Parameters Inversion of Fluid-Saturated Porous Media Based on Neural Networks

The multi-layers feedforward neural network is used for inversion of material constants of fluid-saturated porous media. The direct analysis of fluid-saturated porous media is carried out with the boundary element method. The dynamic displacement responses obtained from direct analysis for prescribed material parameters constitute the sample sets training neural network. By virtue of the effective L-M training algorithm and the Tikhonov regularization method as well as the GCV method for an appropriate selection of regularization parameter, the inverse mapping from dynamic displacement responses to material constants is performed. Numerical examples demonstrate the validity of the neural network method.

A new method based on the construction of hypothetical symmetrical networks for fixture calibration in network analyzers

At least three known standards are normally required for the full two-port test fixture calibration in vector network analyzers (VNA). In this paper, a calibration procedure using only one standard, based on establishing two hypothetical symmetrical fixtures using triple-through method, is shown. The results using the calibrating method to subtract the influence of fixtures are in accord with the directly measured data of the device-under-test (DUT) without the fixtures very well, which shows that the proposed method is very simple and accurate.

A novel face recognition method based on hypersausage neural networks

In this paper, we firstly give the nature of 'hypersausages', study its structure and training of the network, then discuss the nature of it by way of experimenting with ORL face database, and finally, verify its unsurpassable advantages compared with other means.

Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.

Range-based attacks on links in random scale-free networks

Range and load play key roles in the problem of attacks on links in random scale-free (RSF) networks. In this paper we obtain the approximate relation between range and load in RSF networks by the generating function theory, and then give an estimation about the impact of attacks on the efficiency of the network. The results show that short-range attacks are more destructive for RSF networks, and are confirmed numerically.

Geographical constraints to range-based attacks on links in complex networks

In this paper, we studied range-based attacks on links in geographically constrained scale-free networks and found that there is a continuous switching of roles of short-and long-range attacks on links when tuning the geographical constraint strength. Our results demonstrate that the geography has a significant impact on the network efficiency and security; thus one can adjust the geographical structure to optimize the robustness and the efficiency of the networks. We introduce a measurement of the impact of links on the efficiency of the network, and an effective attacking strategy is suggested

Construction of coordination networks with high connectivity: a new 8-connected self-penetrating network based on tetranuclear metal clusters

Two highly connected cobalt(II) and zinc(II) coordination polymers with tetranuclear metal clusters as the nodes of network have been prepared, being the first example of an 8-connected self-penetrating net based on a cross-linked alpha-Po subnet.

New high-dimensional networks based on polyoxometalate and crown ether building blocks

Three novel supramolecular assemblies constructed from polyoxometalate and crown ether building blocks, [(DB18C6)Na(H2O)(1.5)](2)Mo6O19.CH3CN, 1, and [{Na(DB18C6)(H2O)(2)}(3)(H2O)(2)]XMo12O40.6DMF.CH3CN (X = P, 2, and As, 3; DB18C6 = dibenzo-18-crown-6; DMF = N,N-dimethylfomamide), have been synthesized and characterized by elemental analyses, IR, UV-vis, EPR, TG, and single crystal X-ray diffraction. Compound 1 crystallizes in the tetragonal space group P4/mbm with a = 16.9701(6) Angstrom, c = 14.2676(4) Angstrom, and Z = 2. Compound 2 crystallizes in the hexagonal space group P6(3)/m with a = 15,7435(17) Angstrom, c = 30.042(7) Angstrom, gamma = 120degrees, and Z = 2. Compound 3 crystallizes in the hexagonal space group P6(3)/m with a = 15.6882(5) Angstrom, c = 29.9778(18) Angstrom, gamma = 120degrees, and Z = 2. Compound 1 exhibits an unusual three-dimensional network with one-dimensional sandglasslike channels based on the extensive weak forces between the oxygen atoms on the [Mo6O19](2-) polyoxoanions and the CH2 groups of crown ether molecules, Compounds 2 and 3 are isostructural, and both contain a novel semiopen cagelike trimeric cation [{Na(DB18C6)(H2O)(2)}(3)(H2O)(2)](3+). In their packing arrangement, an interesting 2-D "honeycomblike" "host" network is formed, in which the [XMo12O40](3-) (X = As and P) polyoxoanion "guests" resided.

Novel polyoxometalate-templated, 3-D supramolecular networks based on lanthanide dimers: Synthesis, structure, and fluorescent properties of [Ln(2)(DNBA)(4)(DMF)(8)][Mo6O19] (DNBA=3,5-dinitrobenzoate)

The spherical Lindquist type polyoxometalate, Mo6O192-, has been used as a noncoordinating anionic template for the construction of novel three-dimensional lanthanide-aromatic monocarboxylate dimer supramolecular networks [Ln(2)(DNBA)(4)(DMF)(8)][Mo6O19] (Ln = La 1, Ce 2, and Eu 3, DNBA = 3,5-dinitrobenzoate, DMF = dimethylformamide). The title compounds are characterized by elemental analyses, IR, and single-crystal X-ray diffractions. X-ray diffraction experiments reveal that two Ln(III) ions are bridged by four 3,5-dinitrobenzoate anions as asymmetrically bridging ligands, leading to dimeric cores, [Ln(2)(DNBA)(4)(DMF)(8)](2+); [Ln(2)(DNBA)(4)(DMF)(8)](2+) groups are joined together by pi-pi stacking interactions between the aromatic groups to form a two-dimensional grid-like network; the 2-D supramolecular layers are further extended into 3-D supramolecular networks with 1-D box-like channels by hydrogen-bonding interactions, in which hexamolybdate polyanions reside. The compounds represent the first examples of 3-D carboxylate-bridged lanthanide dimer supramolecular "host" networks formed by pi-pi stacking and hydrogen-bonding interactions encapsulating noncoordinating "guest" polyoxoanion species. The fluorescent activity of compound 3 is reported.

Dynamic mechanical properties of interpenetrating polymer networks based on polyacrylates and epoxy

Interpenetrating polymer networks (IPNs) based on polyacrylate (poly(polyethylene glycol diacrylate), PEGDA) and epoxy(diglycidyl ether of bisphenol A, DGEBA) were prepared simultaneously Dynamic mechanical properties of the SINs (simultaneous interpenetrating networks) with various compositions were studied. Enhanced mechanical properties were found in this case. From the point of view of pre-swollen networks, all of the PEGDA/DGEBA IPNs were composed of the individual pre-swollen networks. A micro-phase segregation system was produced in the SIN. Glass transition temperatures shifted inward, which was attributed to molecular packing effects or mutual-entanglements of molecular segments among the individual pre-swollen networks. In accordance with the additivity of properties, namely the parallel model, the entanglement density between the two polymer networks reached its maximum at 50/50 PEGDA/DGEBA IPN.

Synthesis and characterization of interpenetrating polymer networks based on polyacrylate and epoxy

The synthesis and properties of simultaneously interpenetrating networks (SINs) based on poly(polyethylene glycol diacrylate) (PEGDA) and epoxy (diglycidyl ether of bisphenol A, DGEBA) were studied. The effect of compositional variation on the morphology and properties of products was investigated. The swelling coefficient, densities, glass transition behavior, and thermal stability of these interpenetrating networks (IPNs) are discussed. Microphase separation morphological structures were found in all PEGDA/DGEBA IPNs. Decreased swelling ratios compared to the calculated swelling coefficients based on the weight additivity of the components were obtained after the formation of IPNs. Increased density and thermal stability were also obtained in these IPNs, implying the existence of interpenetration (topological entanglements) among the component networks.

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.