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
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
In this paper, we precisely discuss two necessary methods to manipulate artificially a rheology object by another rigid object via force feedback (haptics) devise. the rheology object is quite popular material in our ...
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In this paper, we precisely discuss two necessary methods to manipulate artificially a rheology object by another rigid object via force feedback (haptics) devise. the rheology object is quite popular material in our living life, e.g., food materials such as several kinds of noodles, human bodies such as organs and muscles, and an article of clothing. For this reason, it is very important for us to manipulate a rheology object by another rigid object. In our previous works, we finish modeling and calibrating MSD rheology model. therefore, in this paper, we define some collision model to make and calibrate a set of external forces based on the swept volume of rheology object by rigid body. Also, we revisit some force transformation method between encountered surfaces of rheology object and rigid body. In this research, we successively regard bothmethods as penalty method and bilinear approximation method
the problem of terrain modeling is basically a type of function approximation problem. this type of problem has been widely studied in the soft computing community. In recent years, neural networks have been successfu...
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the problem of terrain modeling is basically a type of function approximation problem. this type of problem has been widely studied in the soft computing community. In recent years, neural networks have been successfully applied to surface reconstruction and classification problems involving scattered data. However, due to the iterative nature of training a neural network, the resulting high cost in computational time limits the implementation of machine learning based methods in many real world applications (for example, navigation applications in unmanned aerial vehicles) that require fast generation of terrain models. A recently proposed machine learning method, the extreme learning machine (ELM), is able to train single-layer feed forward neural networks with excellent speed and good generalization. In this paper, we present terrain modeling using various machine learning methods, and we compare the performances of these methods with ELM. We also present a comparison of terrain modeling performances between ELM and the popular choice of terrain and surface modeling technique, the Delaunay triangulation with linear interpolation. Our results show that machine learning using ELM offers a potential solution to terrain modeling problems with good performances
We present a method based on the morphological 3D h-maxima transform to segment the kidneys in image volumes obtained by magnetic resonance imaging after injection of a contrast agent. Manual cropping techniques had b...
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We present a method based on the morphological 3D h-maxima transform to segment the kidneys in image volumes obtained by magnetic resonance imaging after injection of a contrast agent. Manual cropping techniques had been heavily relied upon to sever connections to other organs (arteries, liver, spleen, intestines, etc.) when routine methods produced oversegmented results. We show that simply generated rectangular masks and edge information may be incorporated to guide the segmentation. this approach is particularly useful when severe pathology or congenital abnormalities are present as no prior information about location or appearance is assumed. Results and validation from experiments on a number of selected MR datasets are demonstrated
An algorithm to estimate pan, tilt and zoom (PTZ) parameters of a PTZ camera from meta data and frame-to-frame (F2F) correspondences at different sampling rates is proposed in a real-time video surveillance and automa...
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An algorithm to estimate pan, tilt and zoom (PTZ) parameters of a PTZ camera from meta data and frame-to-frame (F2F) correspondences at different sampling rates is proposed in a real-time video surveillance and automatic object tracking system. Two extended Kalman filters are designed to simultaneously estimate zoom and pan-tilt parameters. Uncorrelated constant velocity models are used to model the kinematics of focal length, pan and tilt motions, while the F2F homography is employed to model the relative motion of the camera. Experiment results from both synthetic and realtime system data demonstrate that the F2F correspondence information can enhance the PTZ estimation accuracy as long as its error is smaller than a particular threshold
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