摘要:Abstract:The carboxyl groups of anionic aqueous polyurethane dispersions (PUD) serve as an internal emulsifier stabilizing PUD and cause the PUD film exhibit high hydrophilicity.In order to improve the performances of PUD film, PUD were crosslinked by sorbitol glycidyl ether (GE-60) and tetraglycidyl meta-xylene diamines (GA-240), respectively. The effect of crosslinkers on the water-resistance, alcohol-resistance, hardness, and thermal stability of PUD films were studied. The FTIR spectrum showed that the carboxyl and amino groups of PUD react with epoxy groups of GA-240 and GE-60 during the drying process of PUD. The properties of PUD films indicated that the water solvent-resistance,flexibility and cold crack resistance of PUD films cured by GA-240 and GE-60 are improved impressively;GA-240 also increases the gel content and hardness of PUD films up by 89.0% and 0.83, respectively, and the optimal mass ratio of GA-240 and PUD(m(GA-240)∶m(PUD))is 0.02, while the corresponding mass ratio of GE-60 and PUD (m(GE-60)∶m(PUD))is 0.012. The TGA curves determined that the thermal stability of PUD crosslinked by GA-240 and GE-60 is better than that of PUD.
摘要:Abstract:Due to the high dimensionality and sparseness of text data, the performance of traditional clustering algorithm may not be satisfied in clustering text data. The largest dense region having a small coverage rate with the partitioned clusters was selected out as initial cluster centroid set gradually by learning the similarity information between the partitioned and remainning sets. After generating the predetermined number of initial cluster centroid set, the remaining documents were assigned to their nearest clusters. By this way, the sensitivity of the clustering algorithm to the initial cluster centroid was reduced. Some threshold values used in this algorithm were calculated by the automatic statistic of the dataset dynamically in the process of clustering to avoid the blindness of the threshold parameters by experience or experiment in most clustering algorithms. The experiments on several Chinese and English datasets showed that this algorithm performes better than clustering algorithms in CLUTO.