Non-Destructive Inspection Data & Evidence Fusion Program (NDI)

KBSI applied advanced data fusion technologies in investigating methods for increasing the accuracy of non-destructive inspection imaging technology.  Using both ultrasonic and eddy current images of KC-135 lap joint coupons, KBSI developed image processing techniques, both classical and wavelet-based, for the pre-processing of image data prior to corrosion quantification and fusion.

Corrosion is considered the most significant form of damage for an aging aircraft—impacting both maintenance costs and readiness.  Currently, there is an urgent need to improve on established non-destructive inspection methods that increase detection reliability and accuracy in multi-layer structures.

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Geospatial Three Dimensional Topology Model (3DTGIS)

3DTGIS provides an innovative 3D topology-based, non-manifold representation approach that enables the ability to integrate a solids modeling capability with COTS Geographic Information Systems (GIS) to improve battlefield decision making.

“Long experience indicates that, all else being equal, military practitioners and their civilian supervisors who purposely make geography work for them are winners more often than not, whereas those who lack sound appreciation for the significance of geography succeed only by accident.”

– John Collins, Military Geography: For Professionals and the Public

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intelligence Rapid Product Generator (iRPG)

The iRPG initiative is extending KBSI’s MODELMOSAIC® technology to support the creation, analysis, and distribution of tactical intelligence data.  iRPG will help users develop application services for assessing COA and develop workflows that address specific domain ontology questions, allowing for more effective use of intelligence data.

KBSI is researching, designing, and developing an intelligence Rapid Product Generator (iRPG) that is an extension of KBSI’s MODELMOSAIC® knowledge management framework.  The iRPG technology will allow the ISR-C2 community to use MODELMOSAIC® in support of warfighters’ creation, analysis, and distribution of tactical intelligence data.

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Information to Systematically Enhance Intuitive Decision-Making (iSEE)

The iSEE initiative researched and developed the critical Augmented Cognition (AugCog) environment extensions needed to improve, via training, the cognitive readiness of military personnel in the high stress, sensorially overwhelming environments that characterize today’s full-spectrum warfare.

The new reality of full-spectrum warfare calls for ordinary soldiers to possess skill sets that extend beyond conventional combat, and, in response, our armed services have recently recognized the importance of formalizing the standards, terms, and instructional foundations associated with “cognitive readiness.”  Cognitive readiness describes the mental preparation an individual must establish and sustain to perform effectively in the complex and unpredictable environment of modern military operations.  Effective cognitive readiness ultimately manifests itself as successful pattern recognition, creative adaptability, and intuitive decision making in the field.

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Data Display Markup Language (DDML)

KBSI is developing an advanced component-based Data Display Markup Language (DDML), an XML-based neutral format also developed by KBSI, as the inter-lingua in translating the data display languages supported by different vendors.

Data display is a critical component for T&E environments in aircraft, space, and energy systems.  Because telemetry functions associated with these systems produce too much data for a single person to comprehend, data display–customizable display objects, including strip charts, bar charts, vertical meters, round gauges, cross plots, tabular displays, orientation displays, and bit maps–is critical in presenting this information in an understandable format.

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XML/IDEF3-Based Training

KBSI defined a neutral format for capturing, organizing, and sharing process-related knowledge that is based on the Integrated Definition family of Methods and the eXtended Markup Language (XML).  Using this standard, KBSI developed an advanced authoring tool-kit for situation-based, process-centric computer-based training systems.

Because processes are central to the operation of all aspects of an organization, most decision-making applications and implementation solutions deal with the capture, specification, representation, and manipulation of process-related information.  This information may in turn be used to support business process re-engineering, workflow systems development, and a wide range of training needs.  Since these application domains require the same process information, process knowledge capture for one application should easily support other purposes.  Yet, sharing and reusing process knowledge across applications remains an unrealized vision.  The need for standards to capture, represent, share, and display process-related information is particularly important in training applications.  Process-centered training provides students with the most reliable approach for understanding, internalizing, and applying new concepts.

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Geopolitical Region of Interest Projection System (GRIPS)

GRIPS is a methodology and tool suite that allows military planners to define, systematically evaluate, and globally share data concerning future geo-political contexts.  GRIPS’s quantitative analysis and knowledge sharing capabilities help military planners to current and future force deployment plans.

Naval planners must often look years and decades into the future to answer complex questions concerning the objectives and composition of future forces.  Successful long-term strategic planning starts with a thorough and systematic evaluation of the future states during which the plans will be enacted, followed by an assessment of plan outcomes and trade-offs.  Intelligence Analysts must develop supportable predictions of future geo-political contexts within the regions of interest.

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Netcentric Operations Defense Environment (NODE™)

The NODE™ system uses data mining and machine learning technologies to provide a more advanced and adaptable computer network defense.  The technology executes data mining and machine learning technologies and algorithms over the network hosts;  i.e., over the entire computing fabric.

The ubiquity of computing systems and networks has vastly improved the speed and ease of gathering, storing, and disseminating information.  Networking on a global scale, however, also gives rise to a significant disadvantage:  network vulnerability.  Securing data, particularly with respect to sensitive national security data and data transmissions, is a paramount concern, particularly given the increasing sophistication of computer terrorism.

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Generalized Event Representation Modeling & Analysis Tool (GERMAT™)

GERMAT™ provides knowledge-based assistance for neural network and fuzzy logic modeling, helping the U.S. Army to significantly improve battleground classification, target/decoy recognition and discrimination, logistics forecasting, intelligent data fusion, data mining, knowledge discovery, and optimization.

Neural networks and fuzzy set theoretic models offer promising results in performing complex mappings and reasoning in a wide variety of commercial and military domains.  Although these technologies have been applied extensively for over a decade, their use in complex, real-time domains is just beginning to be tested.  With the advent of recent, more robust and “non black-box”-like algorithms [such as wavenets and Fuzzy Associative Memories (FAMs)], these technologies exhibit even greater promise and potential.  These new generation information processing systems provide capabilities like adaptability, robustness, generalization, and the ability to work amid the imprecision and uncertainty of the real world–an ability which makes them especially attractive.  Neural networks in particular offer massive parallelism and future promise for hardware implementation and are ideal for applications such as forecasting, classification, pattern recognition, customer analysis, data mining, fraud detection, function fitting, etc.

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Mobile Application for Healthcare, Epidemiology, & Safety (MAES)

The MAES initiative designed and developed technology that collects data from multiple sources, analyzes the data for trends and patterns, and, using handheld devices, deliver context sensitive, location based alerts and advice to users on healthcare, disease, safety, and environmental related issues.

Humanitarian relief, disaster recovery, nation building:  these missions are becoming more significant for both military and non-governmental organizations every day.  Health awareness and a proactive approach to health outcomes are critical to these missions, but, in many developing countries, even basic reporting of the health status of affected populations can be a daunting task.  Communications infrastructures are minimal, transportation may be difficult, time is short, and the focus is often on addressing basic human survival needs.  Establishing baselines for epidemiological awareness may be too time consuming and difficult to be a priority.

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