Optimal bounded control of first-passage failure of quasi-integrable Hamiltonian systems with wide-band random excitation

The optimal bounded control of quasi-integrable Hamiltonian systems with wide-band random excitation for minimizing their first-passage failure is investigated. First, a stochastic averaging method for multi-degrees-of-freedom (MDOF) strongly nonlinear quasi-integrable Hamiltonian systems with wide-band stationary random excitations using generalized harmonic functions is proposed. Then, the dynamical programming equations and their associated boundary and final time conditions for the control problems of maximizinig reliability and maximizing mean first-passage time are formulated based on the averaged It$\ddot{\rm o}$ equations by applying the dynamical programming principle. The optimal control law is derived from the dynamical programming equations and control constraints. The relationship between the dynamical programming equations and the backward Kolmogorov equation for the conditional reliability function and the Pontryagin equation for the conditional mean first-passage time of optimally controlled system is discussed. Finally, the conditional reliability function, the conditional probability density and mean of first-passage time of an optimally controlled system are obtained by solving the backward Kolmogorov equation and Pontryagin equation. The application of the proposed procedure and effectiveness of control strategy are illustrated with an example.

Optimal bounded control of first-passage failure of strongly non-linear oscillators under combined harmonic and white-noise excitations

A procedure for designing the optimal bounded control of strongly non-linear oscillators under combined harmonic and white-noise excitations for minimizing their first-passage failure is proposed. First, a stochastic averaging method for strongly non-linear oscillators under combined harmonic and white-noise excitations using generalized harmonic functions is introduced. Then, the dynamical programming equations and their boundary and final time conditions for the control problems of maximizing reliability and of maximizing mean first-passage time are formulated from the averaged Ito equations by using the dynamical programming principle. The optimal control law is derived from the dynamical programming equations and control constraint. Finally, the conditional reliability function, the conditional probability density and mean of the first-passage time of the optimally controlled system are obtained from solving the backward Kolmogorov equation and Pontryagin equation. An example is given to illustrate the proposed procedure and the results obtained are verified by using those from digital simulation. (C) 2003 Elsevier Ltd. All rights reserved.

Spatiotemporal segmentation based on two-dimensional spatiotemporal entropic thresholding

A novel spatiotemporal segmentation technique is further developed for extracting uncovered background and moving objects from the image sequences, then the following motion estimation is performed only on the regions corresponding to moving objects. The frame difference contrast (FCON) and local variance contrast (LCON), which are related to the temporal and spatial homogeneity of the image sequence, are selected to form the 2-D spatiotemporal entropy. Then the spatial segmentation threshold is determined by maximizing the 2-D spatiotemporal entropy, and the temporal segmentation point is selected to minimize the complexity measure for image sequence coding. Since both temporal and spatial correlation of an image sequence are exploited, this proposed spatiotemporal segmentation technique can further be used to determine the positions of reference frames adaptively, hence resulting in a low bit rate. Experimental results show that this segmentation-based coding scheme is more efficient than usual fixed-size coding algorithms. (C) 1997 Society of Photo-Optical Instrumentation Engineers.

DISTRIBUTION FUNCTION OF A DRIFTING ELECTRON-GAS FOR GENERAL ENERGY-BAND STRUCTURES

The usual application of the Lei-Ting balance equation method for treating electron transport problems makes use of a Fermi distribution function for the electron motion relative to the center of mass. It is pointed out that this presumes the existence of a moving frame of reference that is dynamically equivalent to the rest frame of reference, and this is only true for electrons with a constant effective mass. The method is thus inapplicable to problems where electrons governed by a general energy-band dispersion E(k) are important (such as in miniband conduction). It is demonstrated that this difficulty can be overcome by introducing a distribution function for a drifting electron gas by maximizing the entropy subject to a prescribed average drift velocity. The distribution function reduces directly to the usual Fermi distribution for electron motion relative to the center of mass in the special case of E(k)=($) over bar h(2)\k\(2)/2m*. This maximum entropy treatment of a drifting electron gas provides a physically more direct as well as a more general basis for the application of the balance equation method.

Spectral response of winter wheat to crop development and various agronomic limitations

For maximizing the effective applications of remote sensing in crop recognition, crop performance assessment and canopy variables estimation at large areas, it is essential to fully understand the spectral response of canopy to crop development and varying growing conditions. In this paper, the spectral properties of winter wheat canopy under different growth stages and different agronomic conditions were investigated at the field level based on reflectance measurements. It was proved that crop growth and development, nitrogen fertilization rates, nutrient deficit (e.g. lacking any kind of nitrogen, phosphorus and kalium fertilizer or lacking all of them), irrigation frequency and plant density had direct influence on canopy reflectance in 400-900 nm which including the visible/near infrared bands, and resulted in great changes of spectral curves. It was suggested that spectral reflectance of crop canopy can well reflect the growth and development of crop and the impacts from various factors, and was feasible to provide vital information for crop monitoring and assessment. ©2010 IEEE.

从赌博游戏中的损失厌恶行为看情感预测与体验在决策中的作用

In Kermer, Driver-Linn, Wilson and Gilbert’s (2006) study on affective forecast, they found that people have a tendency to overestimate affective reactions in gains and losses, and people expect losses to have greater hedonic impact than gains of equal magnitude. Because of thus affective forecasting error, people prefer to irrationally avoid losses. Loss aversion is then seen as both a wealth-maximizing error and an affect-maximizing error. The present study examined the relationships among affective forecast, affective experience and loss aversion, and tested Kermer et al.’s (2006) conclusion that people’s loss aversion is an affective forecasting error. In experiment 1, we examined the relationship between affective forecast and loss aversion. Kermer et al.’s (2006) hypothesized that when people expect losses to have greater hedonic impact than gains, they will accept the gambling task, and when people expect gains to have greater hedonic impact than losses, they will refuse the gambling task. We found that (1) individuals with lower loss aversion had a greater tendency to accept a gambling task than those with higher loss aversion; (2) individuals with lower loss aversion expected losses and gains to have smaller affective impacts than those with higher loss aversion. Thus, people never exactly calculated their forecasting affective. In experiment 2, we examined the relationship between affective forecast and affective experience. Consistent with Kermer et al.’s (2006) finding, we found that our participants tended to overestimate affective reactions in gains as well as losses. More interestingly, Kermer et al.’s (2006) found that participants’ predictions for a loss were significantly more distant from experienced emotions than were their predictions for a win, we, however, found the opposite —participants’ predictions for a win were significantly more distant from the experienced emotions than were their predictions for a loss. These experiments further validated the relations between affection and decision making, and contributed to our understanding on the affective reactions to future events. Our study imply that it was not the exact calculation of affective forecast on decision outcomes, but rather the magnitude of affection on outcomes, that influenced people’s affective decision making. It indicated that those with lower magnitude of affection would less like to avoid losses, and thus more like to accept a gambling task.