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Automatic generation of urban rail transit train diagram for express/slow trains operation and CAD realization
WANG Xianming, CHEN Rongwu, CAI Zheyang, WANG Fangchao
Journal of Computer Applications    2015, 35 (4): 1190-1195.   DOI: 10.11772/j.issn.1001-9081.2015.04.1190
Abstract1302)      PDF (988KB)(660)       Save

According to the idea of manually drawing a train diagram of the express and slow, the different proportions modularization thought of express and slow trains was proposed. Under the condition to satisfy the requirements of trains,the transitions and adjustments between different modules were added to meet the entrance/exit tracks of depot, so that the automatically generated diagram could meet the needs of designers. It can draw different full-day operation plans of different lines in particular proportions. Besides it can convert train diagram into CAD scripts and realize automatic drawing in CAD software. Finally, taking line 18 of Chengdu Metro as an example, the proposed method is implemented and its feasibility is proved.

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Novel text clustering approach based on R-Grams
WANG Xianming, GU Qiong, HU Zhiwen
Journal of Computer Applications    2015, 35 (11): 3130-3134.   DOI: 10.11772/j.issn.1001-9081.2015.11.3130
Abstract538)      PDF (775KB)(519)       Save
Focusing on the issue that the clustering accuracy rate and recall rate are difficult to balance in traditional text clustering algorithms, a clustering approach based on the R-Grams text similarity computing algorithm was proposed. Firstly, the clustered documents were sorted in descending order; secondly, the symbolic documents were identified and then initial clustering results were achieved by using an R-Grams-based similarity computing algorithm; finally, the final clustering results were completed by combining the initial clustering. The experimental results show that the proposed approach can flexibly regulate the clustering results by adjusting the clustering threshold parameter to satisfy different demands and the optimal parameter is about 15. With the increasing of the clustering threshold, the clustering accuracies increase, and the recalls increase at first, then decrease. In addition, the approach is free from time-consuming processing procedures such as word segmentation and feature extraction and can be easily implemented.
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