Organization:
SEED Center for Data Farming (Simulation Experiments & Efficient Designs)

Series

Proceedings and Bulletin of the International Data Farming Community

Description

SEED advances research and application of efficient experimental designs to simulation studies at the Naval Postgraduate School. Our overriding approach has been to advance the state-of-the-art in conducting large-scale simulation studies, by developing and disseminating experimental designs that facilitate the exploration of complex simulation models. Ongoing application areas include studies on peacekeeping, technical and human aspects of warfare, adaptive asymmetric adversaries, homeland security, and networked future forces.

Website of the organization

https://nps.edu/web/seed

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Publication Search Results

Now showing 1 - 10 of 127

Team 6: Enhanced Design of Experiment for Testing in a Joint Environment
(2008-04) Beach, Timothy; Dryer, Dave; Fiebrandt, Mark; Short, Maureen; Sciarretta, Al; Kelton, David; Sanchez, Susan; Upton, Steve; Shamburg, Jeff; Tollefson, Eric; Alt, Jonathan; Donnelly, Thomas; Clites, Lawton; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
The intent of Team 6 activities at the International Data Farming Workshop (IDFW) 16 was to explore enhanced design of experiment (DOE) techniques and models relevant to developing evaluation strategies for testing in a joint environment (TIJE). This goal was met through the utilization of the Map Aware Non-uniform Automata (MANA) model to trace a “call for fire” (CFF) from the originator to the final weapon system, at the detailed level of an individual task thread. A capability-level evaluation strategy for battlespace deconfliction tasks was used as the scenario driver for the data farming runs. This evaluation strategy has been developed as part of the Joint Test and Evaluation Methodology (JTEM) project.
Team 9: Healthcare Applications of Data Farming
(2007-03) Cornell, Paul; Paterson, Jennifer; Young, Nancy; Chites, Lawton; Wan, Hong; Kang, Keebom; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
Healthcare in the United States is expensive and inefficient. As a whole, it is at least ten years behind other industries in the application of information technology to processes and practices. Hospital administrators, with a cadre of consultants and vendors in tow, are rushing to catch up, spending billions on IT. Unfortunately, process knowledge is often lacking, and technology interventions fail to achieve their goals. This contributes to the low rate of adoption—less than 10 percent—of tools such as electronic medical records.
Team 5: System-of-Systems Test Planning
(2007-03) Wegner, Chris; Upton, Steve; Raffetto, Mark; Srivastava, Niraj; Oh, Regine; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
Joint operations have become the mainstay of warfighting. Force Transformation requires the Test and Evaluation (T&E) community to place a much greater emphasis on testing joint warfighting capabilities. A unique challenge in assessing the effectiveness and suitability of systems in the joint environment is the multitude of possible interactions and outcomes in a system-of-systems construct. New and developing acquisition programs rely on interfaces with existing or future systems, quite possibly from separate services, to achieve mission success. Because of resource constraints and the complexity of conducting live, virtual, and constructive testing in a joint mission environment, the Joint Test and Evaluation Methodology (JTEM) program is interested in determining if analytical techniques, like Modeling and Simulation (M&S), can be applied to understand the relationship between system-of-systems performance and joint mission effectiveness.
Using NetLogo in the Data Farming Environment
(2006-11) Koehler, Matthew; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
NetLogo is a freely available agent-based modeling environment being developed by Northwestern University’s Center for Connected Learning (ccl.northwestern.edu/ netlogø). NetLogo is an excellent environment for creating simpler or smaller-scale agent-based models or prototyping more complex models. NetLogo’s strengths include using a very easy to learn and flexible scripting environment, a GUI interface that handles all the necessary code for you, and a section dedicated to documenting your model, and a very large sample model library with very good documentation. The down side of NetLogo is that you must create all functionality you desire to have in the model, which can be time consuming if you have a great deal of complicated behaviors. Furthermore, NetLogo is written in Java and its scripting language is only semi-compiled (some primitives are compiled into Java byte-code, other primitives are interpreted), which can lead to some performance issues if your models is very large or involves a great deal of computation. Finally, NetLogo is compatible with the Data Farming and cluster computing methods and tools created by Project Albert and its collaborators.
Team 4: Agent-Based Sensor-Effector Modeling
(2008-04) Haymann, Karsten; Nitsch, Daniel; Mertens, Andreas; Sciarretta, Al; Meyer, Ted; Schwierz, Klaus-Peter; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
Team Proposal: During previous Data Farming workshops, different representatives of the German Federal Office of Defense Technology and Procurement and EADS were concentrating on the simulation of technical aspects in network centric operations (NCO). In the respective working groups, the main focus was on analyzing the influence of networked sensors and effectors on military capabilities and the operational outcome.
Team 3: Operational Synthesis Approach for the Analysis of Peace Support Operations
(2009-03) Kunde, Dietmar; Schwartz, Gunther; Wagner, Gudrun; Stemate, Luminita; Chong, Ng Ee; Khiang, Leo Yong; Chen, Dan; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
Team 1: Small Unmanned Ground Vehicles (SUGV): Contribution to Small Combat Unit Combat Effectiveness
(2008-04) Geren, Richard; Richkowski, David; Foo, Kong Pin; McDonald, Mary; Pearman, Jerry; Donnelly, Thomas; Vance, Richard; Kiesling, Tobias; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
As part of the Army’s current transformation, robots are being integrated into force structure to reduce human risk. These mechanical “battle buddies” are being used for a myriad of tasks; however, there are currently no established standards for measuring and evaluating their contribution to force combat effectiveness. This research attempts to establish some metrics using essential elements of analysis (EEA), a SUGV functional decomposition hierarchy, Measures of Effectiveness (MOEs) and Measures of Performance (MOPs). Using these metrics, we will determine if the increased situational awareness provided by SUGVs and attached sensors improves combat effectiveness and mission accomplishment. The primary SUGV functional capabilities (Figure 1) modeled were Gain Information (detect/identify agents), Move (speed), Survive (vulnerability), and Employ Effects (sensor ranges). The MOEs measured were the number of friendly forces killed (separating SUGVs and Soldiers), the number of enemy forces killed, and the overall combat effectiveness of the Small Combat Unit (SCU). Pythagoras, an agent-based modeling program was used to develop the simulation. The scenario was based on a dismounted infantry platoon conducting building clearing operations as part of a larger company level cordon and search mission in an urban environment. For comparison, excursions either included one SUGV or none. It is our hope that results obtained will be beneficial to the U.S. armed forces for subsequent research or implementation into any military tactics, techniques, or procedures (TTPs) involving our new “battle buddies”.
Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 3 Workshop 15
(2007-11) Hingston, Philip; Stemate, Luminita; Kunde, Dietmar; Spaans, Mink; Seng, Choo Chwee; Chee, Yee Kah; Dean, David; Horne, Gary; Sanchez, Susan; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Meyer, Ted; Horne, Gary
Team 7: Applying Automated Red Teaming in an Urban Ops Scenario
(2006-11) Lee, M.; Ang, D.; Huee, L. Fung; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
With rapid urbanisation, troops today will have to operate in an increasingly complex and urbanised environment. Together with a more potent enemy capability, the troops will have to be highly armour protected even at the lowest level (company size) in order to minimise the casualty rate. The fighting force will need to be a combined force to achieve a swift and decisive result in an urbanised terrain. This study explored the Coy level urban fighting force packages operating in a built up area.
Team 2: Littoral Combat Ship (LCS) Mission Packages: Determining the Best Mix
(2008-04) Abbott, Benjamin; Kaiser, Chad; Milliken, Mike; Atamian, Michael; SEED Center for Data Farming (Simulation Experiments & Efficient Designs)
The threat facing the U.S. Navy is changing from engagement in blue water to combat in the littorals. In order to meet this threat, the U.S. Navy built the Littoral Combat Ship (LCS) — a high speed, shallow draft, focused-mission platform capable of operating independently, as a squadron, or as part of a Carrier/Expeditionary Strike Group (CSG/ ESG).1 As with every new platform, many questions regarding the employment of LCS are still unanswered. How many LCS should comprise a squadron?