This paper describes a binary image representation scheme, which is called image block representation and presents a new skeletonization algorithm, which is implemented on block represented binary images. The main pur...
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This paper describes a binary image representation scheme, which is called image block representation and presents a new skeletonization algorithm, which is implemented on block represented binary images. The main purpose of the image block representation is to provide an efficient binary image representation that permits the execution of operations on image areas instead of image points. The skeletonization algorithm operates in four subiterations: each subiteration deletes the north, the south, the west and the east boundary points, respectively. Due to the substitution of the boundary points by the block's boundary points the relevant operations are performed fast, while preserving the end points and the object connectivity.
In this paper we present a method for image feature extraction using coordinate logic filters. Coordinate logic filters constitute a new kind of nonlinear filter, which are closely related to morphological fillers. Th...
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In this paper we present a method for image feature extraction using coordinate logic filters. Coordinate logic filters constitute a new kind of nonlinear filter, which are closely related to morphological fillers. The key issue to the coordinate logic analysis of images is the methodology of fast successive filtering and managing of the residues. The desired processing is succeeded by executing only direct logic operations among the pixels of the given image. The coordinate logic filters can be easily implemented using logic circuits or cellular automata.
In this paper is presented a new approach to robust non-linear control design, which can guarantee a prescribed decay rate of exponential stability for known system uncertainties. The proposed approach doesn’t employ...
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In this paper is presented a new approach to robust non-linear control design, which can guarantee a prescribed decay rate of exponential stability for known system uncertainties. The proposed approach doesn’t employ matching conditions. The non-linear power system with deterministic uncertainties is chosen as demonstration example.
This paper presents a novel and robust GA-fuzzy controller structure. This controller incorporates genetic algorithms and fuzzy logic in order to control the complicated nonlinear plant. The GA is used to optimize the...
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ISBN:
(纸本)0780342534
This paper presents a novel and robust GA-fuzzy controller structure. This controller incorporates genetic algorithms and fuzzy logic in order to control the complicated nonlinear plant. The GA is used to optimize the parameters of fuzzy logic and its control rules. The results of simulation studies for an industrial plant and inverted pendulum demonstrate that the controller is robust and efficient.
This paper presents a model-based approach to fault detection of dynamic systems, which is robust to unmodeled dynamics. A “Quasi-ARMAX model᾿ is first proposed for describing nonlinear systems by incorporating a gro...
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This paper presents a model-based approach to fault detection of dynamic systems, which is robust to unmodeled dynamics. A “Quasi-ARMAX model᾿ is first proposed for describing nonlinear systems by incorporating a group of certain nonlinear structures into a linear ARMAX structure. The model can be used for a best linear approximation of the system, as well as for the estimation of resulting unmodeled dynamics, by a hierarchical implementation of recursive identification. Then robust fault detection is performed based on thresholding approach using Kullback discrimination information as fault detection index, in which the estimated unmodeled dynamics is incorporated.
Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an imag...
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Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system.
Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification i...
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Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification is based on the local linear model tree (LOLIMOT) algorithm and leads to local linear models. The parameters of the local models are used for generation of structured residual equations, similar to the well-known parity space approach. This enables detection and isolation of five different sensor faults of the three-tank process, continously over all ranges of operation.
This research is concerned with fault detection of nonlinear systems using Kullback discrimination information (KDI) as an index. A hybrid quasi-ARMAX model is proposed, which combines a linear ARMAX model and a multi...
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This research is concerned with fault detection of nonlinear systems using Kullback discrimination information (KDI) as an index. A hybrid quasi-ARMAX model is proposed, which combines a linear ARMAX model and a multi-ARX-model based on interpolation. In the case where the faults occur on the ARMAX model part, a KDI-based "robust" fault detection is performed, in which multi-ARX-model part is treated as error due to nonlinear undermodeling. In other cases, the model is transformed into several local ARMAX models and fault detection is performed by using the KDI to discriminate each identified local model. In this paper, we mainly concentrate our discussion on the latter cases.
This paper describes a method to achieve the most relevant contours of an image. The presented method proposes to integrate the information of the local contours from chromatic components such as H, S and I, taking in...
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This paper describes a method to achieve the most relevant contours of an image. The presented method proposes to integrate the information of the local contours from chromatic components such as H, S and I, taking into account the criteria of coherence of the local contour orientation values obtained from each of these components. The process is based on parametrizing pixel by pixel the local contours (magnitude and orientation values) from the H, S and I images. This process is carried out individually for each chromatic component. If the criterion of dispersion of the obtained orientation values is high, this chromatic component will lose relevance. A final processing integrates the extracted contours of the three chromatic components, generating the so-called integrated contours image.
Net condition/event systems (NCES) have been defined to provide a modular modelling framework for discrete event systems. NCES are based on the interconnection of modules whose dynamic behavior is modeled by means of ...
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Net condition/event systems (NCES) have been defined to provide a modular modelling framework for discrete event systems. NCES are based on the interconnection of modules whose dynamic behavior is modeled by means of Petri nets. The interconnection is performed by signals only. The resulting system is no longer an ordinary, classical Petri net. Hence, if we want to analyse those systems by means of methods for Petri nets, we have to generalize the formalisms of Petri nets. We call those extended nets signal/event nets. In this paper, signal/events nets are defined and their position in the hierarchy of computational models is shown. Although the main problems are undecidable, analysis of signal/event nets is not impossible.
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