Wei Y.-Q.,Shandong Police College |
Liu D.,Shandong Normal University |
Duan L.-S.,Shandong Normal University
Proceedings of 2012 International Symposium on Information Technologies in Medicine and Education, ITME 2012 | Year: 2012
For mining sequential patterns on massive data set, the distributed sequential pattern mining algorithm based on MapReduce programming model and PrefixSpan is proposed. Mining tasks are decomposed to many small tasks, the Map function is used to mine each Prefix-Projected sequential pattern, and the projected databases were constructed parallelly. It simplifies the search space and acquires a higher mining efficiency. Then the intermediate values are passed to a Reduce function which merges together all these values to produce a possibly smaller set of values. Both theoretical analyses and experimental results show MR-PrefixSpan reduces the time of scanning database. It solves the problem of mining massive data effectively, has considerable speedup and scaleup performances with an increasing number of processors on the Hadoop platform. © 2012 IEEE.
Pan W.-X.,Shandong University |
Lai Y.-C.,Shandong University |
Wang R.-X.,Shandong Police College |
Zhang D.-J.,Shandong University |
Zhan J.-H.,Shandong University
Journal of Raman Spectroscopy | Year: 2014
To better understand experimentally observed surface-enhanced Raman Scattering (SERS) of polychlorinated biphenyls (PCBs) adsorbed on nanoscaled silver substrates, a systematic theoretical study was performed by carrying out density functional theory and time-dependent density functional theory calculations. 2,2′,5,5′-tetrachlorobiphenyl (PCB52) was chosen as a model molecule of PCBs, and Agn (n = 2, 4, 6, and 10) clusters were used to mimic active sites of substrates. Calculated normal Raman spectra of PCB52-Agn (n = 2, 4, 6, and 10) complexes are analogical in profile to that of isolated PCB52 with only slightly enhanced intensity. In contrast, the corresponding SERS spectra calculated at adopted incident light are strongly enhanced, and the calculated enhancement factors are 104 ~ 10 5. Thus, the experimentally observed SERS phenomenon of PCBs supported on Ag substrates should correspond to the SERS spectra rather than the normal Raman spectra. The dominant enhancement in Raman intensities origins from the charge transfer resonance enhancement between the molecule and clusters. Copyright © 2014 John Wiley & Sons, Ltd.
Pan W.,Shandong University |
Qi Y.,Shandong University |
Wang R.,Shandong Police College |
Han Z.,Shandong Academy of Sciences |
And 2 more authors.
Chemosphere | Year: 2013
The effective abatement of flue gas emissions, especially polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), is one of the challenging issues in the field of environmental science currently. Imidazolium-based dicyanamide ionic liquids (ILs) were proposed to have potential in controlling the emissions of PCDD/Fs. However, the relevant mechanism at the molecular level still remains unclear. To address this subject, we here present a combined molecular dynamics (MD) simulation and quantum chemical (QM) study on the adsorption of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), the most toxic congener among PCDD/F family, by 1-butyl-3-methylimidazolium dicyanamide IL, a representative imidazolium dicyanoamide ILs, which were demonstrated to possess high capture capability for PCDD/Fs. The MD simulation results show that TCDD molecules can be effectively adsorbed on the IL surface to form a dense layer, but cannot enter the interior of the IL. The results of QM calculations show that the adsorption of TCDDs on the IL surface occurs via intra-molecular hydrogen bond interactions. The calculated interaction energy of the anion with TCDD molecule is two times more than that of the cation, implying that the IL anion dominates the interaction with TCDD molecules, while the cation plays a secondary role. Based on the calculated results, we propose that imidazolium dicyanamide IL films/membranes may be better materials than the corresponding bulk for capturing TCDD. The present theoretical results may be helpful to designing the functional ILs which effectively capture and concentrate PCDD/F compounds. © 2013 Elsevier Ltd.
Wang R.,Shandong Police College |
Zhang D.,Shandong University |
Liu C.,Shandong University
Computational Materials Science | Year: 2014
To explore a novel sensor to detect toxic pollutant in the atmosphere, we investigate reactivities of the germanium doped (Ge-doped) (8, 0) single-walled boron nitride nanotubes (BNNTs) towards carbon monoxide (CO) and nitric oxide (NO) by performing density functional theory (DFT) calculations. CO and NO are found to present strong chemisorption on the Ge-doped BNNT with substituted boron and nitrogen defect site. Calculated data for the electronic density of states and the electronic charge densities further indicate that the doping of Ge atom improves the electronic transport property of the BNNT, induces magnetism of the BNNT, and increases its adsorption sensitivity towards CO and NO. Doping BNNTs with Ge is expected to be an available strategy for improving the properties of BNNTs, and Ge-doped BNNT is expected to be a potential resource for detecting the presence of CO and NO. © 2013 Elsevier B.V. All rights reserved.
Wang H.,Shandong Police College |
Wang H.,Shandong University |
Li H.,Shandong University
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2015
When cleaning seed cotton, cleaning devices of different types had different cleaning efficiencies on different types of impurities. Therefore, the classification identification of seed cotton impurities had a guiding significance for adjusting the parameter of seed cotton cleaning equipment. A classification recognition algorithm of impurities in seed cotton based on local binary pattern and gray level co-occurrence matrix was proposed in this paper. First, the images were transformed to local binary pattern images, and so the gray value of each pixel was also converted to the local binary pattern value. Local binary pattern reflected the micro-structure of the center pixel and its 3×3 neighborhood, but it could not reflect a wider range of image structure. If the micro-structures of images were similar but macro-structures were different, the local binary pattern could not effectively distinguish the images. Gray level co-occurrence matrix was used to the statistics on the position of pixel pair. The pixel pairs had some relationship of gray values. The distances of pixel pairs could be controlled by the step length. In this paper, gray level co-occurrence matrix was used for local binary pattern image. It could describe the image structures of different scales by adjusting the step-length value. This paper calculated the characteristic values of seed cotton images and all kinds of impurities images with the step-length values from 1 to 8. The characteristics included contrast, angular second moment, correlation and inverse difference moment. The test results showed that these characteristics could distinguish seed cotton and every kind of impurity when the step-length value was equal to 3 or 4. The classifier of this algorithm used the support vector machine. In solving the small-sample, nonlinear and high-dimension problems, the support vector machine had more advantages than the traditional machine learning methods. The support vector machine was a typical two-class classifier. But classification recognition of seed cotton and impurities needed multi-class classifier. Several classifiers of support vector machine were combined into one multi-class classifier, and radial basis function was used as the kernel function of the classifier. This paper compared the standard local binary pattern algorithm (LBP), the standard gray level co-occurrence matrix algorithm (GLCM) and the algorithm designed in this paper (LBP-GLCM). The test results showed that the average recognition rate of the algorithm designed in this paper, which reached 94%, was higher than the LBP algorithm and the GLCM algorithm. Among different objects, the recognition rate of the boll shell and the cotton bush was 100%, the recognition rate of the leaf fragment was 92%, the recognition rate of the dust miscellaneous was 94%, the recognition rates of the deed cotton and barren cotton seed were 90% and 88%, respectively. The recognition rate could satisfy the demand of practical application. ©, 2015, Chinese Society of Agricultural Engineering. All right reserved.