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Mine Burial Expert System for Change of MIW Doctrine

Mine Burial Expert System for Change of MIW Doctrine in Franklin, TN

Current price: $12.95
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Mine Burial Expert System for Change of MIW Doctrine

Barnes and Noble

Mine Burial Expert System for Change of MIW Doctrine in Franklin, TN

Current price: $12.95
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Mine impact burial models such as IMPACT25, IMPACT28, and IMPACT35 have been used in the MIW community in an attempt to calculate the percentage of impact burial for sea mines. Until recently the models have been deterministic, using parameters such as sediment type, air and sea trajectories, drop angle, and mine type to calculate the percentage of burial. These models have been relatively effective in calculating impact burial, but little attention has been given to the temporal effects on mine burial, known as scour burial. Another shortfall of the deterministic modeling approach is the inability to capture the stochastic nature of the input parameters. To address these issues the John Hopkins University" Applied Physics Laboratory (JHU-APL), in conjunction with the NRL has developed the Mine Burial Expert System (MBES). The MBES is a Bayesian network of physics based, deterministic models, observational data, and expert opinion. It provides the opportunity to give input parameters as probability density tables (PDTs) and receive a burial percentage as an output distribution. This allows its user to capture the variability of input parameters and converge them into variability in the burial prediction, providing valuable risk data to the mine countermeasure (MCM) Commander. The MBES has been incorporated into the Environmental Post Mission Analysis (EPMA) tool for Naval Oceanographic Office (NAVO), which could give the MCM planners an idea of the confidence level of its predictions. To understand how the variability and confidence levels can be used and how it may affect current doctrine, a series of tests have been run through the MBES. A thorough review of the results can have a significant effect on future use of the system and subsequent changes to MIW doctrine. In particular, current doctrinal sediment categories are not sufficient in capturing the resolution of the MBES predictions.
Mine impact burial models such as IMPACT25, IMPACT28, and IMPACT35 have been used in the MIW community in an attempt to calculate the percentage of impact burial for sea mines. Until recently the models have been deterministic, using parameters such as sediment type, air and sea trajectories, drop angle, and mine type to calculate the percentage of burial. These models have been relatively effective in calculating impact burial, but little attention has been given to the temporal effects on mine burial, known as scour burial. Another shortfall of the deterministic modeling approach is the inability to capture the stochastic nature of the input parameters. To address these issues the John Hopkins University" Applied Physics Laboratory (JHU-APL), in conjunction with the NRL has developed the Mine Burial Expert System (MBES). The MBES is a Bayesian network of physics based, deterministic models, observational data, and expert opinion. It provides the opportunity to give input parameters as probability density tables (PDTs) and receive a burial percentage as an output distribution. This allows its user to capture the variability of input parameters and converge them into variability in the burial prediction, providing valuable risk data to the mine countermeasure (MCM) Commander. The MBES has been incorporated into the Environmental Post Mission Analysis (EPMA) tool for Naval Oceanographic Office (NAVO), which could give the MCM planners an idea of the confidence level of its predictions. To understand how the variability and confidence levels can be used and how it may affect current doctrine, a series of tests have been run through the MBES. A thorough review of the results can have a significant effect on future use of the system and subsequent changes to MIW doctrine. In particular, current doctrinal sediment categories are not sufficient in capturing the resolution of the MBES predictions.

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