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201.
Jianbo Yu 《Computers & Industrial Engineering》2011,61(3):881-890
Unnatural patterns exhibited in manufacturing processes can be associated with certain assignable causes for process variation. Hence, accurate identification of various process patterns (PPs) can significantly narrow down the scope of possible causes that must be investigated, and speed up the troubleshooting process. This paper proposes a Gaussian mixture models (GMM)-based PP recognition (PPR) model, which employs a collection of several GMMs trained for PPR. By using statistical features and wavelet energy features as the input features, the proposed PPR model provides more simple training procedure and better generalization performance than using single recognizer, and hence is easier to be used by quality engineers and operators. Furthermore, the proposed model is capable of adapting novel PPs through using a dynamic modeling scheme. The simulation results indicate that the GMM-based PPR model shows good detection and recognition of current PPs and adapts further novel PPs effectively. Analysis from this study provides guidelines in developing GMM – based SPC recognition systems. 相似文献
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Purpose
Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence.Method
Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function.Results
The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers. 相似文献204.
基于PCA的XML文档特征提取方法 总被引:1,自引:0,他引:1
为了更好地对XML文档进行分类或聚类分析,以主成分分析的理论基础为指导,在研究了文本表示的各种模型的基础上,提出了两种对XML文档进行向量化表示并进行特征提取的方法,同时也实现了对XML文档的有效降维。实验结果表明,两种方法都能有效地表示XML文档的主体特征,但全路径特征向量抽取方法能更好地描述XML信息,为下一步有效处理XML文档做了良好铺垫,具有一定的研究价值。 相似文献
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208.
Donggang Yu Author Vitae Tuan D. Pham Author Vitae Author Vitae Stephen T.C. Wong Author Vitae 《Pattern recognition》2009,42(4):498-508
Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method. 相似文献
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Fethallah Benmansour Laurent D. Cohen 《Journal of Mathematical Imaging and Vision》2009,33(2):209-221
In this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the minimal
path approach. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically
using a front propagation approach. We assume that the desired object has a closed boundary. This a-priori knowledge on the
topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain
visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal
paths allows us to extract a closed contour from a 2D image. This approach can also be used for finding an open curve giving
extra information as stopping criteria. Detection of a variety of objects on real images is demonstrated. Using a similar
idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to
3D data with promising results.
相似文献
Laurent D. CohenEmail: |