In the field of computer vision, extraction and integration of appropriate features on surface shapes play important role of recognition processes. As such features, surface curvatures have been widely used since they reflect essential properties of 3-D objects. In this paper, we propose a method for estimating surface curvatures by means of surface fitting to range images. Furthermore, a clustering method using a Mixture Probability Algorithm is proposed for image segmentation, in which the surface is classified into regions with similar curvatures. As the approximating surface, B-spline surface with controllable knots is chosen, which can represent arbitrary 3-D surface shapes by arranging optimal knots. In order to realize the optimal knots arrangement, a knot control strategy based on recursive knot insertions, deletions and transfers is developed. The effectiveness of the proposed method has been confirmed through experiments using synthetic images and real range images.
A new quadratically stabilizing state feedback controller is derived based on the parameterization of H∞ controllers. The controller contains a contractive time-varying gain. The time-varying gain can be used to adjust the responses of the resulting closed-loop system. As one of the methods of using this adjustable gain, we consider a steepest descent tuning of Lyapunov functions. Particularly, this tuning method is also a new gain-scheduled control design when parameter variations are assumed to be available for feedback control
In this paper, we propose a new controller reduction method which considers maintaining the index value of H2-optimal controller. In the method, a frequency-weighted balanced realization technique developed by Enns is employed to obtain low order controllers. The fractional representation is used to reduce the order of unstable controllers effectively. To illustrate the effectiveness of our method, two numerical examples are presented.
This paper discusses how beliefs of a decision maker (DM) should be revised or updated for safety control of a large-complex plant. We evaluate the expected value of a loss in the plant which is caused by a safety-control action of the DM. There exist the following two kinds of mechanisms for belief revision : (i) combination rules for basic probability assignment functions, and (ii) updating rules based on the conditioning of belief functions. For each case, we give an optimal rule for minimizing the expected loss in the plant. The following three points are proven : (1) the “best” rule of belief revision for assuring plant safety does not always minimize the expected loss in the plant, (2) the optimality of a combination rule depends on the “type” of the safety-control policy (i.e., the safety-preservation type or the fault-warning type), and (3) the optimality of a conditioning rule, on the other hand, is irrespective of the type of the safety-control policy.
This paper considers digital control problems for continuous-time systems having symmetric transfer functions. It is supposed that digital controllers are implemented with zero-order hold, discrete-time controller, and ideal or integral sampler. The following results are obtained : (1) stabilization : transfer functions of discretized systems with both types of samplers keep symmetries of original continuous-time transfer functions. A condition for discrete-time stabilizing controllers to be symmetric is obtained. (2) discrete-time H2 control synthesis : discrete-time H2 optimal controllers for symmetric discrete-time generalized plants are symmetric. (3) sampled-data H2 control synthesis : for symmetric continuous-time generalized plants with integral samplers, sampled-data H2 optimal controllers are symmetric, while symmetries can not be utilized with ideal samplers.