The chemical constituents from red alga Gymnogongrus flabelliformis Harv.

Eight compounds were isolated from red alga Gymnogongrus flabelliformis Harv. In normal phase silica gel, Sephadex LH-20 gel column chromatography, reverse phase HPLC, and recrystallization. Based on MS and 1D NMR spectroscopic data, their structures were determined as: stigmast-4-en-3-one (I), cholest-4-en-3-one (II), cholesterol (III), uracil (IV), uridine (V), adenosine (VI), succinic acid (VII), and 5-hydroxy-4-methyl-5-pentyl-2,5-dihydro-furan-2-on (VIII). All of them were obtained from this species for the first time. Cytotoxicity of these compounds was screened using standard MTT method, but all the compounds were inactive (IC50 > 10 mu g/ml).

Two new norisoprenoid derivatives from the red alga Gymnogongrus flabelliformis

Two new norisoprenoid derivatives have been isolated from the red alga Gymnogongrus flabelliformis. Their structures were elucidated as (3R, 6R, 7E)-(+)-3-O-phenylacetyl-4,7-megastigmadiene-9-one and (3R,7E)-(-)-3-O-phenylacetyl-5,7-megastigmadiene-9-one, respectively, by spectroscopic methods including HRMS, 1D and 2D NMR techniques.

青海地表始冻期和解冻期对气候变化的响应

通过对青海省8个农气观测站的土壤表面始冻期和解冻期连续观测资料的分析,结果表明:青海各地土壤表面始冻期和解冻期存在着明显的地域差异,多数站点的封冻期呈现出缩短的趋势,互助、德令哈略有延长.始冻期变化受气候变化的明显影响,当3-9月平均气温升高1℃,除贵德、诺木洪、互助站响应不明显外,其余站点的始冻期推迟1.5～7.5d,平均推迟2.3d;当3-9月日照时数增多10h,诺木洪地区始冻期提早0.5d,其余站点的响应不明显;当上年10月至当年2月平均气温升高1℃,除门源、德令哈站的解冻期响应不明显外,其余站点提前2～13d,平均提早2.5d;降水量增多10mm,除诺木洪推迟12d,其余站点推迟2～6d,平均推迟2.3d;当上年10月至当年2月平均气温升高1℃,封冻期日数除河南、门源、贵德响应不明显外,其余缩短2.4～23.1d,平均缩短1.8d.

藏药大果大戟中的巨大戟烷型二萜酯类成分

目的研究藏药大果大戟Euphorbia wallichii丙酮提取物中的化学成分。方法用溶剂提取，常规硅胶柱色谱分离和葡聚糖凝胶Sephadex LH-20纯化，采用化学方法和现代波谱分析技术（包括IR，HRESIMS，HRSIMS，1D和2DNMR等）鉴定其化学结构。结果从青海产大果大戟根的丙酮提取物中分离得到6个化合物，分别鉴定为：羊毛甾醇（lanosterol，Ⅰ）、巨大戟二萜-20-肉豆蔻酸酯（ingenol-20-myristinate，Ⅱ）、巨大戟二萜-3-肉豆蔻酸酯（ingenol-3-myristinate，Ⅲ）、没食子酸（gallicacid，Ⅳ）、1-O-a-L-阿拉伯糖-（1→6）-β-D-葡萄糖苷-3，7-二甲基-2-烯-7-羟基-辛醇（1-O-a-L-arabinofuranosyl-（1→6）-β-D-glucopyranosyl-3，7-dimethyl-oct-2-en-7-ol，Ⅴ）、1-O-没食子酰葡萄糖苷（1-O-galloyl-β-D-glucose，Ⅵ）。结论巨大戟烷型二萜酯类化合物Ⅱ和Ⅲ为新化合物，其他化合物均为首次从该植物中分离得到，单萜二糖苷类化合物Ⅴ系首次在该属中发现。

尖苞雪莲的化学成分

利用硅胶柱层析、Sephadex LH-20和MCI等分离和纯化手段,从产于青海的菊科风毛菊属植物尖苞雪莲的全草中,分离得到5个化合物,经IR、NMR(1D、2D)、MS等现代波谱技术鉴定它们为芦丁、槲皮素-5-O-β-D-葡萄糖苷、丁香苷、胡萝卜苷和β-谷甾醇,其化学成分为首次报道。

黄头鹡鸰产卵能学的研究

在自然生长状态下 ,用测定在黄头鹡鸰(Motacillacitreola)成鸟产卵期性腺及输卵管蛋的质量变化的方法 ,研究了它们在产卵期间的能量投入及其分配形式 .产卵期 ,雌鸟用于性腺增长和卵形成的能量预算为 6 6 2 4～71 19kJ ,其中用于蛋白质和脂肪的能量分别为 40 42kJ和 2 5 82～ 30 78kJ,占总能量预算的 5 6 8%～ 6 1 0 %和36 3%～ 46 5 % .用于繁殖蛋白的能量分配为 :输卵管 2 77kJ ,卵黄蛋白 12 80kJ ,卵白蛋白 2 4 85kJ.雌鸟耗能最大的是第 0d～ +1d ,达 (13 2 8～ 14 2 6 )kJ/d .产卵期雄鸟耗能 (用于性腺增长 ) 7 98kJ,雌雄亲鸟用于卵的形成和性腺增长共投入能量 74 2 2～ 79 17kJ.雌鸟耗能为雄鸟的 8 3倍～ 8 9倍。

黄头鹡鸰鸟生长能学的研究

在黄头鹡鸰(Motacillacitreola)自然生长状态下 ,通过测定雏鸟气体代谢和身体组织能量积累的方法 ,研究了它们在生长期间的能量投入 .最小能量投入在 0～ 1d (d为日龄 ,用t表示 ) ,为 10 82kJ/d . 1只雏鸟需要能量为 414 2 6kJ,其中生产能为 15 3 14kJ,用于维持的能量是 2 6 1 12kJ .

珊瑚树叶片叶绿素荧光非光化学猝灭的日变化和季节变化

用脉冲调制荧光仪观测了珊瑚树叶片叶绿素荧光非光化学猝灭（qE）的日变化和季节变化后发现：在晴天，qE及其慢弛豫组分（qE-slow）随着光强的增加而升高，中午达最高值，之后随光强的减弱而下降；阴天时，这两个指标的日变化不明显。在不同季节，相同日时间和同一光照强度下测定珊瑚树叶片的qE和qE-slow，两个指标在冬季明显高于春、秋两季；在短时间（1d）内改变强光下的叶片周围的温度，叶片的qE和qE-slow在高温和低温下均高于过温下测定的结果。

The glycosides from Lomatogonium rotatum

A new phenyl glycoside, 2-(3'-O-beta-D-glucopyranosyl) benzoyloxygentisic acid (1), along with seven known glycosides 2-8 was isolated from Tibetan herbal medicine Lomatogonium rotatum. The structures of the compounds were elucidated by spectroscopic methods including 1D and 2D NMR techniques and MS data.

基于线性不变矩和角度向量的立体匹配算法

针对室内场景双目立体匹配有别于一般场景立体匹配的特殊性,提出了一种计算简便、准确度高的立体图像匹配算法。该算法首先利用canny算子检测物体的边缘,根据边缘的线性不变矩寻找出目标物体,然后提取出目标物体轮廓的特征点,利用角度直方图计算出左右图像的旋转角度,最后利用角度向量实现左右图像的对应像素点的匹配。线性不变矩有效地将计算复杂度由二维降低到一维,大大降低了计算量。角度向量的提出降低了特征点匹配的复杂度,而且计算简便,准确率高。实验表明,该算法对图像的缩放、旋转、平移均免疫,具有较高的识别精度和良好的抗干扰性,计算效率高于传统方法,有着较高的应用价值。

机器人视觉导航中的实时在线识别算法研究

随着移动机器人应用范围的日益扩展，在动态、非结构化环境下提高其自主导航能力已经成为移动机器人研究领域迫切需要解决的问题。在机器人自主导航关键技术中，识别技术是最难解决、也是最急需解决的问题。视觉作为导航中的重要传感器，与其他传感器相比具有信息量大、重量轻便、功耗低等诸多优势，因此基于视觉的识别技术也被公认为最具潜力的研究方向。 本文以国防基础研究项目和中科院开放实验室基金项目为依托，以沈阳自动化所自主研发的“轮腿复合结构机器人”和“无人机”为实验平台，针对地面自主机器人和无人机自主导航中迫切需要解决的应用问题，有针对性的展开研究，旨在提高移动机器人在动态、非结构化环境下的适应能力。 本论文的主要内容如下： 首先，为了提高复杂环境下地面移动机器人的自主能力，本文提出了一种基于立体视觉的面向室外非结构化环境障碍物检测算法。文中首先给出了一种可以从V视差图(V-disparity image)中有效估计地面主视差（Main Ground Disparity, MGD)的方法。随后，我们利用由粗到精逐步判断的方式，来识别疑似障碍和最终障碍并对障碍进行定位。最后，该方法已在地面自主移动平台得到实际应用。通过在各种场景下的实验，验证了该方法的准确性和快速性。 其次，以无人机天际线识别为背景，提出了一种准确、实时的天际线识别算法，并由此估计姿态角。通过对天际线建立能量泛函模型，利用变分原理推出相应偏微分方程。在实际应用中出于对实时性的考虑，引入分段直线约束对该模型进行简化，然后利用由粗到精的思想识别天际线。具体做法是：首先，对图像预处理并垂直剖分，然后利用简化的水平直线模型对天际线进行粗识别，通过拟合获得天际线粗识别结果，最后在基于梯度和区域混合开曲线模型约束下精确识别天际线，并由此估计无人机滚动和俯仰姿态角。 第三，通过对红外机场跑道的目标特性进行分析，文中设计了一种新的基于1D Haar 小波的并行的红外图像分割算法的；然后，有针对性的对分割区域提取特征；最后，两种常用的识别方法，支持向量机（SVM）和投票法（Voting）被用于对疑似目标区域进行分类和识别。通过对实际视频和红外仿真图片的测试，验证了本文算法的快速性、可靠性和实时性，该算法每帧平均处理时间为30ms。 最后，针对无人机空中巡逻中对人群进行自动监控所遇到的问题，通过将此类问题简化为固定视角下人流密度监测问题，提出了一种全新的基于速度场估计的越线人流计数和区域内人流密度估计算法。 首先，该算法把越线的人流当成运动的流场，给出了一种有效估计1D速度场的运动估计模型；然后，通过对动态人流进行速度估计和积分，将越线人流的拼接成动态区域；最后，对各个动态区域提取面积和边缘信息，利用回归分析实现对人流密度估计。该方法与以往基于场景学习的方法不同，本文是一种基于角度的学习，因此便于实际应用。

线形脑啡肽(N—Tyr~1—Gly~2—Gly~3—Phe~4—Leu~5)的NMR溶液构象研究

本文在Bruker AM-400 NMR谱仪上,在不同温度下研究了线形脑啡肽(N-Tyr~1-Gly~2-Gly~3-Phe~4-Leu~5)在DMSO中的NMR溶液构象。由NMR测试结果,得到了NH化学位移温度梯度系数、扭转角φ、χ＇约束和~1H-~1H NOE距离约束,用目标函数法计算了脑啡肽的溶液构象,分析了优势边链构象。研究结果指明了多肽骨架的柔变性且处于构象平衡中。

叠前地震数据规则化、重建及噪音压制

I address of reconstruction of spatial irregular sampling seismic data to regular grids. Spatial irregular sampling data impairs results of prestack migration, multiple attenuations, spectra estimation. Prestack 5-D volumes are often divided into sub-sections for further processing. Shot gathers are easy to obtain from irregular sampling volumes. My strategy for reconstruction is as follows: I resort irregular sampling gathers into a form of easy to bin and perform bin regularization, then utilize F-K inversion to reconstruct seismic data. In consideration of poor ability of F-K regularization to fill in large gaps, I sort regular sampling gathers to CMP and proposed high-resolution parabolic Radon transform to interpolate data and extrapolate offsets. To strong interfering noise--multiples, I use hybrid-domain high-resolution parabolic Radon transform to attenuate it. F-K regularization demand ultimately for lower computing costs. I proposed several methods to further improve efficiency of F-K inversion: first I introduce 1D and 2D NFFT algorithm for a rapid calculation of DFT operators; then develop fast 1D and 2D CG method to solve least-square equations, and utilize preconditioner to accelerate convergence of CG iterations; what’s more, I use Delaunay triangulation for weight calculation and use bandlimit frequency and varying bandwidth technique for competitive computation. Numerical 2D and 3D examples are offered to verify reasonable results and more efficiency. F-K regularization has poor ability to fill in large gaps, so I rearrange data as CMP gathers and develop hybrid-domain high-resolution parabolic Radon transforms which be used ether to interpolate null traces and extrapolate near and far offsets or suppress a strong interfere noise: multiples. I use it to attenuate multiples to verify performances of our algorithm and proposed routines for industrial application. Numerical examples and field data examples show a nice performance of our method.

基于波动方程的层间多次波预测方法研究

In exploration geophysics，velocity analysis and migration methods except reverse time migration are based on ray theory or one-way wave-equation. So multiples are regarded as noise and required to be attenuated. It is very important to attenuate multiples for structure imaging, amplitude preserving migration. So it is an interesting research in theory and application about how to predict and attenuate internal multiples effectively. There are two methods based on wave-equation to predict internal multiples for pre-stack data. One is common focus point method. Another is inverse scattering series method. After comparison of the two methods, we found that there are four problems in common focus point method: 1. dependence of velocity model; 2. only internal multiples related to a layer can be predicted every time; 3. computing procedure is complex; 4. it is difficult to apply it in complex media. In order to overcome these problems, we adopt inverse scattering series method. However, inverse scattering series method also has some problems: 1. computing cost is high; 2. it is difficult to predict internal multiples in the far offset; 3. it is not able to predict internal multiples in complex media. Among those problems, high computing cost is the biggest barrier in field seismic processing. So I present 1D and 1.5D improved algorithms for reducing computing time. In addition, I proposed a new algorithm to solve the problem which exists in subtraction, especially for surface related to multiples. The creative results of my research are following: 1. derived an improved inverse scattering series prediction algorithm for 1D. The algorithm has very high computing efficiency. It is faster than old algorithm about twelve times in theory and faster about eighty times for lower spatial complexity in practice; 2. derived an improved inverse scattering series prediction algorithm for 1.5D. The new algorithm changes the computing domain from pseudo-depth wavenumber domain to TX domain for predicting multiples. The improved algorithm demonstrated that the approach has some merits such as higher computing efficiency, feasibility to many kinds of geometries, lower predictive noise and independence to wavelet； 3. proposed a new subtraction algorithm. The new subtraction algorithm is not used to overcome nonorthogonality, but utilize the nonorthogonality's distribution in TX domain to estimate the true wavelet with filtering method. The method has excellent effectiveness in model testing. Improved 1D and 1.5D inverse scattering series algorithms can predict internal multiples. After filtering and subtracting among seismic traces in a window time, internal multiples can be attenuated in some degree. The proposed 1D and 1.5D algorithms have demonstrated that they are effective to the numerical and field data. In addition, the new subtraction algorithm is effective to the complex theoretic models.

基于量子蒙特卡罗的地震波反演方法

The primary approaches for people to understand the inner properties of the earth and the distribution of the mineral resources are mainly coming from surface geology survey and geophysical/geochemical data inversion and interpretation. The purpose of seismic inversion is to extract information of the subsurface stratum geometrical structures and the distribution of material properties from seismic wave which is used for resource prospecting, exploitation and the study for inner structure of the earth and its dynamic process. Although the study of seismic parameter inversion has achieved a lot since 1950s, some problems are still persisting when applying in real data due to their nonlinearity and ill-posedness. Most inversion methods we use to invert geophysical parameters are based on iterative inversion which depends largely on the initial model and constraint conditions. It would be difficult to obtain a believable result when taking into consideration different factors such as environmental and equipment noise that exist in seismic wave excitation, propagation and acquisition. The seismic inversion based on real data is a typical nonlinear problem, which means most of their objective functions are multi-minimum. It makes them formidable to be solved using commonly used methods such as general-linearization and quasi-linearization inversion because of local convergence. Global nonlinear search methods which do not rely heavily on the initial model seem more promising, but the amount of computation required for real data process is unacceptable. In order to solve those problems mentioned above, this paper addresses a kind of global nonlinear inversion method which brings Quantum Monte Carlo (QMC) method into geophysical inverse problems. QMC has been used as an effective numerical method to study quantum many-body system which is often governed by Schrödinger equation. This method can be categorized into zero temperature method and finite temperature method. This paper is subdivided into four parts. In the first one, we briefly review the theory of QMC method and find out the connections with geophysical nonlinear inversion, and then give the flow chart of the algorithm. In the second part, we apply four QMC inverse methods in 1D wave equation impedance inversion and generally compare their results with convergence rate and accuracy. The feasibility, stability, and anti-noise capacity of the algorithms are also discussed within this chapter. Numerical results demonstrate that it is possible to solve geophysical nonlinear inversion and other nonlinear optimization problems by means of QMC method. They are also showing that Green’s function Monte Carlo (GFMC) and diffusion Monte Carlo (DMC) are more applicable than Path Integral Monte Carlo (PIMC) and Variational Monte Carlo (VMC) in real data. The third part provides the parallel version of serial QMC algorithms which are applied in a 2D acoustic velocity inversion and real seismic data processing and further discusses these algorithms’ globality and anti-noise capacity. The inverted results show the robustness of these algorithms which make them feasible to be used in 2D inversion and real data processing. The parallel inversion algorithms in this chapter are also applicable in other optimization. Finally, some useful conclusions are obtained in the last section. The analysis and comparison of the results indicate that it is successful to bring QMC into geophysical inversion. QMC is a kind of nonlinear inversion method which guarantees stability, efficiency and anti-noise. The most appealing property is that it does not rely heavily on the initial model and can be suited to nonlinear and multi-minimum geophysical inverse problems. This method can also be used in other filed regarding nonlinear optimization.