期刊：
JOURNAL OF INTERNET TECHNOLOGY,2011年12(3):503-515 ISSN：1607-9264
通讯作者：
Dai, Zhi-Cheng
作者机构：
[Dai, Zhi-Cheng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Li, Zhi] Huazhong Univ Sci & Technol, Patent Agcy Ctr, Dept Control Sci & Engn, Wuhan, Peoples R China.
通讯机构：
[Dai, Zhi-Cheng] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
关键词：
WSAN;Placement;CDS;Coverage
摘要：
Wireless sensor and actor network (WSAN) refers to a group of sensors and actors, which gather information in detection area and perform appropriate actions upon the event area, respectively. To take these actions collaboratively in the whole monitored area, maximal actor coverage along with inter-actor connectivity is desirable. In this paper, we propose an actor placement algorithm for WSAN with different transmission ranges. The algorithm utilizes the minimum connected dominating set (MCDS) construction of sensor network in order to determine the number of actors and their locations. The actors are then positioned to the location of the MCDS in order to maximize coverage and reduce data latency. The paper describes the centralized algorithm and the distributed version in detail respectively. The simulation experiments are conducted with different network density and transmission ranges, and the results show that the proposed algorithm can achieve the smaller connected dominating set (CDS) and the higher actors' coverage performance compare with existing algorithms.
摘要：
Chinese word segmentation is a difficult and challenging job because Chinese has no white space to mark word boundaries. Its result largely depends on the quality of the segmentation dictionary. Many domain phrases are cut into single words for they are not contained in the general dictionary. This paper demonstrates a Chinese domain phrase identification algorithm based on atomic word formation. First, atomic word formation algorithm is used to extract candidate strings from corpus after pretreatment. These extracted strings are stored as the candidate domain phrase set. Second, a lot of strategies such as repeated substring screening, part of speech (POS) combination filtering, and prefix and suffix filtering and so on are used to filter the candidate domain phrases. Third, a domain phrase refining method is used to determine whether a string is a domain phrase or not by calculating the domain relevance of this string. Finally, sort all the identified strings and then export them to users. With the help of morphological rules, this method uses the combination of statistical information and rules instead of corpus machine learning. Experiments proved that this method can obtain better results than traditional n-gram methods.
