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.
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.
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.
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.
The recent successes of the uninhabited aerial vehicle (UAV) in U.S. armed forces missions raise the possibility for increasingly diverse roles for UAVs in future operations: reconnaissance, surveillance, tracking, cooperative search and attack, relay communications, target identification, navigational guidance, and more.Continue reading
The AWSM™ technology represents a new paradigm for ship manufacturing that redesigns manufacturing processes and uses computing and wireless technologies to deliver information–activity statuses, resource availability, design and scheduling changes–to every user, work crew, or process involved in the project.
A central challenge in any large-scale manufacturing environment is to effectively adjust to production and procurement glitches that ripple across and continually threaten manufacturing schedules. The ship manufacturing industry is no exception. With manufacturing projects that stretch over years and involve numerous divisions, materials, facilities, and manpower, how can U.S. shipyards achieve the kind of proactive flexibility needed to develop and maintain the most efficient and cost-effective production schedules?
The TraceLogic initiative is developing methods, processes, and algorithms to decipher the hidden rules or logic of complex flight operations aboard Navy aircraft carriers. The TraceLogic technology will help the Navy to better understand and address the technical and pragmatic problems associated with improving flight operation performance.
Operations on aircraft carriers have been described as “controlled chaos” that involve a complex, choreographed mix of flight-mission preparations, launch, recovery, and mission close-out operations. Critical activities take place on the hangar deck, the flight deck, and in the control center and these activities are performed by personnel with distinct roles, using mobile and fixed equipment, ordnance, and fuel. Missions are often in flux, and a single equipment failure can throw the entire plan of action into a tailspin.
KBSI investigated and demonstrated the conversion of CALS Type I raster mechanical drawings to parametric vector format and to parametric 3D solid models, improving electronic commerce, competitive reprocurement, and maintenance for related systems.
This initiative investigated and demonstrated the conversion of CALS Type I raster mechanical drawings to parametric vector format and to parametric 3D solid models by leveraging KBSI’s “Vector to 3D” (3VD) technology. Raster drawings consume storage space and network bandwidth, have a finite resolution, often suffer from poor image quality, and are expensive to maintain and edit. These limitations impact electronic commerce, competitive reprocurement, and maintenance.
MiNPAC allows analysts to discover emerging themes, trends, patterns of beliefs, sentiments, etc. from multi-source data and correlate these findings with structural and behavioral dynamics within the same or related social networks. This capability improves situational awareness, helping planners formulate more responsive COA plans to meet rapidly evolving, asymmetric threats.
Modern asymmetric warfare involves operational and strategic decision making under high levels of uncertainty. For course of action (COA) planning to be effective, decision makers must contend with the state of flux—the enemy’s shifting organizational patterns and methods of communication, growth and indoctrination, levels of preparation, policy shifts, advances in capability, new technology acquisition, deployment methods, etc.—that is the hallmark of modern warfare. Most pressing, and most challenging, is the need for accurate situational awareness at many levels, particularly in understanding a complex web of dynamic elements that includes demographic, political, military, economic, social, information, and infrastructure dimensions.
The FAST™ technology is a simulation-based training system that allows users to create scenario-based training content for better managing airspace and deconfliction in manned and RPA systems. The scenarios are stored in a FAST™ scenario library for reuse or can be stored as templates for adapting and evolving scenarios to meet specific training needs.
The increasing use of Remotely Piloted Aircraft (RPA) systems is complicating airspace management; in addition, these small, stealthy, and highly maneuverable RPA systems are difficult for conventional airspace-management technology to effectively manage. Training for RPA operators has also failed to match the pace of RPA development and generally fails to address methods and techniques for managing airspace in the presence of RPA, further raising safety risks. Improved training for RPA operators, the ability of develop an operating picture in common, and effective deconfliction methods and techniques will be significant steps in making mixed-use airspace effective and safe.