| Building the LeM2*R3 Model of Pilot Trust and Dynamic Workload Allocation. A Transition of Theory and Empirical Observations to Cockpit Demonstration |
FEB 1998 |
102 pages |
| Authors:
Peter G. Raeth; John M. Reising; AIR FORCE RESEARCH LAB WRIGHT-PATTERSON AFB OH HUMAN EFFECTIVENESS DIRECTORATE
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 | For pilots to accept active decision aids during complex flight scenarios, it is essential that the automation work is in synergy with aircrew. To accomplish this, the automation must go well beyond menu and macro selections, where the pilot must explicitly tell the automation what to do and when to do it. It must also transcend "mother may I" approaches, where the automation asks ... |
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| Scaleable Expert Systems for Adding Crisp Knowledge to Pilot-Vehicle Interfaces |
MAY 96 |
98 pages |
| Authors:
Peter G. Raeth; Anthony J. Montecalvo; James L. Noyes; WRIGHT LAB WRIGHT-PATTERSON AFB OH
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| Vehicle systems operations, from automobiles, to airliners, through craft for sea and space, are heavily dependent on automated uses of data generated by both on-board and off-board sources. Not only are vehicles affected, but so are diverse systems such as process, manufacturing, and power generation plants. Historically, the volume of data in each instance keeps growing because of such factors as task and system complexity. Other factors are environmental, accuracy, ... |
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| An Application of Exponential Neural Networks to Event-Train Recognition |
08 MAY 92 |
104 pages |
| Authors:
Peter G. Raeth; WRIGHT LAB WRIGHT-PATTERSON AFB OH
|
| The purpose of this project is to investigate neural networks for specific applications in passive electronic warfare (EW) involving restoration of deinterleaved pulse trains to their original broadcast form. The project took a generic event-train approach and focused on event-train recognition. It was determined that backpropagation neural networks did not represent a logistically supportable means of training. Gaussian radial basis functions were found to be far superior. This report is ... |
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| Stretch and Hammer Neural Networks for N-Dimensional Data Generalization |
15 JAN 92 |
102 pages |
| Authors:
Peter G. Raeth; Steven C. Gustafson; Gordon R. Little; Todd S. Puterbaugh; WRIGHT LAB WRIGHT-PATTERSON AFB OH
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| A hypersurface stretch and hammer neural network has been developed that generalize data from processes that have one output variable and one or more input variables. This network achieves several desirable properties through a novel combination of standard methods. The methods incorporate principal components, linear least squares, Gaussian radial basis functions, and diagonnally dominant matrices. An easily visualized physical model of network function ensures that the combination of methods is ... |
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| Event-Train Restoration Via Backpropagation Neural Networks |
DEC 89 |
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| Authors:
Peter G. Raeth; WRIGHT RESEARCH AND DEVELOPMENT CENTER WRIGHT-PATTERSON AFB OH AVIONICS LAB
|
 | This project is investigating backpropagation neural networks for specific applications in passive electronic warfare involving restoration of deinterleaved event trains to their original broadcast form. This is different from traditional bit-error detection/correction which relies on a prior knowledge of what the original bit stream looked liked. In electronic warfare it is unlikely that such prior knowledge will be available. Results of this research can be applied to 3 major problem ... |
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| A Functional Level Preprocessor for Computer Aided Digital Design |
DEC 1980 |
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| Authors:
Peter G. Raeth; AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING
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| While good gate level and register transfer level digital simulators exist, one can not easily integrate the two due to their inherent limitations. A given simulation can not be described partially in gate level and partially in a higher level. A solution is to create a functional level preprocessor and a library of functional device models linked to a gate level simulator's input language. This permits the mixing of behavioral ... |
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