Modern microprocessor technology and display systems make it entirely feasible to automate maay flight-deck functions previously performed Some automation-related aircraft accidents and incid_,Ls are discussed as examples of human factors problems in automated flight.

Previous research in the optimal allocation of airline seats has followed one of two themes: marginal seat revenue or mathematical programming. Both approaches capture important elements of the revenue management problem. The marginal seat revenue approach accounts for the “nesting” of fare classes in computer reservation systems, but can only control seat inventory by bookings on legs. The mathematical programming approach will handle realistically large problems and will account for multiple origin–destination itineraries and side constraints, but it does not account for fare class nesting in the reservation systems. This paper combines both approaches by developing equations to find the optimal allocation of seats when fare classes are nested on an origin–destination itinerary and the inventory is not shared among origin–destinations. These results are applicable to seat allocation in certain reservations systems, point-of-sale control, and acceptance of groups over their entire itinerary. A special case of the analysis produces the optimal booking limits for leg-based seat allocation with nested fare classes.

A model is presented to predict human dynamic spatial orientation in response to multisensory stimuli. Motion stimuli are first processed by dynamic models of the visual, vestibular, tactile, and proprioceptive sensors. Central nervous system function is modeled as a steady state Kalman filter that optimally blends information from the various sensors to form an estimate of spatial orientation. Where necessary, nonlinear elements preprocess inputs to the linear central estimator in order to reflect more accurately some nonlinear human response characteristics. Computer implementation of the model has shown agreement with several important qualitative characteristics of human spatial orientation.

No abstract

State Estimation Using Quantized Measurements-ScienceDirect Estimation and control with quantized measurements ? by Renwick E. Curry. Author. Curry, Renwick E. Published. Cambridge, Mass.: M.I.T. Press, 1970. Estimation and control with quantized measurements-IEEE Xplore The Kalman Like Particle Filter: Optimal Estimation with Quantized. An upper bound of mean-square error in state estimation with. joint maximum a posteriori estimation. The latter approach is simplified and evaluated experimentally on a moving cart with quantized position measurements The Kalman Like Particle Filter: Optimal Estimation With Quantized. eBook Estimation and Control with Quantized Measurements download online audio id:52roxb6. eBook Estimation and Control with Quantized Measurements Input design in worst-case system identification with quantized. The problem of estimation with quantized measurements is almost as old as the. Without any further. Joint 48th IEEE Conference on Decision and Control and. Estimation and control with quantized measurements by Renwick. 9 May 2018. The Institute of Measurement and Control. 1.579. Keywords State estimation, quantization, upper bound, Riccati equation, convergence typical aircraft altitude tracking scenario in air traffic control. ATC systems are presented.