Estimation of time-varying delay time in nonstationary linearsystems: an approach to monitor human operator adaptation in manualtracking tasks

Adaptability is one of man's advantages over machines. Perhaps one of the reasons for our limited understanding about human adaptation during manual tracking tasks is that we have only limited tools to identify the model coefficients (especially delay time) of an adapting human operator. In this paper, we introduce a discrete time recursive delay identifier (RDI) capable of simultaneously estimating a human operator's nonstationary delay time and linear model coefficients. At its core lies the extended Kalman filter (EKF). Our goal to obtain fractional delay time estimates was realized by using the bicubic interpolation scheme as part of the EKF to provide subsample magnitude and derivative estimates of the observed input/output time series. While this theoretically limits the RDI applicability to band-limited or differentiable signals, this is seldom a concern in practice. Based on data from simulated and experimental time varying tracking tasks, we show the RDI's potential to substantially increase our understanding about human adaptations thus perhaps offering new avenues for machine adaptation     

An algorithm for combined cell-site selection and power control tomaximize cellular spread spectrum capacity

There is much current interest in spread spectrum wireless mobile communications and in particular the issue of spread spectrum wireless capacity. We characterize spread spectrum cellular capacity and provide a combined power control, cell-site selection algorithm that enables this capacity to be achieved. The algorithm adapts users' transmitter power levels and switches them between cell-sites, and it is shown that the algorithm converges to an allocation of users to cells that is optimal in the sense that interference is minimized. The algorithm is decentralized, and can be considered as a mechanism for cell-site diversity and handover. We provide numerical examples to show how effectively the algorithm relieves local network congestion, by switching users in a heavily congested cell to adjacent, less congested cells     

Uncertainty of discrete stochastic systems: general theory andstatistical inference

Uncertainty is defined in a new manner, as a function of discrete probability distributions satisfying a simple and intuitively appealing weak monotonicity condition. It is shown that every uncertainty is Schur-concave and conversely, every Schur-concave function of distributions is an uncertainty. General properties of uncertainties are systematically studied. Many characteristics of distributions introduced previously in statistical physics, mathematical statistics, econometrics and information theory are shown to be particular examples of uncertainties. New examples are introduced, and traditional as well as some new methods for obtaining uncertainties are discussed. The information defined by decrease of uncertainty resulting from an observation is investigated and related to previous concepts of information. Further, statistical inference about uncertainties is investigated, based on independent observations of system states. In particular, asymptotic distributions of maximum likelihood estimates of uncertainties and uncertainty-related functions are derived, and asymptotically α-level Neyman-Pearson tests of hypotheses about these system characteristics are presented     

Explosion-resistance of the cylindrical part of a fast-reactor vessel

All-Russia Scientific-Research Institute of Electrophysical Apparatus. Special Office of Machine Design. Institute of High Temperatures, Russian Academy of Sciences. Translated from Atomnaya énergiya, Vol. 79, No. 3, pp. 178–188, September, 1995.     

Comparison of computational results obtained with different programs for radiation transfer

Main Science Center of the Russian Federation — Institute of High-Energy Physics. Translated from Atomnaya énergiya, Vol. 79, No. 4, pp. 269–279, October, 1995.     

Multiagent reasoning with probability,time, and beliefs

Any agent interacting with the real world must be able to reason about uncertainty in the world, about the actions that may occur in the world (either due to the agent or those initiated by other agents), about the (probabilistic) beliefs of other agents, and how these (probabilistic) beliefs are changing over time. In this article, we develop a family of logics that a reasoning agent may use to perform successively more sophisticated types of reasoning in such environments. We also characterize different types of agents. Furthermore, we provide a logic that enables a systems designer (who may have populated an environment with a collection of such autonomous agents) to reason about the system of agents as a whole. © 1995 John Wiley & Sons, Inc.     

Guaranteeing the skill level of specialists in the nuclear industry under conditions of the restructuring and dissolution of the USSR

Abstracts of report.