Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequ...Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequences such as the failure of the orbit feedback and the transverse feedback.A troubleshooting has been made to prevent the defective BPMs from affecting the accuracy and stability of the storage ring in Shanghai Synchrotron Radiation Facility(SSRF).Different types of malfunctions have been successfully identified by using the idea of principal component analysis(PCA).展开更多
The major element composition of sound-producing sand is reported together with rare-earth elements (REE) and other selected elements for the first time. Rare-earth element concentrations in beach sands from Miyagi an...The major element composition of sound-producing sand is reported together with rare-earth elements (REE) and other selected elements for the first time. Rare-earth element concentrations in beach sands from Miyagi and Tottori in Japan were determined by induction-coupled, argon-plasma spectrometry (ICP-MS) to characterize the REE of sound-producing and silent sands relative to the parental rocks. Sound-producing sand beaches are very common and all over in Japan: five beaches in Miyagi and 2 in Tottori are selected with other silent sand beaches in the areas. Both sound-producing sand and silent sand samples from Miyagi and Tottori contain more than 60wt% of SiO2 and are composed mainly of quartz and feldspar. Miyagi sand samples are characterized by light REE enrichment and flat chondrite-normalized patterns that are similar to those of local source sandstone. However, all sand samples from Miyatojima in Miyagi show positive Eu anomalies, a characteristic feature not shown in other sand samples from Miyagi. Tottori sand samples also are characterized by high REE contents and remarkable positive Eu anomalies. The sands containing lower REE contents are due to high quartz and feldspar contents. Miyatojima sand samples and Tottori sand samples have high REE contents and show remarkable positive Eu anomalies due to the presence of feldspar. The best results are obtained using all of the geological methods and the Principal Component Analysis (PCA) as a measure of the similarity between sound-producing sand and silent sand. The difference between sound-producing sand and silent sand is obtained from the PCA results.展开更多
Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal e...Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.展开更多
基金Supported by National Natural Science Foundation of China(No.11075198)
文摘Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequences such as the failure of the orbit feedback and the transverse feedback.A troubleshooting has been made to prevent the defective BPMs from affecting the accuracy and stability of the storage ring in Shanghai Synchrotron Radiation Facility(SSRF).Different types of malfunctions have been successfully identified by using the idea of principal component analysis(PCA).
文摘The major element composition of sound-producing sand is reported together with rare-earth elements (REE) and other selected elements for the first time. Rare-earth element concentrations in beach sands from Miyagi and Tottori in Japan were determined by induction-coupled, argon-plasma spectrometry (ICP-MS) to characterize the REE of sound-producing and silent sands relative to the parental rocks. Sound-producing sand beaches are very common and all over in Japan: five beaches in Miyagi and 2 in Tottori are selected with other silent sand beaches in the areas. Both sound-producing sand and silent sand samples from Miyagi and Tottori contain more than 60wt% of SiO2 and are composed mainly of quartz and feldspar. Miyagi sand samples are characterized by light REE enrichment and flat chondrite-normalized patterns that are similar to those of local source sandstone. However, all sand samples from Miyatojima in Miyagi show positive Eu anomalies, a characteristic feature not shown in other sand samples from Miyagi. Tottori sand samples also are characterized by high REE contents and remarkable positive Eu anomalies. The sands containing lower REE contents are due to high quartz and feldspar contents. Miyatojima sand samples and Tottori sand samples have high REE contents and show remarkable positive Eu anomalies due to the presence of feldspar. The best results are obtained using all of the geological methods and the Principal Component Analysis (PCA) as a measure of the similarity between sound-producing sand and silent sand. The difference between sound-producing sand and silent sand is obtained from the PCA results.
文摘Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.
