this paper presents an original approach for the optimal 3D reconstruction of manufactured workpieces based on a priori planification of the task, enhanced on-line through dynamic adjustment of the lighting conditions...
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ISBN:
(纸本)9780819464514
this paper presents an original approach for the optimal 3D reconstruction of manufactured workpieces based on a priori planification of the task, enhanced on-line through dynamic adjustment of the lighting conditions, and built around a cognitive intelligent sensory system using so-called Situation Graph Trees. the system takes explicitely structural knowledge related to image acquisition conditions, type of illumination sources, contents of the scene (e. g., CAD models and tolerance information), etc. into account. the principle of the approach relies on two steps. First, a so called initialization phase, leading to the a priori task plan, collects this structural knowledge. this knowledge is conveniently encoded, as a sub-part, in the Situation Graph Tree building the backbone of the planning system specifying exhaustively the behavior of the application. Second, the image is iteratively evaluated under the control of this Situation Graph Tree. the information describing the quality of the piece to analyze is thus extracted and further exploited for, e. g., inspection tasks. Lastly, the approach enables dynamic adjustment of the Situation Graph Tree, enabling the system to adjust itself to the actual application run-time conditions, thus providing the system with a self-learning capability.
An intelligent radar signal sorting system with a robust radial basis function (RBF) is presented in this paper. this system can automatically sort the random overlapped radar signal stream and separate the input puls...
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An intelligent radar signal sorting system with a robust radial basis function (RBF) is presented in this paper. this system can automatically sort the random overlapped radar signal stream and separate the input pulse stream to individual radar pulse sequence. Because tradition Gaussian neural network uses Gauss function as its basis function and adopt gradient descending method to adjust parameters. So the tradition method is likely to produce some non-expectation in learning process. In order to solve the problem, the proposed RBF uses Log-Sigmoid function as its basis function, so it eliminates any risk of instabilities, and it has better learning properties and function approximation capabilities. this algorithm ameliorates the traditional algorithm and enhances the robust properties of learning process. For one thing, the method can adapt to the complicated electromagnetic environment demand due to its self-adapting capability. For another, it can overcome the difficulty that the data have too much noise due to the detection system faultiness. Simulation results demonstrate the obvious superiority of this algorithm.
this paper proposes markovian models in portfolio theory and risk management. In a first analysis, we describe discrete time optimal allocation models. then, we examine the investor's optimal choices either when r...
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ISBN:
(纸本)3540454853
this paper proposes markovian models in portfolio theory and risk management. In a first analysis, we describe discrete time optimal allocation models. then, we examine the investor's optimal choices either when returns are uniquely determined by their mean and variance or when they are modeled by a Markov chain. Moreover we propose different models to compute VaR and CVaR when returns are modeled by a Markov chain.
A fast data preprocessing procedure (FDPP) for support vector regression (SVR) is proposed in this paper. In the presented method, the dataset is firstly divided into several subsets and then K-means clustering is imp...
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We propose a novel algorithm for computing asymmetric word similarity (AWS) using mass assignment based on fuzzy sets of words. Words in documents are considered similar if they appear in similar contexts. However, th...
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A novel approach is presented to the categorisation of non-rigid biological objects from unsegmented scenes in an unsupervised manner. the biological objects investigated are five phytoplankton species from the coasta...
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the Secure Electronic Transaction (SET) protocol is a protocol designed to conduct safe business over Internet. We present formal verification of the Payment Authorization in SET by using ENDL (extension of non-monoto...
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ISBN:
(纸本)354040550X
the Secure Electronic Transaction (SET) protocol is a protocol designed to conduct safe business over Internet. We present formal verification of the Payment Authorization in SET by using ENDL (extension of non-monotonic logic) [1]. the analysis uncovers some subtle defects that may incur malicious attacks. To overcome these vulnerabilities, some feasible countermeasures are proposed accordingly.
In business applications, robust short term prediction is important for survival. Artificial neural network (ANN) have shown excellent potential however it needs better extrapolation capacity in order to provide relia...
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ISBN:
(纸本)354040550X
In business applications, robust short term prediction is important for survival. Artificial neural network (ANN) have shown excellent potential however it needs better extrapolation capacity in order to provide reliable short term prediction. In this paper, a combination of linear regression model in parallel with general regression neural network is introduced for short term financial prediction. the experiment shows that the proposed model achieves comparable prediction performance to other conventional prediction models.
Apriori-like algorithms for association rules mining rely upon the minimum support and the minimum confidence. Users often feel hard to give these thresholds. On the other hand, genetic algorithm is effective for glob...
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ISBN:
(纸本)354040550X
Apriori-like algorithms for association rules mining rely upon the minimum support and the minimum confidence. Users often feel hard to give these thresholds. On the other hand, genetic algorithm is effective for global searching, especially when the searching space is so large that it is hardly possible to use deterministic searching method. We try to apply genetic algorithm to the association rules mining and propose an evolutionary method. Computations are conducted, showing that our ARMGA model can be used for the automation of the association rule mining systems, and the ideas given in this paper are effective.
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