Text Analytics Situational Awareness Toolkit (T-SAT)

KBSI is researching and designing technology that allows users to rapidly build text analytics-based systems that support the research and analysis of large bodies of disparate, unstructured text sources.

The Text Analytics Situational Awareness Toolkit (T-SAT) will apply an advanced object model that allows users to combine multiple text analytics algorithms to create complex research and analysis scenarios.  The technology will provide a user configurable GUI in which users can rapidly construct research and analysis systems that connect data sources to analytics-based algorithms.  T-SAT’s text analytic-based algorithms can be applied to a wide range of source types–unstructured text from an online social network, text from an online chat room, or digitized textual reports taken from human intelligence sources (HUMINT)–eliminating the need for tailoring algorithms according to each target source.

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Toolkit for Intelligent Combat Planning & Simulation (TINCOPS)

TINCOPS is a knowledge-based decision support system that manages and deploys knowledge for the U.S. military’s complex combat decision support applications and for analyzing and evaluating logistics plans.

The result of mission planning is a strategy for accomplishing the intended objectives that reflects decisions on the best methods and course of actions to follow.  The mission planning process is knowledge intensive and involves a number of factors that must be considered including uncertainties in the intelligence collected, enemy response, changes in logistics needs or routes, etc.  In addition, once the mission is active, changes in the battlespace can occur rapidly and commanders must receive accurate and current combat situational information in order to craft the most effective response and change to the original strategy.

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Toolkit for Enabling Adaptive Modeling & Simulation (TEAMS™)

TEAMS is a decision support tool that uses simulation to help NASA’s Kennedy Space Center (KSC) model various activities associated with spaceport project operations, improving decision making in areas like space vehicle mix impact, launch rates, asset utilization, and spaceport life cycle costs.

NASA’s Kennedy Space Center (KSC) was experiencing problems from the numerous configurations and payload possibilities with their planned spaceport, and they lacked a tool to assist in the modeling and analysis of spaceport decisions.  Not to mention, spaceport management was also in need of a method for capturing knowledge pertaining to spaceport operations.  This dilemma resulted in an initiative entitled Toolkit for Enabling Adaptive Modeling and Simulation (TEAMS™), and a solution was consequently developed with KBSI’s expertise in methods and project management.

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Cost Benefit Analysis Support Environment (CBASE)

KBSI developed and demonstrated a new integration-driven paradigm for cost benefit analysis.  The CBASE technology provides qualitative and quantitative methods for modeling problem situations, performing cost benefits analysis, validating cost analysis by simulation, generating alternative scenarios, and comparing cost benefits across scenario.

The first phase of the Cost Benefit Analysis Support Environment (CBASE) initiative focused on developing and demonstrating a new integration-driven paradigm for cost benefit analysis.  Throughout the CBASE research, KBSI used a combination of qualitative and quantitative methods for:

  • Modeling problem situations,
  • Performing cost benefit analysis,
  • Validating cost analysis by simulation,
  • Generating alternative scenarios, and
  • Comparing cost benefits across scenario.

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Reliability Centered Maintenance Costing (RCMC™)

The RCMC™ methods and tools support the critical knowledge discovery, cost modeling, and maintenance cost projection needed by Reliability Centered Maintenance (RCM) decision makers.  The methodology considers proposed maintenance actions on aggregate level metrics like engine availability, performance, and life cycle costs.

Current aircraft engine maintenance activities performed by the Air Force must account for both scheduled and unscheduled maintenance needs.  These maintenance activities, whether scheduled or unscheduled, are characterized as following an on-condition maintenance (OCM) strategy:  the maintenance work is performed to repair only what is broken or has already exceeded its time-on-wing (TOW) limits.

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Hybrid Discovery Wizard (HDWizard™)

HDWizard™ is a hybrid decision support toolkit that provides agent-based decision support for the automated generation of information from disparate and distributed data to support user-defined decision support goals.

Government and industry lack robust, hybridized approaches and methods for applying common sense reasoning techniques in decision support and knowledge management systems.  KBSI’s Hybrid Discovery Wizard (HDWizard™) project focused on developing a generic HDWizard™ toolkit that includes data mining, fusion, and inference/reasoning methods.

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Lean Value Chain (LVC)

The LVC initiative developed technologies and process improvements supporting critical part anticipation, solution presentation, and solution management oversight at the Corpus Christi Army Depot (CCAD) and Tinker Air Force Base.

The Lean Value Chain (LVC) initiative was a three-year program that supported the analysis of critical part management at the U.S. Army’s Corpus Christi Army Depot (CCAD) and Air Force’s Oklahoma City Air Logistics Center (OC-ALC).  The focus of KBSI’s work on the project included the implementation of technologies and process improvements in support of critical part anticipation, solution presentation, and solution management oversight.

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On Demand Simulation Support (ODSS)

ODSS is a system for creating and applying simulation modeling in depot management decision support.  ODSS uses a hybrid discrete-event/rule-based simulation engine, providing support for optimizing plans, schedules, situation response, and process designs.

The goal of this initiative was to design, build, and deploy an On Demand Simulation Support (ODSS) system prototype within the depot-MRO domain.  The ODSS prototype (referred to as the Virtual Planning Wizard – VPW), developed and tested using shared facility data from the paint and strip area at the Oklahoma City Air Logistics Center (OC-ALC), has demonstrated the effectiveness of the ODSS technology for the rapid creation and application of simulation modeling for depot management decision support.

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Reliability Centered Maintenance Scheduler (RCMS™)

The RCMS™ tool and methodology encapsulate a reliability-centered maintenance strategy that supports maintenance planning and scheduling.  The methodology and tool consider the overall effects of maintenance actions, or the risk of not performing those actions, on aggregate level metrics like engine availability, performance, and life cycle cost.

To meet their high standards of system reliability, the Air Force’s current engine maintenance practices involve frequent inspections, parts replacements, and re-working of the engines.  These frequent maintenance activities correspondingly require that the Air Force maintain a large inventory of spare engines and engine components.  How much of this cost is truly needed to ensure the same level of aircraft reliability and availability?

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Adaptive Trajectory Reshaping & Control System (ATRC)

The ATRC initiative developed real time solution techniques and algorithms for a reconfigurable control and guidance system for autonomous reusable launch vehicles (RLVs).  ATRC includes on-line parameter learning and real time reshaping of vehicle trajectories under uncertain damage/failure scenarios.

The U.S. Air Force, to keep pace with the demands of homeland security and global operations, is exploring methods for improved space utilization.  A significant impediment to increased space utilization is the huge cost of launching operations, and the Air Force is investigating more affordable launch operations via a number of Reusable Launch Vehicle (RLV) programs.  Part of the focus is on maintaining the economic viability of RLVs by enhancing operations safety and reliability;  i.e., to improve RLV capabilities for responding to various uncertainties and emerging situations.

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