The longitudinal wave propagating in one-dimensional periodic piezoelectric composite rod with inter-coupling between different piezoelectric segments is investigated. The analytical formulae for such a structure are ...The longitudinal wave propagating in one-dimensional periodic piezoelectric composite rod with inter-coupling between different piezoelectric segments is investigated. The analytical formulae for such a structure are shown and the dispersion relation is calculated. The results show that, by introducing the inter-coupling between the different piezoelectric segments, which is accomplished by serially connecting every n piezoelectric segment into supercells, some tunable Bragg band gaps can accordingly be opened in the low frequency region. The investigation could provide a new guideline for the tunable phononic crystal under passive control.展开更多
Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classificat...Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm.Airborne light detection and ranging(LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model(CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of objectbased forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely,forest gaps, tree canopies, and others. The common support vector machine(SVM) classifier with the radial basis function kernel(RBF) was first adopted to test the effect of classification features(vegetation height features and some typical topographic features) on forest gap classification.Then the different classifiers(KNN, Bayes, decision tree,and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Mo¨ller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales(10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF(90%), Decision Tree(90%), Bayes(90%),or KNN(87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale(s) of segmentation.展开更多
Information on phase equilibria in the Co-Al based systems which are related to some magnetic and heat resistance materials is important for their microstructural control. Recently, it was proposed with a theoretical ...Information on phase equilibria in the Co-Al based systems which are related to some magnetic and heat resistance materials is important for their microstructural control. Recently, it was proposed with a theoretical calculation on electronic band structure that some Heusler-type alloys Co2XAl （X: Cr and Mn） should be a new type of spinelectronic materials so-called half-metallic ferromagnet. In the case of the Co2CrAl, however, magnetic properties expected from the theoretical work can not been experimentally obtained and the reason has been still unknown. On the other hand, a tunneling magnetoresistance （TMR） effect due to the half-metallic properties was reported in Co2（Cr<sup>0.6 Fe<sup>0.4 ）Al alloy, but not the Co2CrAl alloy.In the present paper, it is reported that this discrepancy with the theoretical work in the Co2CrAl alloy is bought about by phase separation between A2 and B2 phases, and that the substitution of Fe for Cr can suppress the precipitation of A2 phase in the B2 phase. Such a phase separation is originally due to the miscibility gap between CoAl and Cr formed in the Co-Al-Cr ternary system as well as that reported by Hao et al. in the Ni-Co-Al-Fe system.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No 11274121
文摘The longitudinal wave propagating in one-dimensional periodic piezoelectric composite rod with inter-coupling between different piezoelectric segments is investigated. The analytical formulae for such a structure are shown and the dispersion relation is calculated. The results show that, by introducing the inter-coupling between the different piezoelectric segments, which is accomplished by serially connecting every n piezoelectric segment into supercells, some tunable Bragg band gaps can accordingly be opened in the low frequency region. The investigation could provide a new guideline for the tunable phononic crystal under passive control.
基金financially supported by grant from National Natural Science Foundation of China(No.31300533)
文摘Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm.Airborne light detection and ranging(LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model(CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of objectbased forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely,forest gaps, tree canopies, and others. The common support vector machine(SVM) classifier with the radial basis function kernel(RBF) was first adopted to test the effect of classification features(vegetation height features and some typical topographic features) on forest gap classification.Then the different classifiers(KNN, Bayes, decision tree,and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Mo¨ller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales(10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF(90%), Decision Tree(90%), Bayes(90%),or KNN(87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale(s) of segmentation.
文摘Information on phase equilibria in the Co-Al based systems which are related to some magnetic and heat resistance materials is important for their microstructural control. Recently, it was proposed with a theoretical calculation on electronic band structure that some Heusler-type alloys Co2XAl （X: Cr and Mn） should be a new type of spinelectronic materials so-called half-metallic ferromagnet. In the case of the Co2CrAl, however, magnetic properties expected from the theoretical work can not been experimentally obtained and the reason has been still unknown. On the other hand, a tunneling magnetoresistance （TMR） effect due to the half-metallic properties was reported in Co2（Cr<sup>0.6 Fe<sup>0.4 ）Al alloy, but not the Co2CrAl alloy.In the present paper, it is reported that this discrepancy with the theoretical work in the Co2CrAl alloy is bought about by phase separation between A2 and B2 phases, and that the substitution of Fe for Cr can suppress the precipitation of A2 phase in the B2 phase. Such a phase separation is originally due to the miscibility gap between CoAl and Cr formed in the Co-Al-Cr ternary system as well as that reported by Hao et al. in the Ni-Co-Al-Fe system.
