Rare earth elements (REEs), especially heavy rare earth elements (HREEs), are in demand for their current and emerging applications in advanced technologies. Here we perform computer-driven micro-mapping at the millim...Rare earth elements (REEs), especially heavy rare earth elements (HREEs), are in demand for their current and emerging applications in advanced technologies. Here we perform computer-driven micro-mapping at the millimeter scale of the minerals that comprise Round Top Mountain, in west Texas, USA. This large rhyolite deposit is enriched in HREEs and such other critical elements as Li, Be, and U. Electron probe microanalysis of 2 × 2 mm areas of thin sections of the rhyolite produced individual maps of 16 elements. These were superimposed to generate a 16-element composition at each pixel. Principal components analysis of elements at each pixel identified the specific mineral at that site. The pixels were then relabeled as the appropriate minerals, thereby producing a single mineral map. The overall mineral composition of the 7 studied samples compared favorably with prior analyses of the Round Top deposit available in the literature. Likewise the range of porosity in the maps was consistent with that of previous direct measurements by water saturation. This new statistical and GIS-based technique provides a robust and unbiased approach to electron microprobe mapping. The study further showed that the high-value yttrofluorite grains exhibited little tendency to cluster with other late-stage trace minerals and that the samples extended the previously documented overall homogeneity of the deposit at field scale to this microscopic scale.展开更多
Analog circuits fault diagnosis is essential for guaranteeing the reliability and maintainability of electronic systems. In this paper, a novel analog circuit fault diagnosis approach is proposed based on greedy kerne...Analog circuits fault diagnosis is essential for guaranteeing the reliability and maintainability of electronic systems. In this paper, a novel analog circuit fault diagnosis approach is proposed based on greedy kernel principal component analysis (KPCA) and one-against-all support vector machine (OAASVM). In order to obtain a successful SVM-based fault classifier, eliminating noise and extracting fault features are very important. Due to the better performance of nonlinear fault features extraction and noise elimination as compared with PCA, KPCA is adopted in the proposed approach. However, when we adopt KPCA to extract fault features of analog circuit, a drawback of KPCA is that the storage required for the kernel matrix grows quadratically, and the computational cost for eigenvector of the kernel matrix grows linearly with the number of training samples. Therefore, GKPCA, which can approximate KPCA with small representation error, is introduced to enhance computational efficiency. Based on the statistical learning theory and the empirical risk minimization principle, SVM has advantages of better classification accuracy and generalization performance. The extracted fault features are then used as the inputs of OAASVM to solve fault diagnosis problem. The effectiveness of the proposed approach is verified by the experimental results.展开更多
文摘Rare earth elements (REEs), especially heavy rare earth elements (HREEs), are in demand for their current and emerging applications in advanced technologies. Here we perform computer-driven micro-mapping at the millimeter scale of the minerals that comprise Round Top Mountain, in west Texas, USA. This large rhyolite deposit is enriched in HREEs and such other critical elements as Li, Be, and U. Electron probe microanalysis of 2 × 2 mm areas of thin sections of the rhyolite produced individual maps of 16 elements. These were superimposed to generate a 16-element composition at each pixel. Principal components analysis of elements at each pixel identified the specific mineral at that site. The pixels were then relabeled as the appropriate minerals, thereby producing a single mineral map. The overall mineral composition of the 7 studied samples compared favorably with prior analyses of the Round Top deposit available in the literature. Likewise the range of porosity in the maps was consistent with that of previous direct measurements by water saturation. This new statistical and GIS-based technique provides a robust and unbiased approach to electron microprobe mapping. The study further showed that the high-value yttrofluorite grains exhibited little tendency to cluster with other late-stage trace minerals and that the samples extended the previously documented overall homogeneity of the deposit at field scale to this microscopic scale.
基金Sponsored by the National Natural Science Foundation of China(Grant No. 61074127)
文摘Analog circuits fault diagnosis is essential for guaranteeing the reliability and maintainability of electronic systems. In this paper, a novel analog circuit fault diagnosis approach is proposed based on greedy kernel principal component analysis (KPCA) and one-against-all support vector machine (OAASVM). In order to obtain a successful SVM-based fault classifier, eliminating noise and extracting fault features are very important. Due to the better performance of nonlinear fault features extraction and noise elimination as compared with PCA, KPCA is adopted in the proposed approach. However, when we adopt KPCA to extract fault features of analog circuit, a drawback of KPCA is that the storage required for the kernel matrix grows quadratically, and the computational cost for eigenvector of the kernel matrix grows linearly with the number of training samples. Therefore, GKPCA, which can approximate KPCA with small representation error, is introduced to enhance computational efficiency. Based on the statistical learning theory and the empirical risk minimization principle, SVM has advantages of better classification accuracy and generalization performance. The extracted fault features are then used as the inputs of OAASVM to solve fault diagnosis problem. The effectiveness of the proposed approach is verified by the experimental results.
