Classification of stellar spectra with SVM based on within-class scatter and between-class scatter

Support Vector Machine (SVM) is a popular data mining technique, and it has been widely applied in astronomical tasks, especially in stellar spectra classification. Since SVM doesn’t take the data distribution into consideration, and therefore, its classification efficiencies can’t be greatly improved. Meanwhile, SVM ignores the internal information of the training dataset, such as the within-class structure and between-class structure. In view of this, we propose a new classification algorithm-SVM based on Within-Class Scatter and Between-Class Scatter (WBS-SVM) in this paper. WBS-SVM tries to find an optimal hyperplane to separate two classes. The difference is that it incorporates minimum within-class scatter and maximum between-class scatter in Linear Discriminant Analysis (LDA) into SVM. These two scatters represent the distributions of the training dataset, and the optimization of WBS-SVM ensures the samples in the same class are as close as possible and the samples in different classes are as far as possible. Experiments on the K-, F-, G-type stellar spectra from Sloan Digital Sky Survey (SDSS), Data Release 8 show that our proposed WBS-SVM can greatly improve the classification accuracies.     

Extreme Flood Events over the Past 300 Years Inferred from Lake Sedimentary Grain Sizes in the Altay Mountains,Northwestern China

Understanding the temporal variations of extreme floods that occur in response to climate change is essential to anticipate the trends in flood magnitude and frequency in the context of global warming. However, long-term records of paleofloods in arid regions are scarce, thus preventing a thorough understanding of such events. In this study, a reconstruction of paleofloods over the past 300 years was conducted through an analysis of grain sizes from the sediments of Kanas Lake in the Altay Mountains of northwestern China. Results showed that grain parameters and frequency distributions can be used to infer possible abrupt environmental events within the lake sedimentary sequence, and two extreme flood events corresponding to ca. 1736–1765 AD and ca. 1890 AD were further identified based on canonical discriminant analysis(CDA) and coarse percentile versus median grain size(C-M) pattern analysis, both of which occurred during warmer and wetter climate conditions by referring to tree-ring records. These two flood events are also evidenced by lake sedimentary records in the Altay and Tianshan mountains. Furthermore, through a comparison with other records, the flood event from ca. 1736–1765 AD in the study region seems to have occurred in both the arid central Asia and the Alps in Europe, and thus may have been associated with changes in the North Atlantic Oscillation(NAO) index.     

Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set’s reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.     

Prediction of Blast-Induced Rock Movement During Bench Blasting: Use of Gray Wolf Optimizer and Support Vector Regression

Natural Resources Research - A large ore loss and dilution can be expected when using a pre-blast ore boundary for shovel guidance because of the movement and re-distribution of ore in the muck...     

Post-earthquake economic resilience and recovery efficiency in the border areas of the Tibetan Plateau: A case study of areas affected by the Wenchuan Ms 8.0 Earthquake in Sichuan,China in 2008

Journal of Geographical Sciences - The border areas of the Tibetan Plateau and the neighboring mountainous areas have a high incidence of earthquakes with a magnitude greater than Ms 5.0, as well...     

Fast genetic mapping in barley: case studies of cuticle mutants using RNA-sequencing

Barley(Hordeum vulgare L.) is one of the earliest domesticated crop species and ranked as the fourth largest cereal production worldwide. Forward genetic studies in barley have greatly advanced plant genetics during the last century; however, most genes are identified by the conventional mapping method. Array genotyping and exome-capture sequencing have also been successfully used to target the causal mutation in barley populations, but these techniques are not widely adopted because of associated costs and partly due to the huge genome size of barley. This review summarizes three mapping cases of barley cuticle mutants in our laboratory with the help of RNA-sequencing. The causal mutations have been successfully identified for two of them and the target genes are located in the pericentromeric regions. Detailed information on the mapping-by-sequencing, mapping-and-sequencing, and RNA-sequencing assisted linkage mapping are presented and some limitations and challenges on the mapping assisted by RNA sequencing are also discussed. The alternative and elegant methods presented in this review may greatly accelerate forward genetics of barley mapping, especially for laboratories without large funding.     

青藏高原隆升的非线性动态有限元仿真研究

根据青藏高原的地质特征建立分析模型，采用3维动态有限元方法，在计算仿真板块速度场的基础上，计算在青藏高原的隆升过程中该地区地壳岩石的等效应力和位移随时间的变化，计算仿真得到的速度场与1998年GPS观测的速度场吻合良好；与过去一贯的假设相反，计算结果反映出地壳应力场不是静态的，而是此起彼伏，不断变化的，应力值最大且变化最剧烈的地区在克什米尔地区、鄂尔多斯地区和鲜水河－小江断裂带，与地震多发区域吻合。     

大同—阳高Ms6.1地震前兆综合加权信息熵的变化

利用“地震预报计算机专家系统”的思想对大同 -阳高 Ms6 .1地震前每一前兆异常事件进行综合评估 ,以每一异常的最可能发震时间来计算发震概率 ,利用地震前兆综合加权信息熵研究了系统熵值与地震的关系。对华北地区的地震前兆综合加权信息熵研究表明 ,在大同 -阳高 Ms6 .1地震前 ,信息熵出现了明显的减熵有序变化     

对青藏东北缘现今块体划分、运动及变形的初步研究

利用2维非连续变形分析方法（DDA），以位移代替围压作为边界约束力，研究青藏东北缘现今块体划分及其运动变形。根据该地区地质构造及地震活动，以GPS点测量位移作为模拟结果约束点，得出了较合理的块体划分模型和随时间演化的主应变分布图，并把应变高值区与近几年来发生的5级以上地震作对比，得出了研究区内地震危险性可能较大的区域。另外，对模拟的甘青块体与阿拉善块体的边缘带断裂左旋运动做了大概计算。     

抽油机悬点投影测量仪

油田抽油机的抽油杆幌动幅度过大，是引起抽油杆断裂的一个重要因素，检测这种幌动幅度是防止抽油杆断裂的一种有效手段。设计了一种基于面阵CCD和普通半导体激光器（LD）测量这种幌动的悬点投影测量方法，通过数字卷积滤波，达到了范围为0－40mm，误差＜0.2mm的技术检测指标。