In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
目的研究蹲厕行为对胶囊内镜检查胃转运时间(gastric transit time,GTT)和全小肠检查率(complete examination rate of small bowel,CER)的影响。方法随机纳入2019年1-12月于重庆医科大学附属第二医院行胶囊内镜检查的患者122例,采用随...目的研究蹲厕行为对胶囊内镜检查胃转运时间(gastric transit time,GTT)和全小肠检查率(complete examination rate of small bowel,CER)的影响。方法随机纳入2019年1-12月于重庆医科大学附属第二医院行胶囊内镜检查的患者122例,采用随机数字表法将其分为试验组63例和对照组59例,试验组即吞服胶囊后随时可以蹲厕,对照组如需如厕采取坐便。比较2组患者GTT、小肠转运时间、CER和诊断率的区别。结果试验组和对照组患者在性别、年龄、住院情况等方面差异无统计学意义。试验组患者CER显著高于对照组(92.06%vs 79.66%),差异有统计学意义(P=0.048)。试验组患者中位GTT显著少于对照组(26.7 vs 45.6 min),差异有统计学意义(P=0.027)。两组患者在小肠转运时间和诊断率上差异无统计学意义。结论行胶囊内镜检查患者采用蹲厕行为能减少胶囊在胃内转运时间,增加全小肠检查率。展开更多
大部分系统使用深度学习技术完成语音识别任务,并取得良好的效果。但是,基于深度学习的语音识别技术对计算机硬件算力的要求较高,同时需要大量的语音样本对语音识别模型进行训练。针对这些问题,基于改进后的动态时间归整(Dynamic Time W...大部分系统使用深度学习技术完成语音识别任务,并取得良好的效果。但是,基于深度学习的语音识别技术对计算机硬件算力的要求较高,同时需要大量的语音样本对语音识别模型进行训练。针对这些问题,基于改进后的动态时间归整(Dynamic Time Warping,DTW)算法设计并实现了一个语音识别系统。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
文摘目的研究蹲厕行为对胶囊内镜检查胃转运时间(gastric transit time,GTT)和全小肠检查率(complete examination rate of small bowel,CER)的影响。方法随机纳入2019年1-12月于重庆医科大学附属第二医院行胶囊内镜检查的患者122例,采用随机数字表法将其分为试验组63例和对照组59例,试验组即吞服胶囊后随时可以蹲厕,对照组如需如厕采取坐便。比较2组患者GTT、小肠转运时间、CER和诊断率的区别。结果试验组和对照组患者在性别、年龄、住院情况等方面差异无统计学意义。试验组患者CER显著高于对照组(92.06%vs 79.66%),差异有统计学意义(P=0.048)。试验组患者中位GTT显著少于对照组(26.7 vs 45.6 min),差异有统计学意义(P=0.027)。两组患者在小肠转运时间和诊断率上差异无统计学意义。结论行胶囊内镜检查患者采用蹲厕行为能减少胶囊在胃内转运时间,增加全小肠检查率。