This paper proposes a method for measuring the position and heading of the vehicle by use of laser and corner cubes. The vehicle has dead reckoning system. The method uses the laser transmitter and receiver on the vehicle and retroreflecting targets on a side of the course. The on-board computer calculates the position and heading from the dead reckoning data when the laser beams hit the corner cube. Each laser beam is set with a certain angle and an offset distance. And the dead reckoning data are stored in memories when the laser beams return to the transmitter. After corner cubes are detected by retroreflected laser beam, position and heading are calculated. The measured data are used for compensating the autonomous position determination.
In order to improve the boiler start-up performance including flexibility in schedules, management of the fatigue damage at pressured parts and reduction in the operational costs, an advanced controller is proposed. It consists of a “demand calculator for the steam pressure and temperature changing rate, considering the schedules and the fatigue damage” and a “fuel-minimum controller for the demanded changing rate applying an inverse model of the physical phenomena in the plant”. In this report, derivation of the inverse model and the controlling logic as well as a performance comparison on an actual 600MW power plant circumstance with the prior art are introduced.
In most MRF-based Bayesian restoration algorithms, the image is modeled by a single MRF. However, an MRF is a proper model only for simple images such as piecewise constant or homogeneous ones having the same statistics over the entire image. This hamper the applicability of these algorithms to more complex images. To overcome this shortcoming, we employ a hierarchical triply stochastic process to model the observed image and develop an iterative algorithm for the restoration of images with region-dependent statistics. The algorithm is developed for two cases of binary and gray level images degraded by flip noise and Gaussian white noise, respectively. No prior knowledge of the noise parameters or the parameters of two hidden processes that model the true image is assumed. The algorithm is data-driven except for the number of regions which is assumed known. The proposed algorithm also provides a segmentation of the observed noisy image as a byproduct. Some simulation examples showing the effectiveness of the algorithm are presented.
This paper presents an alternative characterization of reduced-order controllers via LMI approach. First we give necessary and sufficient conditions for the existence of a reduced-order controller which may be suitable for multi-purpose controller design compared to former ones. And we characterize the set of all reduced-order controllers explicitly. Second we discuss the relation between our results and former ones. Finally we characterize the reduced-order H2 controller in a similar way, and give a numerical example.
This paper is concerned with system identification for robust control in the frequency domain. We propose a new method for identification of both the nominal plant model and its additive uncertainty bound. The method produces the model that minimizes theL∞norm of the modeling error using the set-membership method. A numerical example is given to demonstrate the validity of the proposed identification method.