Navigability is a main concern in the design of Web applications. In order to assess such navigability a number of measures has been proposed. From them, measures defined on conceptual models are specially relevant, a...
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(纸本)3540477039
Navigability is a main concern in the design of Web applications. In order to assess such navigability a number of measures has been proposed. From them, measures defined on conceptual models are specially relevant, as it is well known that high quality conceptual models are critical to the success of the deployed system. However, measurement methods associated to such measures, as well as the design modifications that need to be performed on the models in order to improve their values, are usually tightly coupled with particular Web Engineering approaches. this fact compromises their effectiveness and their propagation capacity to different environments and/or methodologies. Our aim in this paper is to illustrate how navigability measures can be captured in a general manner. In this way, not only is it possible to define a reusable set of relevant measures for a given family of applications, but also such measures can be queried in the context of MDA transformation rules. these rules capture boththe measure decision criteria and the design modifications that should take place if the measure value for a given navigational model does not match such criteria.
Deformable models have had great successes over the past 20 years in medical applications. We have recently developed new classes of deformable models which we term hybrid deformable models to automate the model initi...
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Deformable models have had great successes over the past 20 years in medical applications. We have recently developed new classes of deformable models which we term hybrid deformable models to automate the model initialization process and make improvements in segmentation and registration. In this paper we present several hybrid deformable methods we have been developing for segmentation and registration. these methods include metamorphs, a novel shape and texture integration deformable model framework and the integration of deformable models with graphical models and learning methods. We first present a framework for the robust segmentation and tracking of the heart from tagged MRI images and second applications involving brain tumor segmentation as well as brain and cardiac shape registration
this paper describes the measurement of inner deformation of a rheological object using ultrasonic and MR images and comparison the measured and simulated deformations. We apply finite element (FE) model to simulate e...
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this paper describes the measurement of inner deformation of a rheological object using ultrasonic and MR images and comparison the measured and simulated deformations. We apply finite element (FE) model to simulate elastic, viscoplastic, and rheological deformation of soft objects. Ultrasonic and MR images are used to reveal the inner deformation of a soft object. Here we report the measurement and its evaluation by comparing measured and simulated deformations
As the scale of systems increases, traditional models and fault diagnosis methods are not applicable. Qualitative signed directed graphs (QSDG) are used to model the variables and relationships among them in large-sca...
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As the scale of systems increases, traditional models and fault diagnosis methods are not applicable. Qualitative signed directed graphs (QSDG) are used to model the variables and relationships among them in large-scale complex systems. However, they have distinct limitations of resulting spurious solutions due to the lack of utilization of knowledge or information. this article proposes a kind of probabilistic SDG (PSDG) model to describe the propagation of faults among variables. the fault diagnosis method is also investigated, where Bayesian network has been employed. Finally, examples are given and the future topics are listed
this paper presents a multi-estimation adaptive control strategy for stabilizing a potentially noninversely stable, linear and time-invariant plant. Such a strategy works with several discretization models of the plan...
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this paper presents a multi-estimation adaptive control strategy for stabilizing a potentially noninversely stable, linear and time-invariant plant. Such a strategy works with several discretization models of the plant. Each one of them is obtained by means of a fractional-order hold (FROH) and a multirate input in order to place its zeros into the stability region. A supervisor activates one of such models and maintains it in operation during at least a minimum residence time for stability purposes. that estimation model parameterizes a discrete-time adaptive controller for asymptotically matching a stable reference model at sampling instants
Soft sensors are especially required in lots of advanced process control applications. the ANN based soft sensor are widely studied recently. But the ANN is an uncertain method in nature. In view of the complexity of ...
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Soft sensors are especially required in lots of advanced process control applications. the ANN based soft sensor are widely studied recently. But the ANN is an uncertain method in nature. In view of the complexity of the industrial processes, the robustness is an important criterion to evaluate a model. the generalization capability is another factor to affect the applicability of a model. Aiming at improving the robustness and generalization capability of a system, a two-level architecture MNN model is proposed for soft sensor modeling. In our model, multiple networks are combined withthe Bayesian and fuzzy C-means (FCM) clustering combination methods at different levels. Two experiments are conducted to validate the effectiveness of our model. the results reveal that the proposed model exceeds other three models indeed
this paper presents a scheme for modeling the dynamic contour of wet material objects using optimal periodic surfaces. the surfaces are constructed by employing normalized uniform B-splines as the basis functions. A c...
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this paper presents a scheme for modeling the dynamic contour of wet material objects using optimal periodic surfaces. the surfaces are constructed by employing normalized uniform B-splines as the basis functions. A concise representation for the optimal surfaces is derived, which has the additional merit of lending itself to the development of computational procedures in a straightforward manner. Also, the asymptotical and statistical properties of optimal surfaces are shown. the results are applied to the problem of modeling contour of wet material objects with deforming motion, and the effectiveness is examined by numerical and experimental studies
this contribution presents a novel simulation tool for the electrothermal analysis of solid-state devices and circuits in both static and transient conditions. the code relies on an effective analytical approach to de...
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this contribution presents a novel simulation tool for the electrothermal analysis of solid-state devices and circuits in both static and transient conditions. the code relies on an effective analytical approach to describe the 3D thermal process, and exploits the engine of the commercial software ADS (Advanced Design System) to consistently solve the electrical and thermal networks. Features like model accuracy, lack of convergence problems, low time/memory requirements, flexibility and user friendliness make the proposed software a good alternative to SPICE-like and fully numerical tools for the optimization of both reliability and performance of state-of-the-art devices/circuits
In this paper we present a novel method of using genetic algorithm (GA) to learn a graphical model which is used for human motion characterization. the modeling of human movements will involve a high dimensional joint...
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In this paper we present a novel method of using genetic algorithm (GA) to learn a graphical model which is used for human motion characterization. the modeling of human movements will involve a high dimensional joint probability density function. Withthis graphical model, the joint probability distribution can be decomposed into a number of low dimensional distributions which are represented as tree models and triangulated models. To automatically search for such a model from a database of cases is a NP-hard problem. We use GA to solve this problem, which can optimize boththe ordering structure and the conditional independence relationship of the graphical model. the searched graphical models are used to classify different types of human motions. the experimental results demonstrate that, compared with a previous greedy search algorithm, the GA is more effective for optimization of the graphical model
A new bounded-error approach for the identification of discrete time hybrid systems in the piece-wise affine (PWA) form is introduced. the PWA identification problem involves the estimation of the number of affine sub...
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A new bounded-error approach for the identification of discrete time hybrid systems in the piece-wise affine (PWA) form is introduced. the PWA identification problem involves the estimation of the number of affine submodels, the parameters of affine submodels and the partition of the PWA map from data. By imposing a bound on the identification error, we formulate the PWA identification problem as a MIN PFS problem (partition into a minimum number of feasible subsystems) and propose a greedy clustering-based method for tackling it. the proposed approach yields to better results than the greedy randomized relaxation algorithm used in previous methods. Also, it is not sensitive to the overestimation of model orders and changes in the tuning parameters and therefore finding a right combination of the tuning parameters of the algorithm to get a model with prescribed bounded prediction error is simple
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