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从采自青岛近海的红藻多管藻(Polysiphonia urceolata)中分离到一株毛壳霉属真菌球毛壳菌(Chaetomium globosum.),对其发酵代谢产物的化学成分进行了研究。采用溶剂提取、分配,硅胶柱层析、葡聚糖凝胶柱层析以及制备薄层层析等技术已分离获得6个单体化合物,通过一维、二维超导核磁共振技术、质谱技术等鉴定了其中4个化合物的结构,均为苯甲醛类衍生物,分别为2-(1-庚烯基)-3、6-二羟基-5-(3-甲基-2-丁烯基)苯甲醛(Ⅰ)、2-庚基-3、6-二羟基-5-(3-甲基-2-丁烯基)苯甲醛(Ⅱ)、2-(3、5-庚二烯基)-3、6-二羟基-5-(3-甲基-2-丁烯基)苯甲醛(Ⅲ)、2-(1、3、5-庚三烯基)-3、6-二羟基-5-(3-甲基-2-丁烯基)苯甲醛(Ⅳ)。 相似文献
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Dense profile-oriented resistivity data allows for 2D and 3D inversions. However, huge amounts of data make it practically impossible to do full 2D or 3D inversions on a routine basis. Therefore, a number of approximations have been suggested over the years to speed up computations. We suggest using a combination of Broyden's update on the Jacobian matrix with derivatives calculated using a 1D formulation on a parameterized 2D model of locally 1D layered models. The approximations bring down the effective number of 2D forward responses to a minimum, which again gives us the ability to invert very large sections. Broyden's update is not as useful with a parameterized problem as is the case with a smooth minimum structure problem that has been the usual application. 1D derivatives, however, seem to be very effective when initiating a full 2D solution with Broyden's update. We compare the different methods using two different kinds of data on two synthetic models and on two field examples. The most effective and reliable optimization combines 1D derivatives with a full 2D solution and Broyden's update. When using Broyden's update the Jacobian matrix needs to be reset every once in a while. We do this whenever the difference in data residual from the previous iteration is less than 5%. This combined inversion method reduces the computation time approximately a factor of 3 without losing model resolution. 相似文献
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等高线内插是提高地形图精度的一个常用手段,在地图综合等领域中有很重要的作用。在实际地形图数据库中,等高线除了存在完整的计曲线以外,还有一部分断裂的首曲线。已有的等高线内插研究中未考虑对此类数据的处理,为此,本文提出了一种基于Fréchet距离的断裂等高线内插算法。首先提出了等高线度量关系的计算方法,然后对等高线的节点均匀加密,并利用Fréchet距离进行相似度判断以选择参考等高线,最后根据“最近点”的方式进行插值。通过对江苏某地区实际数据的试验,验证了该算法的合理性,对于鞍部地区的内插,有更好的适应性和准确性。 相似文献
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This paper deals with the application of nonparametric system identification to a nonlinear maneuvering model for large tankers using artificial neural network method. The three coupled maneuvering equations in this model for large tankers contain linear and nonlinear terms and instead of attempting to determine the parameters (i.e. hydrodynamic derivatives) associated with nonlinear terms, all nonlinear terms are clubbed together to form one unknown time function per equation which are sought to be represented by the neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag maneuvers and the proposed method has been applied to these data. The neural network scheme adopted in this work has one middle or hidden layer of neurons and it employs the Levenberg–Marquardt algorithm. Using the best choices for the number of hidden layer neurons, length of training data, convergence tolerance etc., the performance of the proposed neural network model has been investigated and conclusions drawn. 相似文献
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