In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and tra...In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.展开更多
目的探析穴位贴敷治疗原发性痛经的选穴及用药规律,为临床上穴位贴敷治疗原发性痛经的选穴和用药提供参考。方法检索中国知网、万方数据知识服务平台、维普中文期刊服务平台、Web of science、Pubmed数据库至2021年10月1日的穴位贴敷治...目的探析穴位贴敷治疗原发性痛经的选穴及用药规律,为临床上穴位贴敷治疗原发性痛经的选穴和用药提供参考。方法检索中国知网、万方数据知识服务平台、维普中文期刊服务平台、Web of science、Pubmed数据库至2021年10月1日的穴位贴敷治疗原发性痛经的临床研究文献,提取文献中的腧穴处方和药物处方,应用Excel 2016、IBM SPSS modeler 18.0、SPSS 24.0软件,统计腧穴和药物使用频次,进行腧穴、药物关联规则分析、聚类分析、核心处方分析。结果共纳入穴位贴敷处方145首,共涉及33个腧穴和111味药物。腧穴的总使用频次为457次,得到腧穴核心处方:关元、气海、神阙、三阴交、子宫;药物的总使用频次为957次,得到核心药物处方:延胡索、当归、肉桂、细辛、川芎;共涉及9条经脉,常用经脉为任脉、脾经、膀胱经;腧穴关联分析结果以关元—气海为主;药物关联分析结果以莪术—三棱、肉桂—丁香为主。对高频腧穴及药物进行聚类分析,高频腧穴及高频药物分别聚成5类。结论穴位贴敷治疗原发性痛经以选取腹部任脉腧穴为主,体现“腧穴所在,主治所在”之意;补血活血,温经散寒,理气调经止痛为其基本治法。展开更多
基金supported by the National Natural Science Foundation of China(No.61871400)the Natural Science Foundation of the Jiangsu Province of China(No.BK20171401)。
文摘In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.
