An Extension of Self-Organizing Maps to Categorical Data

Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on categorical data is still a challenge for SOM. This paper aims to extend standard SOM to handle feature values of categorical type. A batch SOM algorithm (NCSOM) is presented concerning the dissimilarity measure and update method of map evolution for both numeric and categorical features simultaneously.

The influence of topography and land use on water quality of Xiangxi River in Three Gorges Reservoir region

A self-organizing map (SOM) was used to cluster the water quality data of Xiangxi River in the Three Gorges Reservoir region. The results showed that 81 sampling sites could be divided into several groups representing different land use types. The forest dominated region had low concentrations of most nutrient variables except COD, whereas the agricultural region had high concentrations of NO3N, TN, Alkalinity, and Hardness. The sites downstream of an urban area were high in NH3N, NO2N, PO4P and TP. Redundancy analysis was used to identify the individual effects of topography and land use on river water quality. The results revealed that the watershed factors accounted for 61.7% variations of water quality in the Xiangxi River. Specifically, topographical characteristics explained 26.0% variations of water quality, land use explained 10.2%, and topography and land use together explained 25.5%. More than 50% of the variation in most water quality variables was explained by watershed characteristics. However, water quality variables which are strongly influenced by urban and industrial point source pollution (NH3N, NO2N, PO4P and TP) were not as well correlated with watershed characteristics.

Microsatellites assessment of Chinese sturgeon (Acipenser sinensis Gray) genetic variability

Four microsatellites were used to examine the genetic variability of the spawning stocks of Chinese sturgeon, Acipenser sinensis, from the Yangtze River sampled over a 3-year period (1999-2001). Within 60 individuals, a total of 28 alleles were detected over four polymorphic microsatellite loci. The number of alleles per locus ranged from 4 to 15, with an average allele number of 7. The number of genotypes per locus ranged from 6 to 41. The genetic diversity of four microsatellite loci varied from 0.34 to 0.67, with an average value of 0.54. For the four microsatellite loci, the deviation from the Hardy-Weinberg equilibrium was mainly due to null alleles. The mean number of alleles per locus and the mean heterozygosity were lower than the average values known for anadromous fishes. Fish were clustered according to their microsatellite characteristics using an unsupervised 'Artificial Neural Networks' method entitled 'Self-organizing Map'. The results revealed no significant genetic differentiation considering genetic distance among samples collected during different years. Lack of heterogeneity among different annual groups of spawning stocks was explained by the complex age structure (from 8 to 27 years for males and 12 to 35 years for females) of Chinese sturgeon, leading to formulate an hypothesis about the maintenance of genetic diversity and stability in long-lived animals.

Conservation strategies for endemic fish species threatened by the Three Gorges Dam

The largest damming project to date, the Three Gorges Dam has been built along the Yangtze River (China), the most species-rich river in the Palearctic region. Among 162 species of fish inhabiting the main channel of the upper Yangtze, 44 are endemic and are therefore under serious threat of global extinction from the dam. Accordingly, it is urgently necessary to develop strategies to minimize the impacts of the drastic environmental changes associated with the dam. We sought to identify potential reserves for the endemic species among the 17 tributaries in the upper Yangtze, based on presence/absence data for the 44 endemic species. Potential reserves for the endemic species were identified by characterizing the distribution patterns of endemic species with an adaptive learning algorithm called a "self-organizing map" (SOM). Using this method, we also predicted occurrence probabilities of species in potential reserves based on the distribution patterns of communities. Considering both SOM model results and actual knowledge of the biology of the considered species, our results suggested that 24 species may survive in the tributaries, 14 have an uncertain future, and 6 have a high probability of becoming extinct after dam filling.

基于独立分量分析的地震属性分析方法研究

Conventional seismic attribute analysis is not only time consuming, but also has several possible results. Therefore, seismic attribute optimization and multi-attribute analysis are needed. In this paper, Fuyu oil layer in Daqing oil field is our main studying object. And there is much difference between seismic attributes and well logs. So under this condition, Independent Component Analysis (ICA) and Kohonen neural net are introduced to seismic attribute optimization and multi-attribute analysis. The main contents are as follows: (1) Now the method of seismic attribute compression is mainly principal component analysis (PCA). In this article, independent component analysis (ICA), which is superficially related to PCA, but much more powerful, is used to seismic reservoir characterizeation. The fundamental, algorithms and applications of ICA are surveyed. And comparation of ICA with PCA is stydied. On basis of the ne-entropy measurement of independence, the FastICA algorithm is implemented. (2) Two parts of ICA application are included in this article: First, ICA is used directly to identify sedimentary characters. Combined with geology and well data, ICA results can be used to predict sedimentary characters. Second, ICA treats many attributes as multi-dimension random vectors. Through ICA transform, a few good new attributes can be got from a lot of seismic attributes. Attributes got from ICA optimization are independent. (3) In this paper, Kohonen self-organizing neural network is studied. First, the characteristics of neural network’s structure and algorithm is analyzed in detail, and the traditional algorithm is achieved which has been used in seism. From experimental results, we know that the Kohonen self-organizing neural network converges fast and classifies accurately. Second, the self-organizing feature map algorithm needs to be improved because the result of classification is not very exact, the boundary is not quite clear and the velocity is not fast enough, and so on. Here frequency sensitive principle is introduced. Combine it with the self-organizing feature map algorithm, then get frequency sensitive self-organizing feature map algorithm. Experimental results show that it is really better. (4) Kohonen self-organizing neural network is used to classify seismic attributes. And it can be avoided drawing confusing conclusions because the algorithm’s characteristics integrate many kinds of seismic features. The result can be used in the division of sand group’s seismic faces, and so on. And when attributes are extracted from seismic data, some useful information is lost because of difference and deriveative. But multiattributes can make this lost information compensated in a certain degree.

Controlled synthesis of nanoporous Au microsheet via surfactant emulsion template

A facile and wet-chemical approach was employed to control synthesis of self-organizing, hyperbranched nanoporous Au microsheet with high quality in bulk quantity. This method produced nanoporous Au microsheets with a thickness of 7-15 nm. The microsheets were composed of irregularly interconnected planar Au nanoplates with interstices, i.e. nanopores of 10-50 nm. And the nanoporous Au microsheets were enveloped in 10-30 nm thick polyaniline (PANI) sheaths. The morphology of the nanostructured Au composites could also be easily tuned by changing the concentration of aniline and chlorauric acid. The dendritic and epitaxial growth of nanoporous Au microsheet was believed as the diffusion-limited process confined in the lamellar emulsion phase through self-assembly of aniline and dodecylsulfate. The solution reaction proceeded at a mild condition (room temperature and aqueous solutions), and less toxic reagents were employed instead of extreme toxic and corrosive chemicals.

Solvent-vapor treatment induced performance enhancement of poly(3-hexylthiophene): methanofullerene bulk-heterojunction photovoltaic cells

The authors report enhanced poly(3-hexylthiophene) (P3HT):methanofullerene (PCBM) bulk-heterojunction photovoltaic cells via 1,2-dichlorobenzene (DCB) vapor treatment and thermal annealing. DCB vapor treatment can induce P3HT self-organizing into ordered structure leading to enhanced absorption and high hole mobility. Further annealing the device at a high temperature, PCBM molecules begin to diffuse into aggregates and together with the ordered P3HT phase form bicontinuous pathways in the entire layer for efficient charge separation and transport. Compared to the control device that is merely annealed, optical absorption, short-circuit current, and power conversion efficiency are increased for the DCB vapor-treated cell.

基于LabVIEW的自组织网络的实现与应用

文章介绍了自组织神经网络在故障诊断方面的应用原理,针对自组织神经网络实现问题提出了一种通过在LabVIEW调用MATLAB应用程序实现自组织神经网络的方法。并通过轴承故障诊断的实例,证明了这种方法的有效性。