In this note we present a formulation of two related combinatorial embedding problems concerning level graphs in terms of CNF-formulas. The first problem is known as level planar embedding and the second as crossing-m...
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In this note we present a formulation of two related combinatorial embedding problems concerning level graphs in terms of CNF-formulas. The first problem is known as level planar embedding and the second as crossing-minimization-problem.
The problem of robust H∞ state-feedback control design for a class of continuous-time linear systems with Markovian jumping parameters and multiple time-varying delays at the states, and subject to uncertain paramete...
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Real-time 6D-trackers based on electromagnetic inductance sensing have previously employed 3-axis coaxial direct current coils for both transmission and reception. Here, with the objective of a low-cost, practical sys...
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ISBN:
(纸本)0769509517
Real-time 6D-trackers based on electromagnetic inductance sensing have previously employed 3-axis coaxial direct current coils for both transmission and reception. Here, with the objective of a low-cost, practical system, we discuss a position-calculating algorithm for an electromagnetic inductance real-time 6D-tracker that employs two 1-axis coils and one 3-axis coil and describe the construction of a prototype system. This system has the potential of realizing a real-time 6D-tracker that costs several hundred dollars. It is expected that the measurement range and accuracy will be improved by increasing the number of transmitting coils, and we think it useful making 3D virtual common space by network at low-cost. We show the experimental system of an electronic white board.
PID controllers have been widely used in many chemical processes. Because they have only three control parameters and their physical meanings can be easily grasped. However, it is difficult to tune those parameters pr...
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PID controllers have been widely used in many chemical processes. Because they have only three control parameters and their physical meanings can be easily grasped. However, it is difficult to tune those parameters practically, since the process dynamics often change due to operating conditions or various disturbances. For this problem, a design method of robust PID controllers has been already proposed by the authors in order to guarantee the stability of the control system. However, as that control method is conservative, the desirable setpoint response can not be always obtained. In this paper, a design scheme of a self-tuning pre-filter is proposed to supplement the robust PID controller based on the two-degree-of-freedom control scheme. According to the proposed scheme, the transient property for the setpoint response can be improved keeping the robust stability. Finally, the proposed scheme is experimentally evaluated on an air pressure control system.
In this paper a three dimensional (3D) image processing expert system called 3D-IMPRESS is presented. This system can automatically construct a 3D image processing procedure by using pairs of an original input image a...
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ISBN:
(纸本)0769514022
In this paper a three dimensional (3D) image processing expert system called 3D-IMPRESS is presented. This system can automatically construct a 3D image processing procedure by using pairs of an original input image and a desired output figure called sample figure given by a user This paper describes the outline of 3D-IMPRESS and presents a method of procedure consolidation for generating a procedure commonly applicable to some other 3D gray images. Finally, we show segmentation results of 3D chest CT images by using the procedures generated by 3D-IMPRESS and discuss the performance of the system.
Much research on designing self-tuning control systems for linear systems have been proposed by using the least squares parameter identification method. However, it is difficult to employ the algorithm for the nonline...
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Much research on designing self-tuning control systems for linear systems have been proposed by using the least squares parameter identification method. However, it is difficult to employ the algorithm for the nonlinear systems except for the case where the unknown parameters are linearly combined with nonlinear terms. In this paper, a parameter estimation scheme for nonlinear systems is proposed by using a neural network. Furthermore, a design method of the control system is derived by minimizing a cost function of the generalized minimum variance control. This control input is calculated by using the estimated parameters. Finally, the effectiveness of the proposed scheme is numerically evaluated.
This paper addresses the problem of robust H ∞ state-feedback control design for uncertain discrete-time linear systems with multiple time delays at the states and Markovian jumping parameters. The jumping parameters...
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This paper addresses the problem of robust H ∞ state-feedback control design for uncertain discrete-time linear systems with multiple time delays at the states and Markovian jumping parameters. The jumping parameters are assumed to be available and the uncertainties are supposed to belong to convex bounded domains (polytope type uncertainty). Delay-independent sufficient conditions assuring robust stochastic stability and a prescribed H ∞ disturbance attenuation for the closed-loop uncertain discrete-time linear system with multiple time delays and Markovian jumping parameters are established in terms of linear matrix inequalities, which have the advantage that can be implemented numerically veryefficiently.
In this paper, we propose a multi-channel dissemination system with a clustering mechanism and a presentation technique for time- series on-line news articles on the Internet. We describe a detecting technique for art...
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In this paper, we extend the language we proposed in [11] to make it possible to program are-consistency algorithms. We also propose a hybrid algorithm that integrates the interval-consistency (IC) and arc-consistency...
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Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor i...
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Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor in which an elastic ring is attracted by points in a bi-dimensional space while their internal elastic forces try to shun the elastic expansion. The different weights associated with these two kinds of forces lead the elastic to a gradual expansion in the direction of the bi-dimensional points. In this method, the elastic net algorithm is employed with the help of a heuristic framework that improves its performance for application in the n-dimensional space of cluster analysis. Tests were made with two types of data sets: (1) simulated data sets with up to 1000 points randomly generated in groups linearly separable with up to dimension 10 and (2) the Fisher Iris Plant database, a well-known database referred to in the pattern recognition literature. The advantages of the method presented are its simplicity, its fast and stable convergence, beyond efficiency in cluster analysis.
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