analyt Dayton OH 43524 USA Univ British Columbia Okanagan Kelowna BC V1V 1V7 Canada Alcorn State Univ Lorman MS 39096 USA

ISBN: (数字)9781510635661

ISBN: (纸本)9781510635661;9781510635654

Automatic Target Recognition (TR) has seen many recent advances from image fusion, machine learning, and data collections to support multimodal, multi-perspective, and multi-focal day-night robust surveillance. This paper highlights ideas, strategies, and concepts as well as provides an example for electro-optical and infrared image fusion cooperative intelligent ATR analysis. The ATR results support simultaneous tracking and identification for physics-based and human-derived information fusion (PHIF). The importance of context serves as a guide for ATR systems and determines the data requirements for robust training in deep learning approaches.

关键词： Image Fusion Automatic Target Recognition Information Fusion Multimodal Deep Learning Situation assessment dynamic data driven applications systems

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Artificial Intelligence in Use by Multimodal Fusion 22

Artificial Intelligence in Use by Multimodal Fusion

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22nd International Conference on Information Fusion (FUSION)

作者： Blasch, Erik P. Majumder, Uttam Rovito, Todd Raz, Ali K. AFOSR Rome NY 13441 USA Air Force Res Lab Rome NY 13441 USA Purdue Univ W Lafayette IN 47906 USA

ISBN: (纸本)9780996452786

The explosion of artificial intelligence (AI) methods present many opportunities for future technology, while at the same time, there are some unresolved challenges. One example is the ability to explain, reproduce, and leverage AI methods for practical applications. AI, along with many other pattern recognition and information fusion techniques can augment a user in decision making. However, when difficult decisions arise, contextual knowledge can help discern the merits of the results. In this paper, we highlight the ability of AI to support "usable" man-machine support as for methods that support data at rest, data in use, and data in motion (RUM). An example is shown for deep multi modal image fusion in support of simultaneous tracking and identification.

关键词： High-Level Information Fusion Situation Assessment dynamic data driven applications systems Video analysis

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Information Fusion for Infrastructure Analysis 28

Information Fusion for Infrastructure Analysis

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

作者： Blasch, Erik Air Force Res Lab Arlington VA 22203 USA

ISBN: (纸本)9781510627024

Complex systems are composed from various networks such as physical, computer, cyber, communication, cognitive, and social systems. Information fusion can support complex systems uncertainty analysis for various infrastructures applications such as electrical power grids, human-machine coordination, and urban systems. In addition, advances in artificial intelligence (AI), machine learning (ML), and data mining;along with information fusion provide a big data approach for multidomain complex systems analysis. The presentation position focuses on the dynamic data driven applications systems (DDDAS) paradigm that brings together high-dimensional network models, real-time data measurement processing, and uncertainty quantification to support infrastructure network analysis. The challenges of applying information fusion to infrastructure system include multi-dimensional models, asynchronous measurement updates, human-machine architectures, communication protocols, and user awareness support.

关键词： Information fusion big data complex systems dynamic data driven applications systems

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Distributed Behavior Model Orchestration in Cognitive Internet of Things Solution (vol 12, pg 414, 2018)

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ENTERPRISE INFORMATION systems 2018年第6期12卷 752-752页

作者： Li, C-S Darema, F. Chang, V. Accenture Operations San Jose CA USAhttp://***/0000-0001-9072-1140 Air Force Office of Scientific Research Arlington VA USA IBSS Xi’an Jiaotong-Liverpool University Suzhou China

The introduction of pervasive and ubiquitous instrumentation within Internet of Things (IoT) leads to unprecedented real-time visibility (instrumentation), optimization and fault-tolerance of the power grid, traffic, transportation, water, oil & gas, to give some examples. Interconnecting those distinct physical, people, and business worlds through ubiquitous instrumentation, even though still in its embryonic stage, has the potential to create intelligent IoT solutions that are much greener, more efficient, comfortable, and safer. An essential new direction to materialize this potential is to develop comprehensive models of such systems dynamically interacting with the instrumentation in a feed-back control loop. We describe here opportunities in applying cognitive computing on interconnected and instrumented worlds (Cognitive Internet of Things-CIoT) and call out the system-of-systems trend among distinct but interdependent worlds, and dynamic data-driven Application System (DDDAS)-based methods for advanced understanding, analysis, and real-time decision support capabilities with the accuracy of full-scale models.

关键词： Internet of things CIoT smarter planet behavior models cognitive computing world models smart grid fog computing edge computing progressive representation DDDAS dynamic data driven applications systems infosymbiotic systems autonomy

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Physics-Based and Human-derived Information Fusion Video Activity Analysis 21

Physics-Based and Human-derived Information Fusion Video Act...

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21st International Conference on Information Fusion (FUSION)

作者： Blasch, Erik P. Aved, Alex J. Air Force Off Sci Res Rome NY 13441 USA Air Force Res Lab Rome NY 13441 USA

ISBN: (纸本)9780996452762

With ubiquitous data acquired from sensors, there is an ever increasing ability to abstract content from the combination of physics-based and human-derived information fusion (PHIF). The advancement of PHIF tools include graphical information fusion methods, target tracking techniques, and natural language understanding. Current discussions revolve around dynamic data-driven applications systems (DDDAS) which seeks to leverage high-dimensional modeling with real-time physical systems. An example of a model includes a learned dictionary that can be leveraged as information queries. In this paper, we discuss the DDDAS paradigm of sensor measurements, information processing, environmental modeling, and software implementation to deliver content for PHIF systems. Experimental results demonstrate the DDDAS-based Live Video Computing dataBase Modeling systems (LVC-DBMS) approach affording data discovery and query-based flexibility for awareness to provide narratives of unknown situations.

关键词： High-Level Information Fusion Situation Assessment dynamic data driven applications systems Video analysis

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DDDAS for Space applications 11

DDDAS for Space Applications

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Conference on Sensors and systems for Space applications XI

作者： Blasch, Erik Pham, Khanh D. Shen, Dan Chen, Genshe Air Force Off Sci Res Arlington VA 22203 USA US Air Force Res Lab Space Vehicles Directorate Albuquerque NM USA Intelligent Fus Technol Inc 20271 Goldenrod Labe Germantown MD USA

ISBN: (数字)9781510617940

ISBN: (纸本)9781510617940

The dynamic data-driven applications systems (DDDAS) paradigm is meant to inject measurements into the execution model for enhanced systems performance. One area of interest in DDDAS is for space situation awareness (SSA). For SSA, data is collected about the space environment to determine object motions, environments, and model updates. dynamically coupling between the data and models enhances the capabilities of each system by complementing models with data for system control, execution, and sensor management. The paper overviews some of the recent developments in SSA made possible from DDDAS techniques which are for object detection, resident space object tracking, atmospheric models for enhanced sensing, cyber protection, and information management.

关键词： Space Situational awareness Resident Space Objects dynamic data driven applications systems

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Physics-Based and Human-derived Information Fusion Video Activity Analysis

Physics-Based and Human-derived Information Fusion Video Act...

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International Conference on Information Fusion

作者： Erik P. Blasch Alex J. Aved Air Force Office of Scientific Research Rome NY Air Force Research Laboratory Rome NY

关键词： High-Level Information Fusion Situation Assessment dynamic data driven applications systems Video analysis

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CyberInfrastructures of Cyber-applications-systems

CyberInfrastructures of Cyber-Applications-Systems

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International Conference on Computational Science (ICCS)

作者： Darema, Frederica NSF Arlington VA 22230 USA

The paper discusses foundational notions and conditions in complex applications modeling and in software frameworks to support advanced CyberInfrastructure environments such as those implied and required for dynamic data driven applications systems, as well as other classes of applications, collectively referred to here as Cyber-applications-systems. The paper discusses considerations of dynamic invocation of multi-scale models, uncertainty quantification, uncertainty propagation, and dynamic runtime systems support on heterogeneous, distributed, end-to-end dynamically integrated high-end, real-time and instrumentation and control systems. (C) 2010 Published by Elsevier Ltd.

关键词： dynamic data driven applications systems DDDAS InfoSymbiotic systems InfoSymbiotics multi-scale, multi-modal, multi-data dynamic multi-scale modeling uncertainty quantification uncertainty propagation cyberinfrastructure software architectural frameworks real-time high-end instrumentation control

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CyberInfrastructures of cyber-applications-systems

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Procedia Computer Science 2010年第1期1卷 1287-1296页

作者： Frederica Darema NSF Arlington VA 22230

关键词： dynamic data driven applications systems DDDAS InfoSymbiotic systems InfoSymbiotics Multi-scale Multi-modal Multi-data dynamic multi-scale modeling Uncertainty quantification Uncertainty propagation Cyberinfrastructure Software architectural frameworks Realtime High-end Instrumentation Control

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