Multivariate t-mixture modelling is more robust than Gaussian mixture modelling to a set of data containing a group or groups of observations with longer than Gaussian tails or a typical observations. To alleviate the...
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ISBN:
(纸本)0780384032
Multivariate t-mixture modelling is more robust than Gaussian mixture modelling to a set of data containing a group or groups of observations with longer than Gaussian tails or a typical observations. To alleviate the problem of local convergence of the traditional EM algorithm, a split-and-merge operation is introduced into the EM algorithm for multivariate t-mixtures. The split-and-merge equations are first presented theoretically and then a new merge method is acquired. Accordingly, a modified EM algorithm is constructed. Experiments of data clustering and unsupervised color image segmentation are given.
The patterns of EEG changes with the mental tasks performed by the subject. In the field of EEG signal analysis and application, the study to get the patterns of mental EEG and then to use them to classify mental task...
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ISBN:
(纸本)953184061X
The patterns of EEG changes with the mental tasks performed by the subject. In the field of EEG signal analysis and application, the study to get the patterns of mental EEG and then to use them to classify mental tasks has the significant scientific meaning and great application value. But for the reasons of different artifacts contained in EEG, the pattern detection in EEG produced from normal mental states is a very difficult problem. In this paper, independent component analysis is applied to EEG signals collected from different mental tasks .The experiment results show that when one subject performs a single mental task in different trails, the independent components of EEG are very similar. It means that the independent components can be used as the mental EEG patterns to classify the different mental tasks.
It usually needs complicated nonlinear operations to get the characteristics from the raw information inputted, and it is very difficult to find this kind of algorithm directly. The geometrical meaning of the multilay...
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ISBN:
(纸本)0780375084
It usually needs complicated nonlinear operations to get the characteristics from the raw information inputted, and it is very difficult to find this kind of algorithm directly. The geometrical meaning of the multilayer perceptron's neuron model indicates that classifying samples according to the requirements by constructing neural networks is equal to finding a collection of domains with which vectors of the preset sample sets are partitioned. But in some applications, such as time series forecasting including stock share forecasting, due to their preset sample sets may contain some exceptions and erroneous results, it is desired to introduce some self-adjusting and probabilistic decision-making mechanism to enhance the accuracy of classification. At the same time the mechanism can reduce the size of neural networks and speed up the recognition process. We discuss a self-adjusting and probabilistic decision-making mechanism for the covering algorithm. Based on the method, we developed a self-adjusting and probabilistic decision-making classifier and applied the software package to forecast the share index of Shanghai's stock market.
Panoramic mosaics methods based on 8-paramter planar homography matrix have to overcome the accumulated errors, when a sequence of images loops back on itself. Usual methods are computationally intensive, and can not ...
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ISBN:
(纸本)9628576623
Panoramic mosaics methods based on 8-paramter planar homography matrix have to overcome the accumulated errors, when a sequence of images loops back on itself. Usual methods are computationally intensive, and can not ensure complete consistency of homographies. This paper presents a simple method, which do not require the consistency of homographies. The method mainly exploits un-calibrated image perspective interpolation technique [1]. So it is of simple calculation and easy realization.
Panoramic mosaics methods based on an 8-parameter planar homography matrix have to overcome the accumulated errors when a sequence of images loops back on itself. Usual methods are computationally intensive, and canno...
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ISBN:
(纸本)9628576623
Panoramic mosaics methods based on an 8-parameter planar homography matrix have to overcome the accumulated errors when a sequence of images loops back on itself. Usual methods are computationally intensive, and cannot ensure complete consistency of homographies. The paper presents a simple method which does not require the consistency of homographies. The method mainly exploits an un-calibrated image perspective interpolation technique (B. Yuan et al., 1998). It is therefore simple to calculate and easy to implement.
In this paper, we present a novel indirect convergent Jacobi spectral collocation method for fractional optimal control problems governed by a dynamical system including both classical derivative and Caputo fractional...
Personality primarily refers to the unique and stable way of a person’s thinking and behavior. A few studies have recently been conducted on personality recognition using physiological signals, most of which have use...
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Personality primarily refers to the unique and stable way of a person’s thinking and behavior. A few studies have recently been conducted on personality recognition using physiological signals, most of which have used two-dimensional (2D) emotional stimulus materials. Virtual reality (VR) has been utilized in many fields, and its superiority over 2D in emotion recognition has been proven. However, relevant research on VR scenes is lacking in the field of personality recognition. In this study, based on the psychological principle that emotional arousal can expose an individual’s personality, we attempt to explore the feasibility and effect of using electrocardiogram (ECG) signals in response to VR emotional stimuli for personality identification. For this purpose, a VR-2D emotion-induction experiment was conducted in which ECG signals were collected, and physiological datasets of emotional personalities were constructed through preprocessing and feature extraction. Statistical analysis of the emotion scale scores and ECG features of the participants showed that the VR group had a higher number of significantly correlated features. Meanwhile, VR- and 2D-based personality recognition models were constructed using machine learning algorithms. The results showed that the VR-based personality recognition model achieved better results for the four personality dimensions, with a maximum accuracy of 79.76%. These findings indicate that VR not only enhances the physiological correlation between emotion and personality but also improves the classification accuracy of personality recognition.
In this paper, we develop spectral collocation method for a class of fractional diffusion differential equations. Since the solutions of these fractional differential equations usually exhibit singularities at the end...
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