新型磷系膨胀阻燃剂

介绍了一种新型磷酸盐,通过试验分析了其作为膨胀型阻燃剂对聚烯烃及热固性塑料的阻燃机理与阻燃效果;红磷 酚类体系在高温聚合物中有较好阻燃效果     

可拓检测的方案生成

以可拓学理论为基础，论述了可拓检测的有关定义和概念，提出了可拓检测的方案生成方法及过程，同时探讨了检测方法的优化和评价方法。     

Adaptive cell division for ray tracing

In ray tracing the two most commonly used data structures are the octree and uniform cell division. The octree structure allows efficient adaptive subdivision of space, while taking care of the spatial coherence of the objects in it; however, the tree structure locating the next node in the path of a ray is complex and time consuming. The cell structure, on the other hand, can be stored in a three-dimensional array, and each cell can be efficiently accessed by specifying three indices. However, such a uniform cell division does not take care of object coherence. The proposed data structure combines the positive features of the above data structures while minimising their disadvantages. The entire object space is implicitly assumed to be a three-dimensional grid of cells. Initially, the entire object space is a single voxel which later undergoes “adaptive cell division.” But, unlike in the octree structure, where each voxel is divided exactly at the middle of each dimension, in adaptive cell division, each voxel is divided at the nearest cell boundary. The result is that each voxel contains an integral number of cells along each axis. Corresponding to the implicit cell division we maintain a three-dimensional array, with each array element containing the voxel number which is used to index into the voxel array. The voxel array is used to store information about the structure of each voxel, in particular, the objects in each voxel. While a ray moves from one voxel to another we always keep track of the cell through which the ray is currently passing. Since only arrays are involved in accessing the next voxel in the path of the ray, the operation is very efficient.     

Finite-element grid improvement by minimization of stiffness matrix trace

A new and simple method of finite-element grid improvement is presented. The objective is to improve the accuracy of the analysis. The procedure is based on a minimization of the trace of the stiffness matrix. For a broad class of problems this minimization is seen to be equivalent to minimizing the potential energy. The method is illustrated with the classical tapered bar problem examined earlier by Prager and Masur. Identical results are obtained.     

测定钠中杂质的手动阻塞计的研究

文章介绍了测定钠中杂质浓度的手动阻塞计的原理、装置和实验结果。为了减少测量误差,我们研究了影响准确测定阻塞温度的因素,并且找到了减少测量误差的办法。在同样的杂质饱和温度下,该阻塞计测得的高、低阻塞温度所对应的杂质浓度差是很接近的。对氧其差值为1.03ppm;对氢为0.0763ppm。     

Microscopical studies of water/flour systems

Summary Wheat flour particles sprinkled on a water surface form strands which are visible in the light microscope. These strands form aggregates by means of mechanical forces which show viscoelastic behaviour. Amino acid analysis of both the protein strands and the aggregates formed shows that they consist of gluten protein. The formation of the protein strands occurs at the air/water interface and results from the action of a protein film which spreads around the flour particles.

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Mikroskopische Untersuchungen von Mehl/Wasser-Systemen

Zusammenfassung Weizenmehlpartikel, die auf Wasser gestreut werden, bilden lichtmikroskopisch sichtbare Stränge aus. Durch mechanische Beanspruchung lassen sich these Stränge zu Aggregaten zusammenfassen, die visco-elastisches Verhalten zeigen. Die Aminosäureanalyse sowohl der Proteinstrange wie auch der daraus gebildeten Aggregate zeigt, daß es sich dabei um Kleberprotein handelt. Die Proteinfäden entstehen an der Wasser-Luft-Grenzflache als Folge eines spreitenden Proteinfilms, der sich um die Mehlpartikel herum ausbreitet.

     

准噶尔盆地侏罗系储集层的基本特征及其主控因素分析

从描述储集层基本特征、分析储集层性质的主控因素、建立储集层成因模式和评价、预测储集层性质这 4个层次 ,研究准噶尔盆地重点勘探目标区的侏罗系储集层。系统总结侏罗系储集层岩石学特征 ,认为成岩压实作用强弱是控制储集层性质的关键因素 ;指出相对优质储集层均为剩余原生粒间孔隙型储集层 ,溶蚀作用对渗透率贡献小 ;非煤系储集层的孔隙保存条件明显比煤系储集层好 ,这是三工河组、头屯河组和吐谷鲁群的物性普遍优于八道湾组和西山窑组的原因。在确定储集层性质的控制因素和控制机理基础上 ,建立了煤系和非煤系两大类储集层的成因 演化模型。在上述研究的基础上 ,分地区、分层位评价了储集层 ,并预测了不同渗透率储集层的深度界线及相对优质储集层的平面分布。图 6参 10     

Image processing using cellular neural networks based on multi-valued and universal binary neurons

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at